Support the show: https://www.buymeacoffee.com/datascienceharp
\n\nWatch the video of this episode: https://www.youtube.com/watch?v=-NZbXGoj2bQ
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\n\nWatch the video of this episode: https://youtu.be/eHIlY01n5LI
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\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
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\n\nWatch the video of this episode: https://www.youtube.com/watch?v=xWugtCTnWbw
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\n\nWatch the video of this episode: https://www.youtube.com/watch?v=D5QCcfi7acc
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\n\nWatch the video of this episode: https://youtu.be/6YN5p6T7vys
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\n\nWatch the video of this episode: https://www.youtube.com/watch?v=C_DBrPdh4Jw&ab_channel=HarpreetSahota%7CTheArtistsofDataScience
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\n\nWatch the video of this episode: https://www.youtube.com/watch?v=bFy64aMh_ho
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\nTwitter: https://twitter.com/datascienceharp
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\n\nWatch the video of this episode: https://youtu.be/NDVD28raDqw
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\n\nWatch the video of this episode: https://www.youtube.com/watch?v=G78EE7P-EV8
\n\nDon't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
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\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/datascienceharp
Support the show: https://www.buymeacoffee.com/datascienceharp
\n\nWatch the video of this episode: https://www.youtube.com/watch?v=KE_i6puujLo
\n\nDon't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
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\n\nWatch the video of this episode: https://youtu.be/zn0XHbXrhjs
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\n\nWatch the video of this episode: https://youtu.be/wVsszZLrl6g
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\n\nWatch the video of this episode: https://www.youtube.com/watch?v=Wt0w92QYIPg
\n\nDon't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
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\nTwitter: https://twitter.com/datascienceharp
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\n\nWatch the video of this episode: https://youtu.be/idD5TyW45y8
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\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
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\nYouTube: https://www.youtube.com/c/HarpreetSahotaTheArtistsofDataScience
\nInstagram: https://www.instagram.com/datascienceharp
\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/datascienceharp
Support the show: https://www.buymeacoffee.com/datascienceharp
\n\nWatch the video of this episode: https://www.youtube.com/watch?v=jwi9WH7588E
\n\nDon't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
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\nYouTube: https://www.youtube.com/c/HarpreetSahotaTheArtistsofDataScience
\nInstagram: https://www.instagram.com/datascienceharp
\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/datascienceharp
Support the show: https://www.buymeacoffee.com/datascienceharp
\n\nWatch the video of this episode: https://www.youtube.com/watch?v=9nSil9d9f7w&ab_channel=HarpreetSahota%7CTheArtistsofDataScience
\n\nDon't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
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\nYouTube: https://www.youtube.com/c/HarpreetSahotaTheArtistsofDataScience
\nInstagram: https://www.instagram.com/datascienceharp
\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/datascienceharp
Support the show: https://www.buymeacoffee.com/datascienceharp
\n\nWatch the video of this episode: https://www.youtube.com/watch?v=KUEpn6uiapM
\n\nDon't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\nListen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark
The Artists of Data Science Social links:
\nSupport the show: https://www.buymeacoffee.com/datascienceharp
\nYouTube: https://www.youtube.com/c/HarpreetSahotaTheArtistsofDataScience
\nInstagram: https://www.instagram.com/datascienceharp
\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/datascienceharp
Support the show: https://www.buymeacoffee.com/datascienceharp
\nFind Daliana onine: https://www.linkedin.com/today/author/dalianaliu
\nWatch the video of this episode: https://youtu.be/ldXGeOjGkx4
Memorable Quotes from the Episode:
\n\n[00:37:10] "Whatever we can do to actually, like, connect to our fellow man and woman is really, really important. And exercise and activity is a big way to do that. And the community serves our mental health. Whether you're an introvert or an extrovert, it doesn't really matter. Being around people serves you and allows you to feel like you're part of something like a belonging, right? So I think in all those ways, activity and exercise is huge for individuals who are just really trying to establish a baseline of mental health."
\n\nHighlights of the show:
\n\n[00:00:09] Guest Introduction
\n\n[00:02:05] Where you grew up and what it was like there?
\n\n[00:04:19] When you were in high school, what did you think your future would look like?
\n\n[00:07:41] How did you gravitate towards math having this interest that seemed a bit like honestly, like artsy kind of interest. Did you see any kind of art in mathematics? Is that what drew you to it? What was that pool?
\n\n[00:12:05] How is writing been an important element of success in your career?
\n\n[00:15:58 How do you suggest people get better at writing? Is it just going through, like a business writing class, one of those free business writing classes? How did you get developed that skill?
\n\n[00:20:49] What are some other critical elements to success for someone's career as a data scientist that don't get taught in school?
\n\n[00:24:34] How did you learn different skills?
\n\n[00:27:10] I'm wondering if a little bit in there is that feeling of imposter syndrome, a feeling of not wanting to ask a question because you don't want to be perceived as not knowing something like, oh, you're supposed to be a data scientist. Don't you know how to do this? Do you notice this happening a lot with with data scientist of any career level?
\n\n[00:30:53] Let's talk about how your day to day work as a data scientist is. How is this different from what you expected it to be when you were an aspiring data scientist?
\n\n[00:35:23] What what do most data scientists do wrong when it comes to their career development?
\n\n[00:38:35] Where would you draw the line between a data analyst and a data scientist? Can you point to one skill and be like, oh, right there, that's it. If only you knew this one thing, you'd be a data scientist. Does it work like that? What are your thoughts on that?
\n\n[00:44:09] What are your thoughts on why people are giving you so much pushback around that particular thing?
\n\n[00:52:24] How do you try to ensure that you're providing as fresh a perspective as possible with the content that you create?
\n\n[00:53:33] What are your thoughts on what it means to be a data science influencer?
\n\n[00:55:19] Let get into your podcast "the data scientist show". Talk to us about that. How did that idea come into your mind that you want to start a podcast? Who should listen to this podcast? Do you have to be experienced in the game to listen to it? Or is this a broad spectrum of data scientists.
\n\n[01:00:07-01:00:15] Let's talk about your experience being a woman in tech and a woman in data.If you have any advice or words of encouragement for the women in our audience who are breaking into or currently in our data world?
\n\n[01:06:05] What can we do to foster the inclusion of women in data science and AI?
\n\n[01:05:07] It is 100 years in the future. What do you want to be remembered for?
\n\nRandom Round
\n\n[01:07:12] In your opinion, what do most people think within the first few seconds of meeting you for the first time?
\n\n[01:07:41] You do like journaling in the morning or anything like that?
\n\n[01:07:57] What are you currently reading?
\n\n[01:08:42] Can you share just a couple of tips on how not to feel bad not finishing a book?
\n\n[01:10:21] Pirates are ninjas?
\n\n[01:10:31] Mountains or ocean?
\n\n[01:10:38] If you were a vegetable, what vegetable would you be?
\n\n[01:10:48] If you could live in a book, TV show or movie, what would it be?
\n\n--
\n\nDon't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\nListen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark
The Artists of Data Science Social links:
\nSupport the show: https://www.buymeacoffee.com/datascienceharp
\nYouTube: https://www.youtube.com/c/HarpreetSahotaTheArtistsofDataScience
\nInstagram: https://www.instagram.com/datascienceharp
\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/datascienceharp
Support the show: https://www.buymeacoffee.com/datascienceharp
\n\nWatch the video of this episode: https://www.youtube.com/watch?v=BltSMpwSBWw
\n\nDon't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\nListen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark
The Artists of Data Science Social links:
\nSupport the show: https://www.buymeacoffee.com/datascienceharp
\nYouTube: https://www.youtube.com/c/HarpreetSahotaTheArtistsofDataScience
\nInstagram: https://www.instagram.com/datascienceharp
\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/datascienceharp
Support the show: https://www.buymeacoffee.com/datascienceharp
\nFind Lara online: https://www.drlarapence.com/
\nWatch the video of this episode: https://www.youtube.com/watch?v=jKwGLkMvzis
Memorable Quotes from the Episode:
\n\n[00:38:09] "Whatever we can do to actually, like, connect to our fellow man and woman is really, really important. And exercise and activity is a big way to do that. And the community serves our mental health. Whether you're an introvert or an extrovert, it doesn't really matter. Being around people serves you and allows you to feel like you're part of something like a belonging, right? So I think in all those ways, activity and exercise is huge for individuals who are just really trying to establish a baseline of mental health."
\n\nHighlights of the show:
\n\n[00:01:31] Guest Introduction
\n\n[00:03:03] Where you grew up and what it was like there?
\n\n[00:04:56] Did you think you're going to be into when you're in high school?
\n\n[00:06:36] It's fascinating that you love to study humans because we are interesting, interesting creatures.We always like to compare ourselves to others. So what is it? What is it about humans that make us always go through this, this comparison thing?
\n\n[00:09:13] If somebody else is talking the same topics that I'm talking about and they've got a bigger audience, if anything, they're attracting more people to it. So it's just this little mindset shift. Can we work through my comparison issues on the air? Is that something you want to explore with a couple of questions?
\n\n[00:13:13] Speaking to my audience, a lot of them are are definitely future leaders, if not already current leaders. It may include senior level management type of level, things like that. As we move up the chain in responsibility it can get tempting for us to take on more and more responsibilities, right?
\n\n[00:13:31] At some point we need to start saying no, but how? How do we go about saying no? Why is it important that we are able to say no?
\n\n[00:17:17] "Busy calendar and a busy mind will destroy your ability to do great things in the world."
\n\n[00:19:02] Decision making is definitely an important aspect of data science, especially at the leadership level. You've got to make decisions, you've got to make them well because the consequences could cost in many different ways. I wonder if you can share some ways for us to improve our decision making process.
\n\n[00:22:58] Let's talk about self-awareness as it relates to coming up with our values. First, how do we describe self-awareness in this context? How can we use that to help us identify our values?
\n\n[00:26:37] Is there something that we can attest that we can give ourselves to determine just how self-aware we actually are?
\n\n[00:29:33] What is this concept of of a personal true north? Talk to us about this this concept and how do we define that for ourselves?
\n\n[00:31:27] What are some surefire ways that that we can use to make sure that we can avoid distraction and stay productive?
\n\n[00:35:55] There's an interesting connection between movement and mental health, if you just talk to us a little bit about that.
\n\n[00:39:49] How do we fight that urge and force ourselves to get that movement in because it's going to help us in the long term, right?
\n\n[00:44:01] Talking about your obsession with curiosity. What do you find so curious about curiosity?
\n\n[00:46:31] "I don't need anyone's permission to be curious either. It's free."
\n\n[00:46:44] What can I do to ensure that I don't do anything that would cause him (Harpreet's son) to lose that curiosity?
\n\n[00:49:21] How do we cultivate that sense of curiosity as adults?
\n\n[00:51:24] It is 100 years in the future. What do you want to be remembered for?
\n\nRandom Round
\n\n[00:52:11] What are you most active with in terms of podcasting?
\n\n[00:53:06] What is the the life box substance about this?
\n\n[00:54:46] What in your opinion, what do you think people think within the first few seconds of meeting you for the first time?
\n\n[00:55:11] What are you currently reading?
\n\n[00:55:31] What song do you have on repeat?
\n\n[00:55:54] What accomplishment are you most proud of?
\n\n[00:56:26] What sport are you playing?
\n\n[00:56:31] What makes you cry?
\n\n[00:57:14] What is your favorite city?
\n\n[00:57:50] What is something you can never seem to finish?
\n\n--
\n\nDon't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\nListen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark
The Artists of Data Science Social links:
\nSupport the show: https://www.buymeacoffee.com/datascienceharp
\nYouTube: https://www.youtube.com/c/HarpreetSahotaTheArtistsofDataScience
\nInstagram: https://www.instagram.com/datascienceharp
\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/datascienceharp
Support the show: https://www.buymeacoffee.com/datascienceharp
\n\nWatch the video of this episode: https://www.youtube.com/watch?v=NstXQM0M5JI&t=5s
\n\nDon't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\nListen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark
The Artists of Data Science Social links:
\nSupport the show: https://www.buymeacoffee.com/datascienceharp
\nYouTube: https://www.youtube.com/c/HarpreetSahotaTheArtistsofDataScience
\nInstagram: https://www.instagram.com/datascienceharp
\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/datascienceharp
Support the show: https://www.buymeacoffee.com/datascienceharp
\nFind Jonathan online: https://www.linkedin.com/in/jonathan-wonsulting/
\nFind Jerry Lee online: https://www.linkedin.com/in/jehakjerrylee/
\nWatch the video of this episode: https://www.youtube.com/watch?v=KdfFyY_-XT8&t=89s
Memorable Quotes from the Episode:
\n\n[00:26:43] " I think one of the best things about the content is that we sort of have seen on LinkedIn, Austin, Balzac as an example, he posts very, very actionable content and that's very much what we sort of like to strive for as well when we talk about job search, making it extremely tangible so that someone who is reading it can take action that very second. So for you, if just content that you really like and enjoy with and enjoyed and you're like, Listen, I'm going to try it. If it takes you less than 5 minutes, do it. Try it. Best case scenario is that you get a job out of it. The worst case scenario, you use 5 minutes."
\n\nHighlights of the show:
\n\n[00:00:41] Guests Introduction
\n\n[00:03:41] Jerry, talk to us about where you grew up and what it was like there?
\n\n[00:05:18] Jonathan Mann, tell us a little bit about yourself. Where did you grow up and what it was like there?
\n\n[00:06:49] How do you guys know each other? These guys grow up together. You guys go to high school together. You know, what's what's the back story there?
\n\n[00:07:55] Talk to us first about the genesis of the company. How did this idea start? How did this idea come about? What were you seeing in the world that was just like I just just couldn't take anymore. You had to do something about it. Like, what was that moment?
\n\n[00:10:49] So Jonathan talks about 'what is the definition of an underdog' . Who are the underdogs? And then maybe after that, Jerry, why is it that companies tend to overlook people just because of their "pedigree"?
\n\n[00:12:25] What is it about these companies overlooking people just because of their pedigree?
\n\n[00:14:50] What's like one of the first few things that you start to do with people? What are the first, I guess, myths you start to debunk or the mindset shift mindset shifts you help people go through or anything like that?
\n\n[00:16:11] Jonathan what is the first two steps to getting from that rejection to redirection path?
\n\n[00:17:20] When when you go to a LinkedIn profile, what's the immediate thing you go to? Let's start with that, Jerry, and then go to John.
\n\n[00:19:12] When it comes to the headlines, what is a common mistake you see people make repeatedly when it comes to their headlines?
\n\n[00:22:02] What are some do's and don'ts that you can share?
\n\n[00:23:32] What if we just don't feel like we're an expert enough to post content?
\n\n[00:25:01] There's the creating content, but then there's the consumption of content. How do you how do you ensure that you're consuming good stuff?
\n\n[00:26:12] There are a lot of good content out there as well, right? Once you have the good content filter down to get your feed full of stuff that you actually do want to see, then it becomes, Oh my God, there's so much good stuff and so many good tips, like, how the fuck do I apply this to my life? What am I supposed to do? Do you have like the tips or a framework on, on how you go about doing that.
\n\n[00:26:34] In terms of making use of all the wonderful tips that people are sharing because sometimes they just get so many tips, they might just get paralyzed like, oh my God, what do I do? What are your tips on that?
\n\n[00:31:31] Let's say you applied for a job. You're in the in the interview and you're showing up to an interview and you don't have much experience. Let's say it's an entry level job. So I just want to get your hot take on entry level jobs requiring experience. What are your thoughts around that? How can we break that need experience to get experience a cycle?
\n\n[00:34:27] Should we worry about looking a job hopper in 2022? What are your thoughts on that?
\n\n[00:36:52] Before we get to that phase again, job offers and all that stuff, we can't job help them see a job offers. How about those negotiations? That's the critical piece, I think, of the job process. Do you feel that people tend to be afraid to negotiate? And where do you think that fear stems from?
\n\n[00:38:44] How do you ask better questions during an interview to get to know more about the culture and environment?
\n\n[00:42:28] Is there a right or wrong way to answer to the "tell me about yourself" question. Jonathan, what do you think?
\n\n[00:43:25] How should we answer the "what's your biggest weakness" question? Should we actually just say weakness or what's your tips there?
\n\n[00:44:57] Talk about being an influencer, LinkedIn influencer, kind of the perils of being a LinkedIn influencers. What responsibility do you think it is? I don't know if I'm counted, I only got like 43,000 followers for whatever I'm influencer or not. But I feel like I have some responsibility towards people who consume my content. What are your views on that? What responsibility do we do we have towards towards those who are following us?
\n\n[00:46:59] Have you guys ever gone to any types of bouts of kind of creative burnout? What was that like? How did you overcome it? What were some early warning signs that you're starting to get burnt out?
\n\n[00:49:45] What's the right way to ask for a mentor? How do we identify who we want as a mentor?
\n\n[00:50:59] How do you go about finding this person might be a good candidate or that that vetting process or what have you?
\n\n[00:52:40] It is 100 years in the future. What do you want to be remembered for?
\n\nRandom Round
\n\n[00:54:08] What song do you have on repeat?
\n\n[00:54:41] What talent would you like to show off in a talent show?
\n\n[00:54:59] What fictional place would you most like to go?
\n\n[00:55:18] If you lost all of your possessions but one, what would you want it to be?
\n\n--
\n\nDon't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\nListen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark
The Artists of Data Science Social links:
\nSupport the show: https://www.buymeacoffee.com/datascienceharp
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\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/datascienceharp
Support the show: https://www.buymeacoffee.com/datascienceharp
\n\nWatch the video of this episode: https://www.youtube.com/watch?v=DAkvvP6-TuQ&t=14s
\n\nDon't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\nListen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark
The Artists of Data Science Social links:
\nSupport the show: https://www.buymeacoffee.com/datascienceharp
\nYouTube: https://www.youtube.com/c/HarpreetSahotaTheArtistsofDataScience
\nInstagram: https://www.instagram.com/datascienceharp
\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/datascienceharp
Support the show: https://www.buymeacoffee.com/datascienceharp
\nFind Jeremy online: https://www.linkedin.com/in/rjeremyadamson
\nWatch the video of this episode: https://www.youtube.com/watch?v=UglmEt_CRQE
Memorable Quotes from the episode:
\n\n[00:31:19] "Design thinking is a great ideation framework for understanding based on the business outcome, how we can tackle that. It's five simple steps. The first one is to empathize with the stakeholder, and that's a word that I think we need to be saying a lot more in this practice is empathy."
\n\nHighlights of the show:
\n\n[00:01:22] Guest Introduction
\n\n[00:03:04] Talk to us a little bit about how you got interested in data science and what was your path into the field like?
\n\n[00:05:08] How much more hype has data science, A.I. and all that become since you first broke into the field?
\n\n[00:06:26] What do you see happening in 2022 in data science and analytics? What's the big thing that you're excited or hopeful about?
\n\n[00:13:34] What are some guiding principles that we should keep in mind to ensure that we're successfully building and leading those?
\n\n[00:15:07] What's the etiquette behind the kicking of the doors?
\n\n[00:16:48] We will get into 'design thinking' part of the book, but I want to double down on the 'process' aspect of the book. What is 'process' anyways and what is it all about?
\n\n[00:18:16] What are some some ways that we can ensure that our processes remain parsimonious? And if you got any examples that you want to share with us.
\n\n[00:19:50] Talk to us about comprehensive group of processes that that are required for for project success.
\n\n[00:23:48] Walk us through prioritization projects.
\n\n[00:25:25] Identifying things that are important, we talk about this with respect to a project scoping and planning that there's some questions that we should ask ourselves and ask our stakeholders. Two crucial ones. Can you share those questions with us? And what is it that we hope to get from from asking those questions?
\n\n[00:27:47] When it comes to dealing with stakeholders or let's say we've identified that this is a problem that we should be working on, but how do we make it? How do we frame it from the business problem to an analytics problem? What are some questions we should use to tease out what we need to, to properly frame it?
\n\n[00:31:06] There's something that you talk about called 'design thinking'. What is design thinking? What's it all about? And what does this have to do with 'process'? What does this have to do with data science?
\n\n[00:32:42] It seems like designing requires a skills that are underdeveloped in a lot of data science and analytic professionals. How do we cultivate those skills and make that process enjoyable for everyone who's involved?
\n\n[00:34:46] When it comes to executing a project, does Agile have a place in the data science world?
\n\n[00:35:32] Do you have a structured approach for generating demand within an organization, especially for new teams where all business functions are our customers?
\n\n[00:37:00] What is a SKU morph and how can we use this to our advantage in data science?
\n\n[00:39:20] Are there, if you know of any studies about how agile methods can be applied to teams in data analytics or finance.
\n\n[00:42:53] How can we start viewing ourselves as craftspeople? What do you mean by a 'bi craftsperson'? How can we start being ourselves as that?
\n\n[00:45:34] It's been extremely hard to hire and keep great data scientists. Do you have any tips that have worked for you? You've touched on a few of those, but have you got any additional tips for that?
\n\n[00:47:20] Apart from the technical skills, what is it that you look for in data science candidates?
\n\n[00:48:39] How can an individual contributor embody the characteristics of a good leader without necessarily having that title?
\n\n[00:50:11] It's 100 years in the future. What do you want to be remembered for?
\n\nRandom Round:
\n\n[00:50:45] Let's just think about some interesting use cases for data science and machine learning in the aviation industries. What are a couple of ways that machine learning is being used there?
\n\n[00:52:37] If you were to write a fiction novel, what would it be about and what would you title it?
\n\n[00:53:00] What are you currently reading?
\n\n[00:53:14] What are you currently most excited about or currently exploring?
\n\n[00:53:51] What's something you learned in the last week?
\n\n[00:54:02] What have you created that you're most proud of?
\n\n[00:54:15] Have you ever saved someone's life?
\n\n[00:54:21] What's the best compliment you've ever received?
\n\nDon't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\nListen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark
The Artists of Data Science Social links:
\nSupport the show: https://www.buymeacoffee.com/datascienceharp
\nYouTube: https://www.youtube.com/c/HarpreetSahotaTheArtistsofDataScience
\nInstagram: https://www.instagram.com/datascienceharp
\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/datascienceharp
Support the show: https://www.buymeacoffee.com/datascienceharp
\n\nWatch the video of this episode: https://www.youtube.com/watch?v=qHjKd4van4o
\n\nDon't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\nListen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark
The Artists of Data Science Social links:
\nSupport the show: https://www.buymeacoffee.com/datascienceharp
\nYouTube: https://www.youtube.com/c/HarpreetSahotaTheArtistsofDataScience
\nInstagram: https://www.instagram.com/datascienceharp
\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/datascienceharp
Support the show: https://www.buymeacoffee.com/datascienceharp
\nFind Nick online: https://www.nicksingh.com/
\nWatch the video of this episode: https://youtu.be/7fzOYBkTHDM
Memorable Quotes from the show:
\n\n[00:12:47] "The part of math that I was interested in wasn't that crazy, crazy theoretical math. It was just like, Oh, how can we use data to drive better decisions? Like how can simple statistics and computing metrics and just keeping track of shit using numbers? How can that help build better products or build better systems? And that's what I learned in systems engineering. Combine that with some of my CS classes, which got me into a little bit more machine learning, and then it started clicking in my head of like, Oh, this data thing is really cool."
\n\nHightlights of the show:
\n\n[00:00:40] Guest Introduction
\n\n[00:03:26] Talk to us a little bit about where you grew up and what it was like there.
\n\n[00:07:57] What is it about us (of Indian heritage) and software and data science?
\n\n[00:09:11] Was there something you were always good at? Did you think you were ever going to be an author?
\n\n[00:11:03] Was data science something that you were exposed to when you're young?
\n\n[00:13:57] What is the business side of data? Please paint that picture for us.
\n\n[00:19:22] Is it better to have blank space on a resume than neutral information?
\n\n[00:23:34] LTalk to us about what this philosophy is for projects.
\n\n[00:31:57] How do we demonstrate business value with a project, especially if we don't have on the job experience and are doing a project to demonstrate our technical ability?
\n\n[00:39:20] You talk about cold emailing in your book. Is that just when someone messages somebody highly ranked on LinkedIn and leave it at that?
\n\n[00:40:50] Let's say somebody sees this awesome job on LinkedIn and then started looking for people in that company. Should they go and message an individual contributor, data scientist and have them look at their profile or send a message to the CEO? Like who on the spectrum do they reach out to?
\n\n[00:46:03] It is noticed that a lot of people that are new to the industry are new data scientists who are all up in their head thinking oh, man, like math and everything, thinking all about algorithms and their sleep. They think that these behavioral interview questions are just fluffy bullshit. Why do you think folks have this misconception?
\n\n[00:50:10] You talk about a framework in the book at a high level. Can you share a bit of that framework for how you would answer that question (where the star format doesn't apply)?
\n\n[00:52:34] Would you rather mention your knity gritty experiences from the past in an interview or do mention a little of a role that you played in math or astrophysics. Say that you're trying to get into a machine learning engineer role, can you share your response to that question with us here?
\n\n[00:55:12] Auditing the "tell me about yourself" question.
\n\n[01:04:50] What does product sense mean? What is it? Why are people afraid of it? Why does it seem like such a difficult skill?
\n\n[01:11:35] What's the number one product sense question that you see being asked?
\n\n[01:14:36] It is it's 100 years in the future. What do you want to be remembered for?
\n\nRandom Round
\n\n[01:16:18] What do most people think? Within the first few seconds of meeting you for the first time.
\n\n[01:16:47] You have this awesome blog post about books that you always bring up in conversations. One of them is written by probably my absolute favorite authors and one of my favorite books. That's Antifragile by Nassim Taleb. Talk to us about the three main takeaways you've gotten from that book.
\n\n[01:21:19] What are you currently reading?
\n\n[01:24:23] First question what makes you cry?
\n\n[01:24:41] If you were a vegetable, what vegetable would you be?
\n\n[01:24:50] What have you created that you're most proud of?
\n\n[01:25:33] What's the best piece of advice you have ever received?
\n\n[01:26:54] If you lost all of your possessions but one, what would you want it to be?
\n\n[01:27:29] Do you ever sing When You're Alone?
\n\n[01:27:52] What's your favorite candy?
\n\nDon't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\nListen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark
The Artists of Data Science Social links:
\nSupport the show: https://www.buymeacoffee.com/datascienceharp
\nYouTube: https://www.youtube.com/c/HarpreetSahotaTheArtistsofDataScience
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\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/datascienceharp
Support the show: https://www.buymeacoffee.com/datascienceharp
\n\nWatch the video of this episode: https://www.youtube.com/watch?v=eYfHD1CkvRI
\n\nDon't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\nListen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark
The Artists of Data Science Social links:
\nSupport the show: https://www.buymeacoffee.com/datascienceharp
\nYouTube: https://www.youtube.com/c/HarpreetSahotaTheArtistsofDataScience
\nInstagram: https://www.instagram.com/datascienceharp
\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/datascienceharp
Support the show: https://www.buymeacoffee.com/datascienceharp
\nFind David online: https://twitter.com/d_spiegel
\nRead David's article "Will I live longer than my cat?": https://www.bbc.co.uk/news/magazine-19467491
\nWatch the video of this episode: https://youtu.be/pCWH97vBFmU
Memorable Quotes from the show:
\n\n[00:23:36] "...essentially what probability theory allows us to do is to make assumptions about how the world works, how the data is generated, and turn it and flip it around after we observe some data into statements about our uncertainty about underlying features of the world. We can do that, which of course is very explicit based on work indeed, where after processing data or uncertainty and it turns into uncertainty about the underlying quantities."
\n\nHightlights of the show:
\n\n[00:01:29] Guest Introduction
\n\n[00:03:08] Talk to us about how you first got interested in statistics and what was it that drew you to this field?
\n\n[00:04:55] Why is it that it seems like mathematicians tend to dislike teaching statistics?
\n\n[00:08:27] What is statistical science and what is it all about?
\n\n[00:09:46] You talk about in your book, The Art of Statistics, how to handle problems and approach problems in statistics. You call the P, p, b, a C cycle. Tell us about that framework.
\n\n[00:15:03] You mentioned in the book that statistics is to blame for the reproducibility and replication crises in science. Why? Why is that?
\n\n[00:18:23] When we talk about induction and inductive inference, should the philosopher in us get worried at all about the problem of induction in statistics?
\n\n[00:19:40] Tell our audience about the 'normal distribution'.
\n\n[00:20:34] Do you have any examples of when inductive inference has failed in statistics that you could share with us?
\n\n[00:22:15] Why do we need probability theory when we're doing statistics?
\n\n[00:26:25] I think pouring into the Bayesian stuff is kind of taking a step back here, maybe first principles. But what is probability? How do we measure it? It seems like such a strange epistemological concept.
\n\n[00:28:27] Can we say there's a at least some type of difference between epistemic probability and some physical or I believe you say aleatory?
\n\n[00:30:03] Would there be a difference in the way that a philosopher or a statistician would interpret probability?
\n\n[00:38:32] What's the Bayesian approach all about and why is it that courts in the UK are banning it or have banned it?
\n\n[00:40:16] How is this (Bayesian approach) different from the frequentist approach to viewing probability? What's the central difference?
\n\n[00:44:55] It seems like the prior distribution is something that makes base them so controversial. Why is that?
\n\n[00:46:18] It seems like Bayes Theorem is the scientifically correct way to change your mind when you get new evidence, right?
\n\n[00:48:18] David Deutsch mentioned lately about the Bayesian-ism, and he's having some qualms with Bayesian ism. He says that Bayesian-ism becomes controversial when you try to use it as a way to generate new ideas or judge one explanation against another. How do we reconcile that when we're faced with some epistemic.
\n\n[00:49:51] About using it to help us in our everyday lives to make better decisions. How can we use Bayes in that context?
\n\n[00:53:15] It is 100 years in the future. What do you want to be remembered for?
\n\nRandom Round
\n\n[00:54:17] What do you believe that other people think is crazy?
\n\n[00:55:02] What are you most curious about right now?
\n\n[00:55:55] What are you currently reading?
\n\n[00:58:33] What do you like most about your family?
\n\n[00:58:53] What was your best birthday?
\n\nDon't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\nListen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark
The Artists of Data Science Social links:
\nSupport the show: https://www.buymeacoffee.com/datascienceharp
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\nTwitter: https://twitter.com/datascienceharp
Support the show: https://www.buymeacoffee.com/datascienceharp
\n\nWatch the video of this episode: https://www.youtube.com/watch?v=I6uLiz4lTrU&ab_channel=HarpreetSahota%7CTheArtistsofDataScience
\n\nDon't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\nListen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark
The Artists of Data Science Social links:
\nSupport the show: https://www.buymeacoffee.com/datascienceharp
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\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/datascienceharp
Support the show: https://www.buymeacoffee.com/datascienceharp
\n\nWatch the video of this episode: https://youtu.be/x26n7HmSYjw
\n\nDon't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
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The Artists of Data Science Social links:
\nSupport the show: https://www.buymeacoffee.com/datascienceharp
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\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/datascienceharp
Support the show: https://www.buymeacoffee.com/datascienceharp
\n\nWatch the video of this episode: https://www.youtube.com/watch?v=SYiQ1ncCGv8&ab_channel=HarpreetSahota%7CTheArtistsofDataScience
\n\nDon't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\nListen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark
The Artists of Data Science Social links:
\nSupport the show: https://www.buymeacoffee.com/datascienceharp
\nYouTube: https://www.youtube.com/c/HarpreetSahotaTheArtistsofDataScience
\nInstagram: https://www.instagram.com/datascienceharp
\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/datascienceharp
Support the show: https://www.buymeacoffee.com/datascienceharp
\nFind Marcus online: https://twitter.com/MarcusduSautoy
\nWatch the video of this episode: https://youtu.be/efIBVILq6WI
Memorable Quotes from the show:
\n\n[00:34:25] "...one has to learn the power of the short cut in statistics, which, you know, I tell the story about the we had this advert when I was a kid which which stated eight out of ten cats prefer a particular type of cat food. And, and we had a cat and I never remember anybody asking our cat what cat food it likes. So it was very striking that when I got to university, I learned about the power of sampling and the fact that, you know, to be able to there are 7 million cats here in the UK. How many cats would you have to ask to be confident enough to make that statement about?"
\n\nHighlights of the show:
\n\n[00:00:40] Guest Introduction
\n\n[00:03:08] Talk to us about where you grew up and what it was like there.
\n\n[00:08:15] Math is kind of just the language we use to describe it. What are your thoughts?
\n\n[00:10:49] From your viewpoint, do you think math is an art? Is it a science? Is it a combination of art and science. How do you how do you view this?
\n\n[00:13:52] What was it about Gauss when we talk about Mathametics?
\n\n[00:19:02] Is there any virtue in human laziness?
\n\n[00:21:52] Aristotle, idleness and noble leisure. Discuss.
\n\n[00:21:59] Speaking of creativity and putting you out of a job, can you discuss a little more about what you talk about in your book about it?
\n\n[00:27:18] Speaking of creativity, you took time in this pandemic to write a play. How is that coming along?
\n\n[00:29:44] Fringe Festival in Winnipeg and London Fringe in London.
\n\n[00:30:07] You shared a story in the book about how we can use math to fight off of vampires. If you could recount that story.
\n\n[00:33:47] What are some dangers of using statistical shortcuts that we should be on high alert for?
\n\n[00:39:34] "...data science can be dangerous if it's not combined with a deep understanding of where the data comes from."
\n\n[00:40:33] Why is it that our that our brains aren't very good at assessing probabilities?
\n\n00:44:14] Why is it that some people find that shortcut that Reverend Bayes discovered so controversial?
\n\n[00:47:02] You talked about the philosophical view of probability. Is it frequentist approach, the Bayesian approach? How do you view probability? What's your take on that?
\n\n[00:49:41] What is the Lovelace test and in what ways is it different from the Turing test?
\n\n[00:56:25] You talk about a few different types of creativity in your book, please eloborate.
\n\n[01:06:17] What is it about a mathematician's mindset that is deterministic and foolproof and of engineers?
\n\n[01:09:34] It is 100 years in the future. What do you want to be remembered for?
\n\nRandom Round
\n\n[01:10:46] What was your best birthday and how old were you at that birthday?
\n\n[01:11:42] What's the worst movie you've ever seen?
\n\n[01:12:06] What would you do on a free afternoon in the middle of the week?
\n\nDon't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\nListen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark
The Artists of Data Science Social links:
\nSupport the show: https://www.buymeacoffee.com/datascienceharp
\nYouTube: https://www.youtube.com/c/HarpreetSahotaTheArtistsofDataScience
\nInstagram: https://www.instagram.com/datascienceharp
\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/datascienceharp
Support the show: https://www.buymeacoffee.com/datascienceharp
\n\nWatch the video of this episode: https://www.youtube.com/watch?v=30uugRuW5_E&ab_channel=HarpreetSahota%7CTheArtistsofDataScience
\n\nDon't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\nListen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark
The Artists of Data Science Social links:
\nSupport the show: https://www.buymeacoffee.com/datascienceharp
\nYouTube: https://www.youtube.com/c/HarpreetSahotaTheArtistsofDataScience
\nInstagram: https://www.instagram.com/datascienceharp
\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/datascienceharp
Support the show: https://www.buymeacoffee.com/datascienceharp
\nFind Danny online: https://www.datawithdanny.com/
\nWatch the video of this episode: https://youtu.be/VgQ_Hhq4AlM
Memorable Quotes from the show:
\n\n[00:34:25] "I think in general, just we should really be out there to help others instead of trying to help ourselves in a way. Like I know of a few larger names who the social media presence is, their business, essentially. And I know that's really important. Like everyone has to make money, feed their families, buy all the things that they need in life and all that aspirational sort of things. But in a sense, like for me, like I don't know if this is this might be similar for you as well."
\n\nHightlights of the show:
\n\n[00:00:40] Guest Introduction
\n\n[00:02:32] Where you grew up and what it was like there.
\n\n[00:03:57] What's life in Sydney been like for you? Have you come to North America? Have you done a compare and contrast that what's different and what's the same?
\n\n[00:06:20] What kind of kid were you during high school and what did you think your future would look like?
\n\n[00:10:35] You and I somehow came from a similar type of background, having a kind of walk that actuarial path we're entering into this data science kind of field. Tell us what was your experience like with those exams.
\n\n[00:12:52] What was it about kind of doing that actuarial work that made you want to leave it behind and move to this data thing?
\n\n[00:18:13] How did you figure out that what it was that you needed to figure out in order to make it in this field?
\n\n[00:22:43] How do you try to ensure that you've got as fresh a perspective as possible? Do you even need a fresh perspective as possible? What are your thoughts on that?
\n\n[00:29:14] We're just talking about what it means to be a data science influencer. What are your thoughts on what it means to be a a data science influencer?
\n\n[00:32:57] Do we have an influencer quality - What responsibility do we have to these people that are following us?
\n\n[00:36:51] What do you consider the difference to be between coaching and mentorship?
\n\n[00:39:27] How can somebody go and go about finding a mentor?
\n\n[00:43:07] What elements can you take and apply to this new thing that you want to do in the essence of creativity as well as finding different things that on the surface of it don't look like they belong together. But when you put them together, it actually gels quite nicely.
\n\n[00:44:40] Do you have any tips on on how I can be a better mentor?
\n\n[00:53:09] Talk to us abouth the love of SQL. How did this happen? Is this something that you've always just enjoyed? Has SQL always been your favorite part of the entire data science ecosystem? How this deep, deep love of SQL happened?
\n\n[00:57:23] Can we draw the line between a data analyst and a data scientist?
\n\n[01:08:32] What's your take on the importance of taking action on an idea you have in your mind there?
\n\n[01:12:00] It is 100 years in the future, what do you want to be remembered for?
\n\n[01:13:01] At what point did your meme game get so dank?
\n\n[01:15:21] What are you currently reading?
\n\n[01:17:05] What song do you currently have on repeat or stuck in your head?
\n\nRandom Round
\n\n[01:18:00] Who inspires you to be better?
\n\n[01:18:09] What's the best piece of advice you've ever received?
\n\n[01:18:15] Who is one of your best friends?
\n\n[01:18:29] If you were a vegetable, what vegetable would you be?
\n\nDon't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\nListen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark
The Artists of Data Science Social links:
\nSupport the show: https://www.buymeacoffee.com/datascienceharp
\nYouTube: https://www.youtube.com/c/HarpreetSahotaTheArtistsofDataScience
\nInstagram: https://www.instagram.com/datascienceharp
\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/datascienceharp
Support the show: https://www.buymeacoffee.com/datascienceharp
\n\nWatch the video of this episode: https://youtu.be/LR81rcjuaFk
\n\nDon't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\nListen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark
The Artists of Data Science Social links:
\nSupport the show: https://www.buymeacoffee.com/datascienceharp
\nYouTube: https://www.youtube.com/c/HarpreetSahotaTheArtistsofDataScience
\nInstagram: https://www.instagram.com/datascienceharp
\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/datascienceharp
Support the show: https://www.buymeacoffee.com/datascienceharp
\nFind Chanin online: https://th.linkedin.com/in/chanin-nantasenamat
\nWatch the video of this episode: https://youtu.be/pCHubISFBTI
Memorable Quotes from the show:
\n\n[00:31:42] "So I would believe that scientific method would be the science part of data science, and the data could be biology, chemistry, physics, business data, economic ecology. So I would believe that it's pretty much like a plug and play like data could come from many discipline. And then the analytic part, the machine learning part would be to take that data and make it into an interpretable model."
\n\nHighlights of the show:
\n\n[00:00:36] Guest Introduction
\n\n[00:03:12] Where you grew up and what it was like there?
\n\n[00:04:22] What brought you back to Thailand?
\n\n[00:05:15] How different is your life now than what you thought it would be growing up?
\n\n[00:07:03] When it comes to making YouTube videos, what is your most favorite part about making the YouTube videos and what is the part that you just liked the least?
\n\n[00:08:02] What part of it is the toughest? Is it just that the editing and the blogging and stuff like that? Or is there some parts of it where you're just like, Oh, man, I hate doing this?
\n\n[00:09:47] What is bioinformatics and how did you get into that?
\n\n[00:11:22] Was there any additional upskilling that you had to do in machine learning or data science topics? And if there was any additional upskilling, what was your process to acquire that knowledge?
\n\n[00:17:19] "How do I figure out what projectsI want to do, how to figure out what I want to research?" hat advice do you typically give to such questions?
\n\n[00:19:00] What is drug discovery? Where does data science enter into the mix here?
\n\n[00:22:28] Do you have any interesting use cases or studies you can share with us that talk about the involvement of machine learning and drug discovery, like a friendly, easy to read paper or maybe one of your YouTube videos if you got something like that?
\n\n[00:26:26] Do you know of anything that's been released on the market that has used this (drug discovery) approach? Is it widely used? Is it commonly used? Or is this kind of something that's right now just a theoretical idea?
\n\n[00:27:09] YouTubing, but where did that spark to help other data scientists come from?
\n\n[00:31:40] where is the science in data science?
\n\n[00:34:30] The methodology, a traditional machine learning problem or deep learning one. The process methodology is a little bit different. You worked with both of those, how would you say it's compare and contrast that if you would for us?
\n\n[00:36:50] Talk to us about a few of your blog posts.
\n\n[00:43:43] It is 100 years in the future, what do you want to be remembered for?
\n\n[00:44:45] When it comes to the future of of data science and machine learning, what applications are you most excited about in the field of drug discovery or bioinformatics? What gets you hyped up when you think about it?
\n\n[00:46:38] What are you currently reading?
\n\n[00:48:06] What song do you currently have on repeat?
\n\n[00:48:38] What are your pet peeves?
\n\n[00:49:02] Do you have any nicknames?
\n\n[00:49:22] What talent would you show off in a talent show?
\n\n[00:49:44] When was the last time you changed your opinion about something major?
\n\n[00:51:29] What's your favorite city?
\n\nDon't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\nListen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark
The Artists of Data Science Social links:
\nSupport the show: https://www.buymeacoffee.com/datascienceharp
\nYouTube: https://www.youtube.com/c/HarpreetSahotaTheArtistsofDataScience
\nInstagram: https://www.instagram.com/datascienceharp
\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/datascienceharp
Support the show: https://www.buymeacoffee.com/datascienceharp
\n\nWatch the video of this episode: https://www.youtube.com/watch?v=32znIxJoFRo
\n\nDon't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\nListen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark
The Artists of Data Science Social links:
\nSupport the show: https://www.buymeacoffee.com/datascienceharp
\nYouTube: https://www.youtube.com/c/HarpreetSahotaTheArtistsofDataScience
\nInstagram: https://www.instagram.com/datascienceharp
\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/datascienceharp
Support the show: https://www.buymeacoffee.com/datascienceharp
\nFind Christina online: https://www.linkedin.com/in/christinastathopoulos
\nWatch the video of this episode: https://youtu.be/FCQZfuhV6vY
Highlights of the Show:
\n\n[00:01:32] Guest Introduction
\n\n[00:03:24] Where did you grow up and what was it like there?
\n\n[00:04:18] You spent a decade abroad in Spain. How did that happen?
\n\n[00:06:28] How did you transition into analytics?
\n\n[00:08:31] How did you learn another language as an adult? Was that challenging? How did you figure that out?
\n\n[00:13:48] You and Kate are quite busy. How do you balance all of these activities, all of these speaking engagements and teaching, plus having a full time job?
\n\n[00:18:17] How did you develop this reading habit and how are you getting all these books? Are you getting them delivered to you or do you have a book exchange thing? How's this working?
\n\n[00:20:35] Do you do audiobooks or just strictly so?
\n\n[00:21:50] How can someone who's new to this space (analytics) decide which direction is right for them? And how did you figure out what direction you wanted to go into?
\n\n[00:24:53] What are some soft skills that you think have helped you really excel in your career?
\n\n[00:29:16] Russell defined your multilingual skills with spoken and written language. Do you find that they help you when translating between different coding languages?
\n\n[00:31:08] How can we simplify complex tasks?
\n\n[00:32:59] When you put yourself into their shoes, is there some kind of universal thing that most CEOs tend to care about or universal points that you've noticed through all these interactions that you've had, if such a thing could exist?
\n\n[00:38:18] How did you overcome technological challenges? If you face that challenge at all. What I'm trying to ask is learning new things as they come up in your career. How do you handle that? How do you manage that?
\n\n[00:42:51] Is there anything that you feel like you're just a natural at?
\n\n[00:43:31] In terms of the new methodologies and new technology that is coming out, is it mostly the academic research stuff? Is the cutting edge deep learning stuff? What do you find more fascinating?
\n\n[00:46:20] "How do you practice being present during tough times and tie back to your purpose with the work you do?"
\n\n[00:49:39] If you had any words of advice or encouragement for the women who are breaking into or that are currently in our world of data science?
\n\n[00:52:43] What can we do to foster inclusion of women in data science?
\n\n[00:57:04] It is 100 years in the future. What do you want to be remembered for?
\n\nRandom Round
\n\n[00:57:54] You've done a lot of traveling 50 countries. What's the most beautiful place you've ever seen?
\n\n[01:02:39] What song do you have on repeat?
\n\n[01:05:19] What incredibly strong opinion do you have that is completely unimportant in the grand scheme of things?
\n\n[01:06:01] What was your best birthday?
\n\n[01:06:10] Do you have any nicknames?
\n\n[01:06:24] What's your worst habit?
\n\nDon't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\nListen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark
The Artists of Data Science Social links:
\nSupport the show: https://www.buymeacoffee.com/datascienceharp
\nYouTube: https://www.youtube.com/c/HarpreetSahotaTheArtistsofDataScience
\nInstagram: https://www.instagram.com/datascienceharp
\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/datascienceharp
Support the show: https://www.buymeacoffee.com/datascienceharp
\n\nWatch the video of this episode: https://youtu.be/6wNvlO8Jj7o
\n\nDon't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\nListen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark
The Artists of Data Science Social links:
\nSupport the show: https://www.buymeacoffee.com/datascienceharp
\nYouTube: https://www.youtube.com/c/HarpreetSahotaTheArtistsofDataScience
\nInstagram: https://www.instagram.com/datascienceharp
\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/datascienceharp
Support the show: https://www.buymeacoffee.com/datascienceharp
\nFind Natalie online: https://www.figure8thinking.com/about/
Watch the video of this episode: https://youtu.be/3Dr7OcyQj1M
\n\nHighlights of the show:
\n\n[00:01:32] Guest Introduction
\n\n[00:03:34] Where did you grow up and what was it like there?
\n\n[00:08:04] Talk to us about the idea of “indoctrination to education”.
\n\n[00:12:35] Your interesting ‘creativity research’, when did that start? How did your interest in that get sparked?
\n\n[00:18:24] How can we make creativity more accessible and not just something that feels like it's in the domain of artsy people?
\n\n[00:23:15] What is your definition of creativity?
\n\n[00:27:27] Discuss the aspect of wonder and rigour.
\n\n[00:29:09] What's wrong with the way that we're currently asking questions?
\n\n[00:38:17] Walk us through what design thinking is and how does that help us be more creative?
\n\n[00:40:58] What is divergent and convergent thinking?
\n\n[00:50:47] Talk to us about the remix, the reframe and repurpose. How they help play a role in being creative?
\n\n[00:53:04] Talk to us about 'Fashion thinking'.
\n\n[00:56:20] It is 100 years in the future. What could it be remembered for?
\n\nRandom Round
\n\n[00:57:13] What are you currently reading?
\n\n[00:57:37] What song do you currently have on repeat?
\n\n[00:58:22] What's the best thing you got from one of your parents?
\n\n[00:58:31] In your group of friends, what role do you play?
\n\n[00:58:41] What fictional place would you most like to go to?
\n\n[00:59:02] Pizza or tacos?
\n\nDon't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\nListen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark
The Artists of Data Science Social links:
\nSupport the show: https://www.buymeacoffee.com/datascienceharp
\nYouTube: https://www.youtube.com/c/HarpreetSahotaTheArtistsofDataScience
\nInstagram: https://www.instagram.com/datascienceharp
\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/datascienceharp
Support the show: https://www.buymeacoffee.com/datascienceharp
\n\nWatch the video of this episode: https://youtu.be/7l-pB7RkCJA
\n\nDon't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\nListen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark
The Artists of Data Science Social links:
\nSupport the show: https://www.buymeacoffee.com/datascienceharp
\nYouTube: https://www.youtube.com/c/HarpreetSahotaTheArtistsofDataScience
\nInstagram: https://www.instagram.com/datascienceharp
\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/datascienceharp
Support the show: https://www.buymeacoffee.com/datascienceharp
\nFind Andrew online: https://www.linkedin.com/in/andrew-jones-data-science-infinity
\nWatch the video of this episode: https://youtu.be/hiEEAIGP8Zo
Memorable Quotes from the Show:
\n\n[00:20:55] “You need somebody in there to be putting the nuance on that to make it actually work. I think for me as well, like Auto, where ML doesn't have any business sense necessarily, so it doesn't know what problems to solve or it doesn't know why it should solve them. So I think humans are still a huge part of that. I don't think that's going away anywhere soon. It's just an evolution and data scientists are going to start, you know, there's going to be bits where automation comes in and helps us do our jobs even better. But I don't think it's going to take away jobs necessarily. I don't have any particular fear about that.”
\n\nHighlights of the Show:
\n\n[00:01:18] Guest Introduction
\n\n[00:03:20] Where did you grow up and what was it like there?
\n\n[00:06:19] When you were in high school, what did you think your future would look like?
\n\n[00:07:12] At six foot seven. It's a shame that you did not get into basketball. Is that right?
\n\n[00:07:48] Did you start doing analytics in New Zealand or start in London? Walk us through that journey.
\n\n[00:10:39] What's the toughest part about transitioning from SAS into python?
\n\n[00:16:25] You've been in this field for over a decade. How far has it come since you first broke into it?
\n\n[00:19:13] Can you share a hot take with us on where you think the field of data science is headed?
\n\n[00:33:29] Talk about your mission to help develop data scientists.
\n\n[00:39:28] What makes an employable data scientist different from an unemployable one?
\n\n[00:42:07] Where do you think most data scientists go wrong in terms of their own career development?
\n\n[00:45:07] “How to choose the right model to train the data?”
\n\n[00:49:06 Is there a field within machine learning that focuses on incorporating human concerns through technology development?
\n\n[00:51:58] “What advice do you give social scientists that are learning data analytics? Any particular hints for psychologists trying to understand acceptable norms of behavior when creating data science projects?”
\n\n[00:54:04] Talks to us about the top five reasons that candidates get rejected.
\n\n[00:58:13] When it comes to career growth and development. What's the biggest lesson you learned the hard way that you want to make sure no one else makes?
\n\n[00:59:49] It's 100 years in the future. What do you want to be remembered?
\n\nRandom Round
\n\n[01:01:18] What do you think will be the first video to hit 1 trillion views on YouTube? And what will that video be about?
\n\n[01:03:29] What are you currently reading?
\n\n[01:05:09] What song do you currently have on repeat?
\n\n[01:06:30 If you had to change your name, what would you change it to?
\n\n[01:07:26] What's on your bucket list this year?
\n\n[01:08:40] What's the story behind one of your scars?
\n\n[01:09:51] What languages do you speak?
\n\nDon't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\nListen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark
The Artists of Data Science Social links:
\nSupport the show: https://www.buymeacoffee.com/datascienceharp
\nYouTube: https://www.youtube.com/c/HarpreetSahotaTheArtistsofDataScience
\nInstagram: https://www.instagram.com/datascienceharp
\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/datascienceharp
Support the show: https://www.buymeacoffee.com/datascienceharp
\n\nWatch the video of this episode: https://youtu.be/2kCDJbiTCk8
\n\nDon't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\nListen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark
The Artists of Data Science Social links:
\nSupport the show: https://www.buymeacoffee.com/datascienceharp
\nYouTube: https://www.youtube.com/c/HarpreetSahotaTheArtistsofDataScience
\nInstagram: https://www.instagram.com/datascienceharp
\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/datascienceharp
Support the show: https://www.buymeacoffee.com/datascienceharp
\nRead Brittany' book: Bigger Than Leadership https://www.amazon.com/Bigger-Than-Leadership-Influence-Inspiration-ebook/dp/B093PJGB6R
Watch the video of this episode: https://youtu.be/HP-_FJh8UGY
\n\nMemorable Quotes from the episode:
\n\n[00:33:19] “No matter who you are, no matter where you are, you do leave a visible,or like a physical, but also a more mental footprint everywhere you go. That is something that's really powerful.”
\n\nHighlights of the Show:
\n\n[00:01:18] Guest Introduction
\n\n[00:02:34] Where did you grow up and what was it like there?
\n\n[00:04:05] When you were in high school, what did you think your future would look like?
\n\n[00:07:44] Talk to us about your personal definition of what leadership is, what it means to you, and then why write about leadership?
\n\n[00:10:30] Who is your book for?
\n\n[00:12:36] Do you think a lot of people tend to feel that way, like they don't see themselves as leaders or they don't realize that they have this leadership ability. Would you agree with that? Why? Why not?
\n\n[00:14:35] What was the process like while writing your book? How did you manage your knowledge? How did you manage the notes? And then finally, how did all that come together in a book?
\n\n[00:16:51] Talk to us about the importance of stories and why they serve as such useful reminders for us.
\n\n[00:19:29] How do you balance all the activities - full time course load, writing a book in the middle of a pandemic? How did you manage all that?
\n\n[00:22:55] “Can you tell us more about the role of introspection in your writing work and the role of introspection in stories of leadership?”
\n\n[00:25:55] Do you have an introspection practice that you undertake? Is it just sitting and thinking, is it sitting in journaling? Is it going for a walk and thinking? How do you get your introspection on?
\n\n[00:28:58] How were you able to keep a narrow focus while exploring so much data in your writing?
\n\n[00:32:41] Talk to us about the “Three Eyes framework”. You touched a little bit on the intentionality aspect of it, but talk to us about how these three, I guess what these three eyes are and how they relate to leadership.
\n\n[00:43:02] Talk about the difference between leadership and mentorship.
\n\n[00:45:36] Can you share some tips with the audience for how we can go about finding a mentor for ourselves?
\n\n[00:49:17] What tips would you have for someone who finds themselves in a position similar to mine where all of a sudden people have started following them on LinkedIn and or other social media and have started to view them as mentors. What advice would you share?
\n\n[00:54:20] It is 100 years in the future. What do you want to be remembered for?
\n\nRandom Round
\n\n[00:56:30] When do you think the first video to hit 1 trillion views on YouTube will happen? And what do you think that video will be about?
\n\n[00:57:11] What do most people think within the first few seconds of meeting you for the first time?
\n\n[00:58:48] Talk to us about what the book title “Bigger Than Leadership” means to you.
\n\n[01:00:53] What are you currently reading?
\n\n[01:02:12] What is your procedure for taking notes?
\n\n[01:03:11] What song do you currently have on repeat?
\n\n[01:04:22-01:04:28] When people come to you for help, what do they usually want help with?
\n\n[01:05:45] What is your theme song?
\n\n[01:06:26] What issue will you always speak your mind about?
\n\n[01:07:25] Who inspires you to be better?
\n\nDon't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\nListen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark
The Artists of Data Science Social links:
\nSupport the show: https://www.buymeacoffee.com/datascienceharp
\nYouTube: https://www.youtube.com/c/HarpreetSahotaTheArtistsofDataScience
\nInstagram: https://www.instagram.com/datascienceharp
\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/datascienceharp
Support the show: https://www.buymeacoffee.com/datascienceharp
\n\nWatch the video of this episode: https://youtu.be/tzg4SkNo4g0
\n\nDon't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\nListen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark
The Artists of Data Science Social links:
\nSupport the show: https://www.buymeacoffee.com/datascienceharp
\nYouTube: https://www.youtube.com/c/HarpreetSahotaTheArtistsofDataScience
\nInstagram: https://www.instagram.com/datascienceharp
\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/datascienceharp
Support the show: https://www.buymeacoffee.com/datascienceharp
\nFind Joe online: https://www.linkedin.com/in/josephreis
Joe is a business-minded data nerd who’s worked in the data industry for 20 years.
\n\nIn his two decades as a practitioner he’s worked on the full gamut of data tasks from statistical modeling, forecasting, machine learning, data engineering, data architecture, and almost everything else in between.
\n\nHe’s taken all that experience and started his own venture and is currently the CEO of Ternary data.
\nWatch the video of this episode: https://youtu.be/6jGmXBaTJkI
Memorable Quotes from the episode:
\n\n[00:42:09] "...My other piece of advice, which is, do you lose money for the firm? I'll be understanding. If you lose a shred of reputation, I will be ruthless. Let's talk with me. Right. Reputation is everything. As he also says, it takes a lifetime to build a reputation. It takes 15 minutes to destroy it. So when we started our business, I thought it was interesting. We didn't really care about the money. We cared about reputation and cared about doing great work, meeting great people and just, I think developing good relationships. I always optimizing for reputation. I think we thought if we could build that pile of reputational capital, the money would follow. The reverse is rarely true, though. In the short term, you can build as much money as you can, but you can destroy your reputation. And then who's going to want to do business with you?"
\n\nHighlights of the show:
\n\n[00:01:11] Guest Introduction
\n\n[00:03:34] Joe, where did you grow up and what was it like there?
\n\n[00:05:22] What were you like as a high school kid? What did you think your future would look like?
\n\n[00:06:46] When you'd make the move over to Salt Lake City? Was that when you started working? Did you go to school there?
\n\n[00:09:08] What was it like kind of when you first started out and what drew you to this kind of field (data science)?
\n\n[00:14:02] Where is the science in data science? Is there any science in data science? Is it scientism?
\n\n[00:26:10] How did you guys link up and decide to start ternary data and can we even get the story behind the companies name as well?
\n\n[00:27:23] What are some problems that you just see as a consultant pop up over and over?
\n\n[00:34:06] Do engineers add value and how should we think about a return on investment for the work that they do?
\n\n[00:41:23] Talk to us about your blog post about the concept of reputational capital.
\n\n[00:43:04] Do you have any tips for people who are just early in their data science career. In their first job as a data scientist, how can they accrue some of this 'reputational capital'?
\n\n[00:45:56] How reading science fiction has made you a better technologist? What science fiction has done for you, has it made you a better technologist?
\n\n[00:47:44] What would you say is the one sci-fi work that's had the biggest influence on you as a technologist?
\n\n[00:51:04-00:51:17] You've got such a dope setup here. What's all this about? The keyboards? You got turntables, you got multiple keyboards. Are you making your music. Do you got any undercover Spotify?
\n\n[00:52:59] It's 100 years in the future. What do you want to be remembered for?
\n\nRandom Round
\n\n[00:54:02] When do you think the first video to hit to 1 trillion views on YouTube will happen? When will that happen and what will that video be about?
\n\n[00:55:33] What song do you have on repeat?
\n\n[00:55:53] What are you currently reading?
\n\n[00:59:19] What's kind of your process when you're reading?
\n\n[01:03:12] What talent would you show off in a talent show?
\n\n[01:03:39] What do you mean by organizational behaviour?
\n\nDon't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\nListen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark
The Artists of Data Science Social links:
\nSupport the show: https://www.buymeacoffee.com/datascienceharp
\nYouTube: https://www.youtube.com/c/HarpreetSahotaTheArtistsofDataScience
\nInstagram: https://www.instagram.com/datascienceharp
\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/datascienceharp
Support the show: https://www.buymeacoffee.com/datascienceharp
\n\nWatch the video of this episode: https://youtu.be/voMA7BvAHJ8
\n\nDon't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\nListen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark
The Artists of Data Science Social links:
\nSupport the show: https://www.buymeacoffee.com/datascienceharp
\nYouTube: https://www.youtube.com/c/HarpreetSahotaTheArtistsofDataScience
\nInstagram: https://www.instagram.com/datascienceharp
\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/datascienceharp
Support the show: https://www.buymeacoffee.com/datascienceharp
\n\nFind Brent online: https://www.linkedin.com/in/brentdykes
\nWatch the video of this episode: https://youtu.be/QcihRq9ieWE
Highlights of the show:
\n\n[00:01:21] Guest Introduction
\n\n[00:03:33] Talk to us a bit about where you grew up and what it was like there?
\n\n[00:04:58] You're still involved in the technology field and still involved with 'Data' in a sense. So is life really different than what you imagined it might be?
\n\n[00:08:36] ...the new indicating or talking or informing what is there,subtle differences is it glaring differences? Talk to us about that.
\n\n[00:14:05] I love philosophy, and Aristotle is definitely one of my favorites. So I'm wondering what can Aristotle teach us about persuasion and storytelling?
\n\n[00:15:18] "Telos"
\n\n[00:20:55] System one and System twos.
\n\n[00:21:00] If most of our decisions are very emotional, then how is it that we can make better decisions in spite of this emotional nature that we have?
\n\n[00:24:09 How do you define the term "motivated reasoning"?
\n\n[00:27:25] What are the differences in the ways that facts and stories activate our brains? Are some other differences in the ways that you know, facts and stories that activate our brain?
\n\n[00:31:48] What is a Data story like? Isn't it just the same as a dashboard with visuals or is it something else?
\n\n[00:37:19] What are the elements of an insight? How do we go from fact to insight?
\n\n[00:40:08] Is there a distinction between just the old fashioned insight and like an actionable insight? How do we distinguish the two?
\n\n[00:47:15] What is the "FOUR D" framework?
\n\n[00:52:32] We might have an audience member that's a key audience member, and they just want the facts. How do we how do we handle that situation?
\n\n[00:59:03] What's the difference between a Data story and a Data forgery?
\n\n[01:04:41] You talk about Cognitive Biases, Logical Fallacies in your book, what are these and why are they important to watch out for? Why should we keep an eye out for these things?
\n\n[01:10:45-01] It is this it's one hundred years in the future what do you want to be remembered for?
\n\n[01:12:23] What are you currently reading?
\n\n[01:12:43] What song do you currently have on repeat?
\n\n[01:13:26] If you lost all of your possessions, but one, what would you want it to be?
\n\n[01:13:48] What's your worst habit?
\n\n[01:13:55] What's your favourite, candy?
\n\n[01:14:12 What's one of your favorite comfort foods?
\n\n[01:14:24 What's something you learned in the last week?
\n\nDon't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\nListen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark
The Artists of Data Science Social links:
\nSupport the show: https://www.buymeacoffee.com/datascienceharp
\nYouTube: https://www.youtube.com/c/HarpreetSahotaTheArtistsofDataScience
\nInstagram: https://www.instagram.com/datascienceharp
\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/datascienceharp
Support the show: https://www.buymeacoffee.com/datascienceharp
\n\nWatch the video of this episode: https://youtu.be/lDh5crPq_Yc
\n\nDon't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\nListen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark
The Artists of Data Science Social links:
\nSupport the show: https://www.buymeacoffee.com/datascienceharp
\nYouTube: https://www.youtube.com/c/HarpreetSahotaTheArtistsofDataScience
\nInstagram: https://www.instagram.com/datascienceharp
\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/datascienceharp
Support the show: https://www.buymeacoffee.com/datascienceharp
\n\nFind Fabrice online: https://twitter.com/fabricemesidor
\nWatch the video of this episode: https://youtu.be/HfPZkuU65OY
Memorable Quotes from the episode:
\n[00:42:14] "I find that coding and math. I mean, the entire machine learning models today are really complex. So pick something to have fun with. You don't have to be stuck with quickly coding enough to start working on something that you don't like. So you need to take a project that you will have fun. You just enjoy. And the second thing is, don't be scared of the challenge."
Highlights of the show:
\n\n[00:01:17] Guest Introduction
\n\n[00:02:57] Where you grew up and what it was like there?
\n\n[00:05:05] You grew up in Haiti. In high school, what did you think your future would look like? Do you think you'd end up in the middle of winter in York?
\n\n[00:07:19] Do you like microeconomics or macroeconomics better? Which one do you do you prefer?
\n\n[00:08:55] How did you end up in Papua New Guinea? What was it like working in Papua New Guinea like?
\n\n[00:16:48] Share some tips with us on how to remain focused.
\n\n[00:18:42] Breaking into data science, you had to really upskill in Python. How did you apply those (excel skills) techniques when you're learning Python?
\n\n[00:20:48] Share some tips for public speaking and giving talks about Data science?
\n\n[00:30:00] Talk to us about your project idea. How did you get the idea for this project and what was what was your big takeaway from it?
\n\n[00:31:54] How'd you get the Data for the movie scripts?
\n\n[00:33:23] Applying machine learning to hip hop lyrics, so I thought that was really cool. So walk us through how you came up with the idea for this project.
\n\n[00:34:59] While applying machine learning to hip hop lyrics. what was your problem statement? What methodology did you use? Did you did you grab just the lyrics or did you grab the audio? Or did you combine audio and lyrics? How did you piece that project together? What was the big question that you're trying to answer?
\n\n[00:37:48] Did you have any type of criteria for which songs to include and which song not to include?
\n\n[00:42:11] What can the audience take away from this so they can go create some creative stuff for themselves?
\n\n[00:43:55] Do you have some favorite places that you go, some websites or anything like that?
\n\n[00:46:45] How do you guys use data science to to to help people like meet their goals?
\n\n[00:48:42] It is one hundred years in the future. What do you want to be remembered for that?
\n\nRandom Round
\n\n[00:49:35] If you were to write a fiction novel, what would it be about and what would you title it?
\n\n[00:50:21] When do you think the first video to hit one trillion views on YouTube will happen and what will it be about?
\n\n[00:51:49] What are you currently reading?
\n\n[00:52:29] What song do you have on repeat?
\n\n[00:53:47] What are you interested in that most people haven't heard of?
\n\n[00:54:42] How long were you able to hold your breath for?
\n\n[00:55:23] What would you do on a free afternoon in the middle of the week?
\n\n[00:55:32] What's the best thing you got from one of your parents?
\n\n[00:56:45] Pancakes or waffles?
\n\nDon't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\nListen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark
The Artists of Data Science Social links:
\nSupport the show: https://www.buymeacoffee.com/datascienceharp
\nYouTube: https://www.youtube.com/c/HarpreetSahotaTheArtistsofDataScience
\nInstagram: https://www.instagram.com/datascienceharp
\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/datascienceharp
Support the show: https://www.buymeacoffee.com/datascienceharp
\n\nWatch the video of this episode: https://youtu.be/h2JVRSR22Ec
\n\nResources:
\n\nhttps://conference.measureofmusic.com/
\n\nDon't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\nListen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark
The Artists of Data Science Social links:
\nSupport the show: https://www.buymeacoffee.com/datascienceharp
\nYouTube: https://www.youtube.com/c/HarpreetSahotaTheArtistsofDataScience
\nInstagram: https://www.instagram.com/datascienceharp
\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/datascienceharp
Support the show: https://www.buymeacoffee.com/datascienceharp
\n\nFind Justin online: https://www.linkedin.com/in/justingcgu
\nWatch the video of this episode: https://youtu.be/78sZh6iwoj0
Show Highlights:
\n\n[00:01:00] Guest Introduction
\n\n[00:01:44] Where you grew up and what was it like?
\n\n[00:04:41] It (was) assumed that there are only three possible career choices. Either you can be your doctor, an engineer, or it could be a failure. Was that the same kind of mentality that you had growing up with your parents?
\n\n[00:13:22] I'm guessing that person didn't grow up in the internet era to be able to come with these really interesting ideas that you have. What's your thoughts on that? How did you come up with some great ideas that you've discussed.
\n\n[00:17:14] Why is career services not the core piece of the college offering?
\n\n[00:33:00] Do you think there are some myths out there associated with the ATS applicant tracking system?
\n\n[00:43:05] Share some tips on how to make a good LinkedIn headline. Do you have any tips you can share with us for that?
\n\n[00:48:05] What cheat code can you share with us with respect to the 'About Me' section?
\n\n[00:52:48] it's one hundred years in the future. What do you want to be remembered for?
\n\n[00:54:36] What are you most inspired by right now?
\n\n[00:55:32] What do you believe that other people think is crazy?
\n\n[00:58:00] What song do you currently have on repeat?
\n\n[00:58:32] Which fictional place would you most like to go to?
\n\n[00:59:28] What is your theme song?
\n\n[01:00:14] Do you got any nicknames?
\n\n[01:01:03] Who is one of your best friends and what do you love about them?
\n\nDon't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\nListen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark
The Artists of Data Science Social links:
\nSupport the show: https://www.buymeacoffee.com/datascienceharp
\nYouTube: https://www.youtube.com/c/HarpreetSahotaTheArtistsofDataScience
\nInstagram: https://www.instagram.com/datascienceharp
\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/datascienceharp
Support the show: https://www.buymeacoffee.com/datascienceharp
\nHappy Hour 69 hosted by Antonio Ivanovski
Watch the video of this episode: https://youtu.be/c1Pd6hK4NoE
\n\nResources:
\n\nhttps://coolhunting.com/style/puma-satori-lux/
\nhttps://onthemarkdata.medium.com/making-sense-of-ethereum-data-for-analytics-17655c4859d0
\nhttps://www.instagram.com/lizandmollie/?hl=en
\nhttps://www.sound.xyz/soulection/untitled-001
Don't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\nListen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark
The Artists of Data Science Social links:
\nSupport the show: https://www.buymeacoffee.com/datascienceharp
\nYouTube: https://www.youtube.com/c/HarpreetSahotaTheArtistsofDataScience
\nInstagram: https://www.instagram.com/datascienceharp
\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/datascienceharp
Support the show: https://www.buymeacoffee.com/datascienceharp
\n\nWatch the video of this episode: https://youtu.be/yXCUm7au25U
\n\nMemorable Quotes from the episode:
\n\n[00:23:18] "Positivity paradox is that when you feel like you have to be positive, you feel worse because you're required to do what's called surface acting, and that's also I think it's very similar to emotional labor. This shows up a lot in customer service jobs. So if a customer is being really rude to you and you kind of put a smile on your face, pretend like they're being totally reasonable and take whatever vitriol they're spitting at you."
\n\nHighlights of the Show:
\n\n[00:00:46] Guest Introduction
\n\n[00:03:16] Talk to us about where you grew up and what it was like there?
\n\n[00:07:34] What did you think your future would look like when you grew up?
\n\n[00:12:37] Talk to us about distinction between emotional intelligence and being reasonably emotional. What's the difference between these two kind of ideas?
\n\n[00:17:40] How do you find space throughout the day to kind of just detach from some of these demands that you have of your time?
\n\n[00:23:10] Talk to us about the positivity paradox.
\n\n[00:29:07] Can you share some tips for newbies who are coming into an organization where maybe there's already these in-person relationships that have been developed and you're joining a team of colleagues kind of in this remote sense as a person on a screen like how can we develop meaningful work relationships if we're coming into a new environment in this virtual kind of world?
\n\n[00:32:11] Talk to us about the user manuals and how can they help with developing and building team cohesion?
\n\n[00:33:59] I really like that idea of the user manual but is this something that we can implement regardless of, you know, the depth or length of a work relationship?
\n\n[00:37:06] How can we start doing some implementing some of the stuff that we're learning books like yours?
\n\n[00:46:31] What are some other tips you might be able to share with that with our audience that find themselves in that situation where they've teammates now?
\n\n[00:51:07] How do we go about defining or cultivating a team culture?
\n\n[00:56:47] What about those people who just always seem to disagree and question everything that comes out of our mouth, right? How do we deal with with these people?
\n\n[01:00:37] How we can use our voices to support the women in Data science and just women in our organizations in general?
\n\n[01:04:40] Random round.
\n\n[01:04:41] It is one hundred years in the future. What do you want to be remembered for?
\n\n[01:07:16] When do you think the first video to hit one billion views on YouTube will happen?
\n\n[01:08:35] What are you currently reading?
\n\n[01:09:22] How do you effectively tell an accurate story about Data to an audience that might not be Data savvy?
\n\n[01:09:33] What song do you have on repeat?
\n\n[01:10:00] What languages do you speak?
\n\n[01:10:09] Who is one of your best friends and what do you love about them?
\n\n[01:11:42] What's the best thing you got from one of your parents Legos?
\n\n[01:12:43] What's your go to dance move?
\n\nDon't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\nListen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark
The Artists of Data Science Social links:
\nSupport the show: https://www.buymeacoffee.com/datascienceharp
\nYouTube: https://www.youtube.com/c/HarpreetSahotaTheArtistsofDataScience
\nInstagram: https://www.instagram.com/datascienceharp
\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/datascienceharp
Support the show: https://www.buymeacoffee.com/datascienceharp
\n\nWatch the video of this episode: https://youtu.be/Y1xrax0G86c
\n\n\n\nDon't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\nListen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark
The Artists of Data Science Social links:
\nSupport the show: https://www.buymeacoffee.com/datascienceharp
\nYouTube: https://www.youtube.com/c/HarpreetSahotaTheArtistsofDataScience
\nInstagram: https://www.instagram.com/datascienceharp
\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/datascienceharp
Support the show: https://www.buymeacoffee.com/datascienceharp
\nFind Alyssa online: https://www.linkedin.com/in/sophiaalyssasimpson
Watch the video of this episode: https://youtu.be/IqDE0kZOyag
\n\nMemorable Quotes from the Episode:
\n\n[00:24:42] "The best Data is is real data that is generated either by humans. Sometimes that's emails or whatever that use case is that you're solving. So I'll take a frequent use case, which is often like prioritization of support. Tickets is a classic model that teams want to build inside a lot of different types of organizations. You have zillions of support cases coming in for, and you want to just categorize them or you want to understand which ones are most severe that need to be answered first."
\n\nHighlights of the Show:
\n\n[00:01:27] Guest Intro
\n\n[00:03:05] You mentioned being an unlikely A.I. leader in your book, please talk to us about that.
\n\n[00:04:29] What could possibly go wrong if all we did was focus on creating accurate machine learning systems and just focus on that accuracy metric?
\n\n[00:08:08] Can you share some strategies with us for identifying the types of problems that A.I should solve?
\n\n[00:11:57] What is the Goldilocks problem? How do we define the Goldilocks problem?
\n\n[00:13:21] Can you share some tips with us to understand or tell, at least if a problem is going to be well suited to using machine learning?
\n\n[00:24:21] How do we make sure that it's the right data that we're using?
\n\n[00:28:34] If we have data that needs annotation, how do we check the quality of those annotations? How do we know where to go to get annotated? Do you have any tips around that?
\n\n[00:36:03] Talk to us about the importance of Data strategy.
\n\n[00:38:59] How do you deal with challenges like data governance in an organization if you face those?
\n\n[00:40:21] Being a woman in tech, if you might be able to just share some advice or words of encouragement for the women.
\n\n[00:45:03] Random Round.
\n\n[00:45:04] It is one hundred years in the future. What do you want to be remembered for?
\n\n[00:45:40] When do you think the first video to hit 1 trillion views on YouTube will happen and what will that video be about?
\n\n[00:46:38] In your opinion, what do most people think within the first few seconds of meeting you for the first time?
\n\n[00:46:56] What are you currently reading?
\n\n[00:47:34] What song do you have on repeat?
\n\n[00:48:06] What's your worst habit?
\n\n[00:48:30] What's your favorite candy?
\n\nDon't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\nListen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark
The Artists of Data Science Social links:
\nSupport the show: https://www.buymeacoffee.com/datascienceharp
\nYouTube: https://www.youtube.com/c/HarpreetSahotaTheArtistsofDataScience
\nInstagram: https://www.instagram.com/datascienceharp
\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/datascienceharp
Support the show: https://www.buymeacoffee.com/datascienceharp
\n\nWatch the video of this episode: https://youtu.be/DnPVmB3vAEM
\n\nResources:
\nhttps://docs.python.org/3/library/pdb.html
\nhttps://pythonexamples.org/python-breakpoint-example/
\nhttps://pythontutor.com/
\nhttps://www.bvp.com/atlas/roadmap-data-infrastructure
Don't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\nListen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark
The Artists of Data Science Social links:
\nSupport the show: https://www.buymeacoffee.com/datascienceharp
\nYouTube: https://www.youtube.com/c/HarpreetSahotaTheArtistsofDataScience
\nInstagram: https://www.instagram.com/datascienceharp
\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/datascienceharp
Support the show: https://www.buymeacoffee.com/datascienceharp
\n\nWatch the video of this episode: https://youtu.be/jsNZCiTGWVM
\n\nMemorable Quotes from the show:
\n\n[00:04:37] "...When you open the book, the first thing you see is not financial advice. I am not a CFA. I'm on a talent. I have no mechanism to understand your personal financial situation. My goal, which I say in the book, is to transfer my way of thinking to like a book so that you can understand how I think about this stuff in a sort of economics and finance so that you can make an intelligent decision of like what percent of my investment portfolio should they allocate to this crazy bitcoin nonsense."
\n\nHighlights of the show:
\n\n[00:00:49] Guest Introduction
\n\n[00:01:40] How you got interested in blockchain in the first place?
\n\n[00:05:17] Who did you write this book for?
\n\n[00:06:33] You also talk about some 'stablecoin; What the heck does that even mean?
\n\n[00:07:55] What causes the prices of coins go super high and skyrocketing?
\n\n[00:10:23] When people talk about blockchain and crypto, can we conflate those two? When I say crypto, does that just mean a coin or does crypto also refer to blockchain?
\n\n[00:13:13] Why do we have blockchain when we do have PayPal?
\n\n[00:15:06] Talk to us about "Finan".
\n\n[00:16:20] What is money and why should inflation affect how we think about money?
\n\n[00:23:39] Ethereum.
\n\n[00:36:25] What do 'liquidity' and 'correlation' mean and can you help us out with an example?
\n\n[00:42:52] What 'loss aversion' is all about? Can you describe this concept? Is that why losing money hurts us because it takes so much more effort to get it back?
\n\n[00:44:52] What's the average person want from finance and how can decentralized finance be useful for them?
\n\n[00:50:15] Talk about some traits of a good portfolio?
\n\n[00:55:06] Concept of how to pick a 'protocol'. What do you look at when you're picking a protocol?
\n\n[01:05:06] It's one hundred years in the future. What do you want to be remembered for?
\n\n[01:06:21] Talk about some of your interviewing experience(s).
\n\n[01:18:43] What are you currently reading right now?
\n\n[01:19:38] What is one of your favorite smells?
\n\n[01:20:15] What's something you wish you figured out sooner?
\n\nDon't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\nListen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark
The Artists of Data Science Social links:
\nSupport the show: https://www.buymeacoffee.com/datascienceharp
\nYouTube: https://www.youtube.com/c/HarpreetSahotaTheArtistsofDataScience
\nInstagram: https://www.instagram.com/datascienceharp
\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/datascienceharp
Support the show: https://www.buymeacoffee.com/datascienceharp
\n\nWatch the video of this episode: https://youtu.be/Xeanz9yORZI
\n\nResources:
\nhttps://en.wikipedia.org/wiki/5D_optical_data_storage
\nhttps://en.wikipedia.org/wiki/The_Feed_(British_TV_series)
\nhttps://en.wikipedia.org/wiki/Upload_(TV_series)
\nhttps://qr.ae/pGB0pB
\nhttps://studios.disneyresearch.com/category/robotics/
\nhttps://twitter.com/mxcl/status/608682016205344768?s=21
\nhttps://vinvashishta.substack.com/p/machine-learning-is-the-key-to-metaverse
\nhttps://www.starwars.com/news/the-mandalorian-stagecraft-feature
\nhttps://www.wired.com/story/notpetya-cyberattack-ukraine-russia-code-crashed-the-world/
\nhttps://www.youtube.com/c/SQLBI
Don't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\nListen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark
The Artists of Data Science Social links:
\nSupport the show: https://www.buymeacoffee.com/datascienceharp
\nYouTube: https://www.youtube.com/c/HarpreetSahotaTheArtistsofDataScience
\nInstagram: https://www.instagram.com/datascienceharp
\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/datascienceharp
Watch the video of this episode: https://youtu.be/wvkxi-Et29M
\nFind Loris Marini online: https://www.linkedin.com/in/lorismarini/
\nhttps://www.discoveringdata.com
Support the show: https://www.buymeacoffee.com/datascienceharp
\n\nMemorable Quotes from the show:
\n\n[00:33:01] "We all matter, we are all part of this and we need one another. I can't do that a science well, if the data is not reliable, if it's not trusted, if it's not connected to the business via metadata by a data management program that touches anyone. And so it's really a mind, a change of of mindset from. You can add value as a team in isolation to, well, not really. Data is the common denominator to everything we do, whether we like it or not. Everything we do generates Data."
\n\nHighlights of the Show:
\n\n[00:01:27] Guest Introduction.
\n\n[00:03:01] Where did you grow up and what was it like there?
\n\n[00:05:13] What the heck is quantum photonics and how did you get into that?
\n\n[00:08:51] How did you go from awesome, crazy physics stuff into Data science?
\n\n[00:12:41] Talk to us about your experience of hardcore physics and research and how did that experience lead you into the Data project?
\n\n[00:14:58] What did that look like when you were venturing out as the first data scientist?
\n\n[00:17:57] Data architecture. Talk to us about that transition. What was that transition like? What made you be like, "Oh my God, I need to put the Data science down and pick up the Data architect stuff"
\n\n[00:21:11] What is the difference between Data engineer and the data architect?
\n\n[00:24:47] What do you think a data scientist at a minimum should know about Data architecture and the role that Data architect plays?
\n\n[00:35:27] You're talking about the difference between data, information, knowledge and strategy. What's the difference between these? How does data, information or knowledge play into a strategy?
\n\n[00:40:44-00:40:46] What's the name of that podcast by Brian O'Neill?
\n\n[00:53:03] I love creating machine learning models and then you're trying to do stuff and then you realize that your hands are tied because there's no infrastructure in place; there's no desire or nobody cares about your fancy algorithms and anything like that. How can we start making a culture happen for success?
\n\n[00:58:04] Talk about this latter of Data needs that goes from data integration, data access and data transformation kind of walk us through that process and then talk to us about why transformation that part is so hard.
\n\n[01:02:03] It's one hundred years in the future. What do you want to be remembered for?
\n\n[01:04:00] What do you think is the most mysterious aspect of the universe, which you say that this uncertainty principle is that? Or is there a different thing that is more mysterious than that to you?
\n\n[01:05:02] When do you think the first video to hit one trillion views on YouTube will happen and what will that video be about?
\n\n[01:05:33] Who do people tell you that you look like?
\n\n[01:06:10] What are you currently reading?
\n\n[01:07:17] What song do you currently have on repeat?
\n\n[01:08:02] What's the last book you gave up on and stopped reading?
\n\n[01:08:45] What fictional place would you most like to go to?
\n\n[01:09:21] What languages do you speak?
\n\n[01:09:25] If you were a vegetable, what vegetable would you be?
\n\nDon't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\nListen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark
The Artists of Data Science Social links:
\nYouTube: https://www.youtube.com/c/HarpreetSahotaTheArtistsofDataScience
\nInstagram: https://www.instagram.com/datascienceharp
\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/datascienceharp
Watch the video of this episode: https://youtu.be/NPIjuY_0HYU
\nSupport the show: https://www.buymeacoffee.com/datascienceharp
\nResources:
http://ecai2020.eu/papers/348_paper.pdf
\nhttps://arxiv.org/pdf/1912.10564.pdf
\nhttps://cds.nyu.edu/wp-content/uploads/2019/06/RDS_TentativeSyllabus.pdf
\nhttps://dagshub.com/
\nhttps://discord.gg/ngNdE5Tvzy
\nhttps://diversity.google/annual-report/
\nhttps://hal.inria.fr/hal-01522418/document
\nhttps://insights.stackoverflow.com/survey/2021#salary-comp-total
\nhttps://www.kaggle.com/discussion
\nhttps://www.linkedin.com/feed/update/urn:li:activity:6889576309601640448/
\nhttps://www.linkedin.com/in/reid-blackman-ph-d-0338a794/
\nhttps://www.microsoft.com/en-us/ai/responsible-ai-resources
Don't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\nListen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark
The Artists of Data Science Social links:
\nYouTube: https://www.youtube.com/c/HarpreetSahotaTheArtistsofDataScience
\nInstagram: https://www.instagram.com/datascienceharp
\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/datascienceharp
Watch the video of this episode: https://youtu.be/JN7Anqiv2fU
\nFind Dr. Joe online: https://www.linkedin.com/in/jwperez
\nSupport the show: https://www.buymeacoffee.com/datascienceharp
Memorable Quotes from the Episode:
\n\nHighlights of the Show:
\n\nDon't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\nListen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark
The Artists of Data Science Social links:
\nYouTube: https://www.youtube.com/c/HarpreetSahotaTheArtistsofDataScience
\nInstagram: https://www.instagram.com/datascienceharp
\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/datascienceharp
Watch the video of this episode: https://youtu.be/nLf0_0I6uvU
\n\nResources:
\n\nhttps://abseil.io/resources/swe_at_google.2.pdf
\nhttps://register.gotowebinar.com/register/6783119648565141771
\nhttps://services.google.com/fh/files/misc/practitioners_guide_to_mlops_whitepaper.pdf
\nhttps://theartistsofdatascience.fireside.fm/andy-hunt
\nhttps://www.benjerry.co.uk/flavours/flavour-graveyard/rainforest-crunch
Don't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\nListen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark
The Artists of Data Science Social links:
\nYouTube: https://www.youtube.com/c/HarpreetSahotaTheArtistsofDataScience
\nInstagram: https://www.instagram.com/datascienceharp
\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/datascienceharp
Scott Taylor: https://www.linkedin.com/in/scottmztaylor/
\nhttps://www.metametaconsulting.com
Watch the video of this episode: https://youtu.be/LZHB6wcAUdg
\n\nMemorable Quotes from the Episode:
\n\n[00:26:52] "... if the Data science community worked more closely with the Data management community, I think we can, you know, let's stamp out wrangling or at least munching. Let's stamp out munching at least in our lifetime, since so many of those issues that people spend time on could be solved in the Data management side of the house. They may even have that data. I don't know how many times I learned at DB even at DB, where people were just like kind of starting over looking at something, it's like, you know, there's an existing list somewhere."
\n\nHighlights of the Show:
\n\n[00:01:29] Guest Intro
\n\n[00:03:42] Where'd you grow up? What was it like there?
\n\n[00:05:07] How did you get education in the United Nations School?
\n\n[00:07:06] How how did you get into Data?
\n\n[00:07:54] What was the first job you had?
\n\n[00:09:52] How did you end up learning about "Data"?
\n\n[00:12:56] What are the four Cs you talk about in your book?
\n\n[00:13:37] How have databases transformed from the time you started working on it?
\n\n[00:29:31] What would be the first thing you do to help your organization start on a path to creating a Data strategy?
\n\n[00:46:02] Random Round
\n\n[00:46:07] It's one hundred years in the future. What do you want to be remembered for?
\n\n[00:47:22] When do you think the first video to hit one trillion views on YouTube. Will happen and what will it be about?
\n\n[00:48:41] So what are you currently reading?
\n\n[00:48:57 What's something that you watch recently?
\n\n[00:50:36] What about music? What do you got on repeat?
\n\n[00:51:15] What makes you cry?
\n\n[00:56:10] What talent would you show off in a talent show?
\n\n[00:58:28] What are you interested in that most people haven't heard of?
\n\n[00:58:43] What's your earliest memory?
\n\nDon't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\nListen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark
The Artists of Data Science Social links:
\nYouTube: https://www.youtube.com/c/HarpreetSahotaTheArtistsofDataScience
\nInstagram: https://www.instagram.com/datascienceharp
\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/datascienceharp
Watch the video of this episode: https://youtu.be/2rKppfByI5c
\n\nResources:
\n\nhttps://datascienceharp.medium.com/i-thought-failure-was-my-destiny-until-i-realized-it-made-me-who-i-am-today-1a8bd4ccb1e2
\nhttps://dev.to/arslan_ah/grokking-leetcode-a-smarter-way-to-prepare-for-coding-interviews-5d9d
\nhttps://fs.blog/mental-models/
\nhttps://github.com/jwasham/coding-interview-university
\nhttps://juniortosenior.io/
\nhttps://vinvashishta.substack.com/p/assessing-a-data-scientists-coding
\nhttps://www.jefflichronicles.com/mental-models
\nhttps://youtu.be/4Qta0MyEoYU
Don't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\nListen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark
The Artists of Data Science Social links:
\nYouTube: https://www.youtube.com/c/HarpreetSahotaTheArtistsofDataScience
\nInstagram: https://www.instagram.com/datascienceharp
\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/datascienceharp
Watch the video of this episode: https://youtu.be/SButPV_V2-A
\n\nMemorable Quotes from the Episode:
\n\n[00:20:38] Most of the identifiers that associate a transaction with a person are just a series of letters and numbers, so it's not easy to trace back that Jonathan has sent Harpreet Sahota 1000 or 1000 bitcoin or something, but you can see all the transactions from the very, very beginning and you can export it. You could, you know, any number of data analytics products that you could run against it, just like any data store. One hundred percent, you can affect the data and get right to it, but you can obviously read and if you export it, you can do anything with it.
\n\nHighlights of the Show:
\n\n[00:00:40] Guest Introduction
\n\n[00:02:58] Talk to us a bit about where you grew up and what it was like there.
\n\n[00:06:14] How did this love of technology kick-off? How did you get interested in it?
\n\n[00:09:15] What is a blockchain and how is this different from what we're used to seeing in Data structures?
\n\n[00:15:05] Every time I hear about blockchain in the same sentence, almost they talk about cryptocurrency. So the exact same thing, or can we use them in place of each other? How does this work?
\n\n[00:21:35] What are the implications of blockchain technology for data governance data management?
\n\n[00:27:02] What is the difference between public permissioned and private blockchains.
\n\n[00:45:42] Randon Round.
\n\n[00:45:52] What's your favorite piece of clothing that you own?
\n\n[00:46:13] Who are some of your heroes?
\n\nDon't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\nListen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark
The Artists of Data Science Social links:
\nYouTube: https://www.youtube.com/c/HarpreetSahotaTheArtistsofDataScience
\nInstagram: https://www.instagram.com/datascienceharp
\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/datascienceharp
Watch the video of this episode: https://youtu.be/035XW6k-Kls
\n\n00:00:31 Harpreet's Year End Message
\n\nRegular Episodes Recap
\n\nThe Most Internationally listened to episode: https://youtu.be/-5EBk43uWD4
\n\n00:04:30 The Smartest Person in the Room | Christian Espinosa
\n00:05:36 Clearer, Closer, Better | Emily Balcetis
\n00:06:30 Meditations on Power and Mastery | Robert Greene
\n00:08:09 Pulling the Grim Trigger | Kevin Zollman
\n00:08:27 Your Job Doesn't Define YOU | Eleanor Tweddell
\n00:08:45 Explainable Data Science | Denis Rothman
\n00:09:03 Choose Who You Become | Chase Caprio
\n00:09:24 Your Beliefs Aren't Reality | Dave Gray
\n00:09:58 How to build a Data Science Culture | John K Thompson
\n00:10:51 Data Science Thunder From Down Under | Steve Nouri
\n00:11:55 The Philosophy of Sentientism | Jamie Woodhouse
\n00:12:42 The Shape of Geometry | Jordan Ellenberg
\n00:13:09 Our Nearest Neighbour | Ken Jee
\n00:13:40 Learning How To Learn | Barbara Oakley
\n00:14:04 Skip the Line |James Altucher
\n00:14:20 How to think like a data science billionaire | John Sviokla
\n00:15:02 Do What You Love Doing | Lillian Pierson
\n00:15:46 The Tesstimony | Jonathan Tesser
\n00:16:33 The Fearless Factor | Jacqueline Wales
\n00:16:56 Simplify Complexity | David Benjamin
\n00:17:22 Cultivate Your Rest Ethic | Max Frenzel
\n00:17:51 The Complete Man | Purdeep Sangha
\n00:18:26 Tales of a Data Engineer | Dennis Will
\n00:18:47 Subliminal Motives | Eric Okon
\n00:19:12 Become a Pragmatic Data Scientist | Andy Hunt
\n00:20:10 Turn the Lights on Data | George Firican
\n00:20:47 Give Your Brain Some Space | Tiffany Shlain
\n00:21:52 Wellness for Data Professionals | Madison Schott
\n00:22:24 The Industrial Philosopher | Cristina Digiacomo
\n00:23:05 Turn Ideas into Gold | Steven Cardinale
\n00:23:37 NLP and Philosophy | Kourosh Alizedah
\n00:24:17 The Book of Why | Dana Mackenzie
\n00:24:49 The International Woman of Data - Christina Stathopoulos
The Happy Hours Recap
\n\n00:25:40 Question/Answer and Mentoring
\n00:26:26 What title should be written on the resume?
\n00:30:44 Sharing the hot seat with friends of the show!
\n00:31:38 What is the best way to break into research?
\n00:32:45 How to organize while working?
\n00:33:19 When will Linkedin content creators start making money?
\n00:33:47 What's up with the billion hours of YouTube video KPI?
Having Fun
\n\n00:36:07 Jeff Li's freestyle rap session!
\n00:36:56 Insights with Eric
\n00:37:45 What is the perspective of Data on social media?
00:38:58 Credits
\n\nDon't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\nListen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark
The Artists of Data Science Social links:
\nYouTube: https://www.youtube.com/c/HarpreetSahotaTheArtistsofDataScience
\nInstagram: https://www.instagram.com/datascienceharp
\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/datascienceharp
Watch the video of this episode: https://youtu.be/82m6j8ZJgPI
\n\nResources:
\n\nhttps://www.moreintelligent.ai/10kcasts/
\n\nDon't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\nListen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark
The Artists of Data Science Social links:
\nYouTube: https://www.youtube.com/c/HarpreetSahotaTheArtistsofDataScience
\nInstagram: https://www.instagram.com/datascienceharp
\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/datascienceharp
Watch the video of this episode: https://youtu.be/7YhEjJFkRjI
\n\nResources:
\n\nhttps://apps.ankiweb.net
\nhttps://github.com/jeffmli/data-science-deliberate-practice
\nhttps://www.amazon.com/Interpretable-Machine-Learning-Python-hands-ebook/dp/B08PDFXXRL/ref=zg_bs_16977174011_7?_encoding=UTF8&psc=1&refRID=3TCSJQCA9WPJ5601MEG1
Don't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\nListen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark
The Artists of Data Science Social links:
\nYouTube: https://www.youtube.com/c/HarpreetSahotaTheArtistsofDataScience
\nInstagram: https://www.instagram.com/datascienceharp
\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/datascienceharp
Watch the video of this episode: https://youtu.be/SWSLiGmnpao
\n\nFind Dana Mackenzie online:
\nhttps://danamackenzie.com
\nhttps://scholar.google.com/citations?user=sQhKQ5cAAAAJ&hl=en
Memorable Quotes from the Episode:
\n\n[00:20:28] "At one point he realized something very fundamental and remarkable, which is if you switch the fathers and sons and you plot the sons side as the independent variable and the other side is independent variable, you get the same thing, you get the same fuzzy thing and you get the same correlation. And so correlation is something that is completely independent of causation."
\n\nHighlights of the Show:
\n\n[00:01:22] Guest Introduction.
\n\n[00:03:02] Where you grew up and what it was like there?
\n\n[00:04:23] As a kid, you loved writing, but then you ended up studying math at like the highest levels. Was that something that you foresaw happening? Were you always into math? Was it like a choice between math and writing? How did this play out?
\n\n[00:10:13] if anybody who wants to develop and flex writing muscle, do you have any tips for them on how they can develop and cultivate this skill?
\n\n[00:14:18] In view of your book "The book of Why", what is this computational cognitive faculty that humans certainly acquired that our chimpanzee cousins did not?
\n\n[00:17:28] Concept of counterfactuals.
\n\n[00:24:48] "Every statistics book says correlation is not causation. And they forget to tell you what is causation."
\n\n[00:41:55] What is the ladder of causation?
\n\n[00:48:57] "Smoking causes cancer", discuss.
\n\n[01:01:11] What is the do operator all about? What makes it so revolutionary and special?
\n\n[01:16:00] It is one hundred years in the future. What do you want to be remembered for?
\n\n[01:17:58] What are you currently reading?
\n\n[01:21:13] What song do you have on repeat?
\n\n[01:25:29] What is one of your favorite comfort food comfort foods?
\n\n[01:25:53] What have you created that you are most proud of?
\n\n[01:26:03] Who inspires you to be better?
\n\nDon't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\nListen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark
The Artists of Data Science Social links:
\nYouTube: https://www.youtube.com/c/TheArtistsofDataScience
\nInstagram: https://www.instagram.com/theartistsofdatascience/
\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/ArtistsOfData
Watch the video of this episode: https://www.youtube.com/watch?v=wjueYMuS7kw
\n\nResources:
\n\nhttps://calendly.com/harpreet-comet-ml/30min
\nhttps://cloud.google.com/architecture/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning
\nhttps://cloud.google.com/architecture?doctype=concept%2Creferencearchitecture
\nhttps://craftinginterpreters.com/
\nhttps://fullstackdeeplearning.com/spring2021/lecture-11/
\nhttps://kubernetes.io/blog/2020/12/02/dockershim-faq/
\nhttps://kubernetes.io/blog/2020/12/02/dont-panic-kubernetes-and-docker/
\nhttps://missing.csail.mit.edu/2020/version-control/
\nhttps://theartistsofdatascience.fireside.fm/kurtis-pykes
\nhttps://www.amazon.ca/Software-Architecture-Trade-Off-Distributed-Architectures/dp/1492086894
\nhttps://www.youtube.com/watch?v=a6kqyqTNJM4&list=PLhr1KZpdzukdeX8mQ2qO73bg6UKQHYsHb
\nhttps://youtube.com/playlist?list=PLhr1KZpdzukdeX8mQ2qO73bg6UKQHYsHb
Don't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\nListen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark
The Artists of Data Science Social links:
\nYouTube: https://www.youtube.com/c/HarpreetSahotaTheArtistsofDataScience
\nInstagram: https://www.instagram.com/datascienceharp
\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/datascienceharp
Watch the video of this episode: https://youtu.be/AAAV0wOLqQo
\n\nFind Christian Espinosa online:
\nhttps://christianespinosa.com/
\nhttps://www.linkedin.com/in/christianespinosa/
Memorable Quotes from the Episode:
\n\n[00:24:58] "...the final step is Kaizen. Kaizen is a is a Japanese word that means constant and never ending improvement with any of the six steps prior or the entire methodology. It's a journey, and you're not going to perfect it right out. The gate is taking this first step and the next step and the next step, and then making improvements as you move along. So that's the seven steps to the secure methodology."
\n\nHighlights of the Show:
\n\n[00:01:15] Guest Introduction.
\n\n[00:02:43] Where you grew up and what it was like there?
\n\n[00:05:43] Does Christian has the crazy interest to climb mountains?
\n\n[00:06:13] When you're growing up as a kid man, did you ever think that you'd be this crazy ultra marathon running Iron Man, mountain climbing cybercriminal fighting awesome individual?
\n\n[00:06:48] Where does that self rigor to be able to want to put yourself through these really challenging types of situation come from?
\n\n[00:09:19] What does it mean to be the smartest person in the room? What does that mean to you and when is it a bad thing?
\n\n[00:12:33] Is there a correlation or a relationship between the need to be the smartest person in the room and having like a fixed mindset?
\n\n[00:14:14] Who are these "paper tigers" and why are they so dangerous?
\n\n[00:19:20] How can you tell that somebody knows what their 'why' is? How do you assess for fit against a cultural fit?
\n\n[00:20:53] What is "secure methodology"? What are the seven steps involved in it?
\n\n[00:31:08] Do you think it's possible to identify whether we have a real growth mindset or a false one?
\n\n[00:33:02] Being congruent with your belief and the philosophy behind growth mindset.
\n\n[00:33:57] What are these NLP presuppositions in the context of your secure methodology?
\n\n[00:35:00] What are your top two favorite presuppositions for the communication part of the security framework?
\n\n[00:39:49] What is mono tasking?
\n\n[00:43:17] What are some of the NLP presuppositions that we can use to remind ourselves that it is time to get down to to multitasking?
\n\n[00:45:28] What are a couple of presuppositions that we should have in mind for the Kaisen?
\n\n[00:46:38-00:46:38] Talk to us about the four phases of kaizen.
\n\n[00:50:57] It is one hundred years in the future. What do you want to be remembered for?
\n\n[00:51:37] Random Round.
\n\n[00:51:37] When do you think the first video to hit one trillion views on YouTube will happen and what will it be about?
\n\n[00:52:11] What do most people think within the first few seconds of meeting you for the first time?
\n\n[00:52:41] What are you currently reading?
\n\n[00:53:35] What song do you currently have on repeat?
\n\n[00:53:59] What's your earliest memory?
\n\n[00:54:32] When was the last time you changed your opinion about something major?
\n\n[00:55:37] What's the best piece of advice you have ever received?
\n\n[00:56:29] What's the right way going about finding a mentor in your experience?
\n\n[00:59:01] Who was your favorite teacher and why?
\n\nDon't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\nListen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark
The Artists of Data Science Social links:
\nYouTube: https://www.youtube.com/c/TheArtistsofDataScience
\nInstagram: https://www.instagram.com/theartistsofdatascience/
\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/ArtistsOfData
Watch the video of this episode: https://youtu.be/3GG9snF8p7o
\n\nFind Kourosh Alizadah online:
\nhttps://www.linkedin.com/in/kcalizadeh/
\nhttps://philosophydata.com/
Memorable Quotes from the Episode:
\n\n[00:20:22] "...one word that's very commonly used in philosophy is the word substance and in everyday language. It just means like stuff. But in philosophy, it means like the substrate upon which all the properties change, right? So like what is the substance of a stone that stays the same even when it changes color or breaks or something like that."
\n\nHighlights of the Show:
\n\n[00:01:16] Guest Introduction.
\n\n[00:03:34] Where you grew up and what it was like there?
\n\n[00:04:41] How did you figure out who you want to be? - What did you think your feature is going to look like?
\n\n[00:06:13] Do we still have philosophers who study "philosophy and ideas"?
\n\n[00:07:59] The philosophy of Data science is if we had to kind of pin that, would there be a philosophy to Data science or of Data science?
\n\n[00:09:22] What is Data? How is it different from information or data and information? Are they the same thing?
\n\n[00:10:29] The concept of "philosophy data project".
\n\n[00:11:41] Transition from a capstone project to flat iron Data science boot camp.
\n\n[00:18:39] Did you actually read a lot of books?
\n\n[00:24:25] What are prediction probabilities?
\n\n[00:55:10] Random Rround
\n\n[00:55:12] When do you think the first video to hit $1 trillion views on YouTube will happen and what will it be about?
\n\n[00:56:07] What do most people think within the first few seconds of meeting you for the first time?
\n\n[00:56:24] What are you currently reading right now?
\n\n[00:57:18] What song do you currently have on repeat?
\n\n[00:58:29] Pet, peeves.
\n\n[00:58:37] Who are some of your heroes?
\n\n[00:59:30] When people come to you for help, what do they usually want help with?
\n\n[01:00:01] If you lost all of your possessions, but one, what would you want it to be?
\n\n[01:00:14] What fictional place would you most like to go to?
\n\nDon't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\nListen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark
The Artists of Data Science Social links:
\nYouTube: https://www.youtube.com/c/TheArtistsofDataScience
\nInstagram: https://www.instagram.com/theartistsofdatascience/
\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/ArtistsOfData
Watch the video of this episode: https://youtu.be/SOW9wUY3FpA
\n\nResources:
\n\nhttps://medium.com/@grepdennis/how-a-sql-database-engine-works-c67364e5cdfd
\nhttps://medium.com/building-the-metaverse/evolution-of-the-creator-economy-9e038e8411af
\nhttps://medium.com/data-driven-fiction
\nhttps://snap.stanford.edu/data/roadNet-CA.html
\nhttps://theartistsofdatascience.fireside.fm/guests/anderson-silver
\nhttps://theartistsofdatascience.fireside.fm/guests/donald-j-robertson
\nhttps://www.amazon.com/Continuous-Discovery-Habits-Discover-Products/dp/1736633309
\nhttps://www.amazon.com/INSPIRED-Create-Tech-Products-Customers/dp/1119387507/ref=sr_1_1?keywords=inspired&qid=1637361494&s=books&sr=1-1
\nhttps://www.amazon.com/The-Feed-Season-1/dp/B086HVT7JH
\nhttps://www.hel.fi/uutiset/en/kaupunginkanslia/a-new-minecraft-city-model-introduces-helsinki-in-more-detail
\nhttps://www.linkedin.com/in/dkjapan/
\nhttps://www.tigergraph.com/resources/
\nhttps://www.youtube.com/watch?v=YT0CScFzp1o
Don't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\nListen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark
The Artists of Data Science Social links:
\nYouTube: https://www.youtube.com/c/TheArtistsofDataScience
\nInstagram: https://www.instagram.com/theartistsofdatascience/
\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/ArtistsOfData
Watch the video of this episode: https://youtu.be/cSOXStI5sjg
\n\nFind Steven Cardinale online:
\nhttps://twitter.com/scardinale
\nhttps://www.linkedin.com/in/stevencardinale/
Memorable Quotes from the Episode:
\n\n[00:53:20] "I was talking to somebody the other day and I said, What are you selling because I'm selling media coverage for football teams? I'm like, OK, great, you know, because all football teams need people to know where they're at. Nothing but what are you really selling? Well, I'm selling it to mostly the high school teams, and really what I'm selling is, you know that parents can see their kids and media coverage. Great. What are you selling? It took him a minute and goes, Well, I'm selling the fact that parents are spending money to be have their kids on a football team. They want to see their kids names in the newspaper. So now we're starting to understand something a little more interesting."
\n\n[00:11:21] "...if you think about a data scientist, you guys are alchemists, people who work with, you know, the big data lakes and the uncertainty of data and then convert it into a decision that is the essence of alchemy."
\n\nHighlights of the Show:
\n\n[00:01:24] Guest Introduction.
\n\n[00:04:43] Where you grew up and what it was like there?
\n\n[00:04:41] How did you figure out who you want to be?
\n\n[00:07:34] What are the two definitions of entrepreneurship as mentioned in your book?
\n\n[00:12:34] What are the terms Prima Materia and the Philosopher's Stone. How is it that they fit into this three step process?
\n\n[00:40:14] The "Albedo stage". What's so unique about this stage?
\n\n[00:43:03] The idea of pollination and how it helps us grow.
\n\n[00:48:40] "Ego is the enemy."
\n\n[00:49:51] When we're moving through these three stages, like, do they happen sequentially, concurrently, all over the place? How long should we be spending each?
\n\n[00:51:44] One part that I really enjoyed was just coming up with better questions because I feel like this is something that I've heard from my mentees. They really struggle with is like, they don't even know why questions are important, let alone how to even come up with better questions. So can you share some tips on how we can do that in our work?
\n\n[00:55:43] The rubato mindset. How is this different from the other parts that we've discussed?
\n\n[01:00:29] What are some tips you can share with us for how to use and implement these ideas that you talk about?
\n\n[01:02:18] It's one hundred years in the future. What do you want to be remembered for?
\n\n[01:03:00] When do you think the first video to hit one billion views on YouTube will happen? What's it going to be about?
\n\n[01:05:01] What are you currently reading?
\n\n[01:06:12] What songs do you currently have on repeat?
\n\n[01:07:15] What's your go to dance music?
\n\n[01:07:44] What is one of your favorite smells?
\n\n[01:07:58] In your group of friends. What role do you play?
\n\n[01:09:05] What's the best piece of advice you have ever received?
\n\nDon't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\nListen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark
The Artists of Data Science Social links:
\nYouTube: https://www.youtube.com/c/TheArtistsofDataScience
\nInstagram: https://www.instagram.com/theartistsofdatascience/
\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/ArtistsOfData
Watch the video of this episode: https://youtu.be/IRkGuRMnZ6o
\n\nResources:
\n\nhttps://fossa.com/blog/analyzing-legal-implications-github-copilot/
\nhttps://github.com/jupyter-naas/awesome-notebooks
\nhttps://hbr.org/2009/01/picking-the-right-transition-strategy
\nhttps://papermill.readthedocs.io/en/latest/
Don't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\nListen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark
The Artists of Data Science Social links:
\nYouTube: https://www.youtube.com/c/TheArtistsofDataScience
\nInstagram: https://www.instagram.com/theartistsofdatascience/
\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/ArtistsOfData
Watch the video of this episode: https://youtu.be/6UJED0scgy4
\n\nFind George Firican online:
\nhttps://twitter.com/georgefirican
\nhttps://www.linkedin.com/in/georgefirican
Memorable Quotes from the Episode:
\n\n[00:42:51] "So I think everything needs to start on the business side first, so ideally, that's very clear for everybody where the business with a five year plan, if you will, for the business is so that anything else is a strategy to support that plan, right? Otherwise, it's kind of just wishful thinking. If if you want to go to Mars from a Data perspective, how can you create models for the company to be able to do that? But then if the company doesn't want to get there, then it's pointless. So that's why it's you need a business to take that first step."
\n\nHighlights of the Show:
\n\n[00:01:29] Guest Introduction.
\n\n[00:02:53] Where you grew up and what it was like there?
\n\n[00:04:02] What did you think your future was going to look like at the age of 15?
\n\n[00:08:2] What was the nudge that got you into Data? What was the experience that you had that made you realize that Data was right for you as a great teacher?
\n\n[00:09:45] As data scientist, machine learning practitioners, we're end users of the data, right?
\n\n[00:12:22] What the heck is Data governance?
\n\n[00:14:26] Responsibilities of a data analyst.
\n\n[00:15:47-00:15:50] Can anybody be a data steward? What does a data steward mean?
\n\n[00:19:33] Metadata, master data, what are those? What do they have to do with data governance?
\n\n[00:22:19] Why should Data scientists care about these types of data?
\n\n[00:23:48] Discuss data governance in action in the workplace.
\n\n[00:27:28] When you say business driver, what does that mean?
\n\n[00:29:1] So what is the goal of the organization at a high level?
\n\n[00:30:02] What are your concerns around data governance or is there kind of a a business way to ask the question so that we can translate it into our own lingo?
\n\n[00:31:06] Why is it so painful to get to have the report or access them from a dashboard in a timely fashion?
\n\n[00:33:14] What would be the types of individuals that we would want to see on the council?
\n\n[00:35:11] What are the biggest challenges you foresee her facing when he's starting out a Data strategy at this massive organization?
\n\n[00:37:05] What can Stephen King teach us about Data governance?
\n\n[00:38:41] What are Data Management and other such principles? How do we identify these principles?
\n\n[00:41:18] What does Data strategy have to do with helping us get ahead in our Data careers?
\n\n[00:42:24] How can we help our organizations define a data strategy if we find ourselves in this position of having to to build a Data strategy?
\n\n[00:43:30] Are there any blueprints that exist to help create a Data strategy?
\n\n[00:44:24] What the heck are the maturity models like?
\n\n[00:45:48] Can we have the George tech and maturity model? Does that exist?
\n\n[00:50:37] What is the difference between data scientists and data analysts?
\n\n[00:53:33] Does data governance care about unstructured data or is it only about structured data; how's that?
\n\n[00:54:32] It's 100 years in the future. What do you want to be remembered for?
\n\n[00:54:59] When do you think the first video to hit one billion views on YouTube will happen, and what will it be about?
\n\n[00:55:55] What do most people think within the first few seconds of meeting you for the first time?
\n\n[00:56:46] What are you currently reading?
\n\n[00:56:46] What are you currently reading?
\n\n[00:58:13] Pet peeves?
\n\n[00:58:44] What's on your bucket list this year?
\n\n[01:00:35] Do you ever sing when you're alone?
\n\nDon't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\nListen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark
The Artists of Data Science Social links:
\nYouTube: https://www.youtube.com/c/TheArtistsofDataScience
\nInstagram: https://www.instagram.com/theartistsofdatascience/
\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/ArtistsOfData
Watch the video of this episode: https://youtu.be/t4HevyAyMbo
\n\nResources:
\n\nhttps://www.amazon.com/Superminds-Surprising-Computers-Thinking-Together/dp/0316349135
\nhttps://www.forbes.com/sites/bernardmarr/2021/10/27/glenfiddich-sells-18000-super-rare-whisky-as-nfts--heres-what-that-means/
Don't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\nListen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark
The Artists of Data Science Social links:
\nYouTube: https://www.youtube.com/c/TheArtistsofDataScience
\nInstagram: https://www.instagram.com/theartistsofdatascience/
\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/ArtistsOfData
Watch the video of this episode: https://youtu.be/Zm2wrWgKn_g
\nFind Cristina Digiacomo online: https://www.linkedin.com/in/cristinadigiacomo
Memorable Quotes from the Episode:
\n\n[00:36:55-00:36:55] "... I know that's sort of like a pithy answer, but it's the truth. Our thoughts shape our reality. This is a very fundamental idea and concept from many, many, many, many philosophers across the millennia. We shape the circumstances in our lives just by the way that we look at them."
\n\n[00:23:14-00:23:16] "Philosophy s not just about thinking, it's about acting and acting appropriately. And so all those four things, you know, the perception of the truth and the truth. Managing your thoughts being deliberate and acting accordingly. Wisdom is the word for all of that."
\n\nHighlights of the Show:
\n\n[00:01:12] Guest Introduction
\n\n[00:02:54] Where did you grow up and what it was like there?
\n\n[00:07:08] How did you get into the DJ world?
\n\n[00:14:24] How did you get into into philosophy?
\n\n[00:15:41-00:15:41] Why is it that philosophy and wisdom [they] get lumped into these categories of being like "Woo Woo" out there? Why do you think that is?
\n\n[00:16:41] How do you define philosophy?
\n\n[00:19:46-00:19:52] Speaking of being wise, what is what is the difference between being wise and acting wise?
\n\n[00:24:23-00:24:25] How do we pause? How do we first of all, get to wisdom? How do we mitigate that knee jerk reaction?
\n\n[00:26:26] Talk to us about clarity as discussed in your book.
\n\n[00:28:52] Did you encounter any struggles when you're first trying to think in this way? I guess almost like metacognition, thinking about the way you're thinking and forcing yourself to answer these questions? Was that a bit of a challenge for you? And how did you overcome that?
\n\n[00:34:12] What are your thoughts on constantly being in thought?
\n\n[00:36:15] How can we help ourselves find out when we're having those detrimental thoughts and natural way back into something more productive, right?
\n\n[00:38:57-00:38:58] In your book you're talking about how people get really attached to their thoughts and their ideas. How can we avoid that?
\n\n[00:39:19] How do thought patterns affect our activities and what are some detriments of that?
\n\n[00:46:03] What is the real flow and how can we distinguish that from a fake flow?
\n\n[00:48:03] We talked about the importance of of inaction being just as important as as action. But if you were to just spelll it out clearly for us here, why is it that this inaction is just as important as as the action?
\n\n[00:49:48] What has philosophy taught you about being a better strategist?
\n\n[00:56:02] Is wisdom a trait that can be cultivated?
\n\n[00:56:27] Where can we cultivate this act of being wise everywhere that we are? Do we do it alone by ourselves as we interact with other people? How can we can we do that?
\n\n[00:57:20] What do you want to be remembered for?
\n\n[00:58:05] What do you think the first video to hit one billion views on YouTube will be about? And when will that happen?
\n\n[00:58:43] What do you think most people think within the first few seconds of meeting you?
\n\n[00:59:46] What are you currently reading?
\n\n[01:01:28] What languages do you speak?
\n\n[01:01:38] What's the story behind one of your scars?
\n\n[01:02:09] What's your favourite candy?
\n\nDon't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\nListen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark
The Artists of Data Science Social links:
\nYouTube: https://www.youtube.com/c/TheArtistsofDataScience
\nInstagram: https://www.instagram.com/theartistsofdatascience/
\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/ArtistsOfData
Watch the video of this episode: https://youtu.be/iHhULKL_iAU
\n\nResources: 🔗
\n\nhttps://www.makeovermonday.co.uk/
\nhttps://github.com/amirziai/sklearnflask/
\nhttps://falconframework.org/
\nhttps://substack.com/profile/16324927-vin-vashishta
\nhttps://vinvashishta.substack.com/p/leadership-essentials-setting-clear
\nhttps://towardsdatascience.com/design-a-federated-learning-system-in-seven-steps-d0be641949c6#3c54-b11966999cd5-reply
\nhttps://github.com/phecy/SSL-FEW-SHOT
Don't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\nListen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark
The Artists of Data Science Social links:
\nYouTube: https://www.youtube.com/c/TheArtistsofDataScience
\nInstagram: https://www.instagram.com/theartistsofdatascience/
\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/ArtistsOfData
Watch the video of this episode: https://youtu.be/R4Xr5OiVuzo
\n\nFind Andy online:
\nhttps://twitter.com/PragmaticAndy
\nhttps://www.linkedin.com/pragmaticandy
Memorable Quotes from the episode:
\n\n[00:04:41-00:04:43] "I probably discovered computers around seventy six, seventy seven or so. In fact, the very first computer I ever experienced up close and personal not just read about was a teletype typewriter with an acoustic color. And you know, the kids in the audience are going, What's an acoustic coupler, grandpa? Right? If you remember the movie war games, you dial the phone with a handset and you stick a handset down in these foam cups and it goes. It makes the noises, and that's how you talk to the big giant mainframe, you know, and then literally in the next county over. So that was sort of my first, my first experience and I was hooked."
\n\n[00:35:54] "You are told in school that you're a dummy, that you can't ever learn anything that you're not good at Math. Oh my God. You know, I'm not a violent person, but I would shoot every teacher who told some girl, Oh, girls aren't good at math. You don't need to study that right? Bullshit. Hundred percent legit bullshit. The problem with that Is if you've been told by an authority figure, by a teacher that you can't learn something and then you believe it, then your brain wires itself so that you're not going to be able to learn it. It's a self modifying machine and it becomes a self-fulfilling prophecy. Which, to me, it's criminal. It's absolutely criminal."
\n\nHighlights from the show:
\n\n[00:01:32] Guest Introduction
\n\n[00:02:40] Talk to us a little bit about where you grew up and what was it like there.
\n\n[00:05:54] From the early age, did you just decide I'm going to go to school and I'm going to study computers and software?
\n\n[00:11:00] What is it with this pragmatic stuff? What does that mean to you?
\n\n[00:13:14] What if you're somebody who's just been constrained by your processes?
\n\n[00:20:16] Is it possible for me to be able to think like a software engineer, like an exceptional software engineer or software developer without necessarily being one?
\n\n[00:34:04] What is expertize and why is it so difficult to articulate?
\n\n[00:38:25] Can you talk to us about what Dreyfus model is and and why is it important that we understand this Dreyfus model?
\n\n[00:41:58] What's a good way for us to kind of accurately self assess where we would fall on this spectrum?
\n\n[00:54:18] How was it that you got interested in these type of books (pragmatic thinking) and how have they helped you and your journey?
\n\n[00:58:15] It's one hundred years in the future. What do you want to be remembered for?
\n\n[01:00:50] What is GROWS method?
\n\n[01:06:23] What are you listening to right now? What do you have on kind of repeat?
\n\nDon't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\nListen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark
The Artists of Data Science Social links:
\nYouTube: https://www.youtube.com/c/TheArtistsofDataScience
\nInstagram: https://www.instagram.com/theartistsofdatascience/
\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/ArtistsOfData
Resources: 🔗
\n\nhttps://missing.csail.mit.edu/
\nhttps://www.amazon.com/Pro-Git-Scott-Chacon-ebook/dp/B01ISNIKES/
\nhttps://progit2.s3.amazonaws.com/en/2016-03-22-f3531/progit-en.1084.pdf
\nhttps://www.udemy.com/course/git-and-github-bootcamp/
\nhttps://github.com/romkatv/powerlevel10k
\nhttps://github.com/ericgitonga/code-snippets
\nhttps://twitter.com/HBOMaxHelp/status/1405712235108917249
\nhttps://fossbytes.com/linus-torvaldss-famous-email-first-linux-announcement/
\nhttps://theartistsofdatascience.fireside.fm/greg-coquillo
\nhttps://hbr.org/2009/01/picking-the-right-transition-strategy
\nhttp://www.jefflichronicles.com/blog/2020/9/26/i-got-7-job-offers-during-the-worst-job-market-in-history-heres-the-data
Check it out and don't forget to register for future office hours: http://bit.ly/adsoh
\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\nListen to the latest episode of Emily Balcetis: http://theartistsofdatascience.fireside.fm/emily-balcetis
The Artists of Data Science Social links:
\nYouTube: https://www.youtube.com/c/TheArtistsofDataScience
\nInstagram: https://www.instagram.com/theartistsofdatascience/
\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/ArtistsOfData
Links:
Find Emily online: https://www.psychologytoday.com/us/contributors/emily-balcetis-phd
\nVideo of this episode: https://youtu.be/5SrfAxUHLyw
Highlights of the show:
\n\n[00:00:20] Guest Introduction
\n\n[00:02:40] Where did you grow up and what was it like there?
\n\n[00:07:09] Is life different now than what you imagined it would be?
\n\n00:10:02] At a high level, how does this experience we call site work? Is it just like, seeing things? And that's it, like what's going on in my head when I'm looking at something?
\n\n[00:14:21] You talk about these four different ways that are our sight helps us achieve our goals. Can you talk about them at a high level?
\n\n[00:28:13] Once we do find that one thing we want to focus on, like, how do we push ourselves through to the finish line?
\n\n[00:38:36-00:38:41] Can you help us understand what narrow focus is as knowledge workers? How can we use narrow focus to help us achieve our goals?
\n\n[00:45:55] how do we concretely identify a definitive moment of success before starting our journey?
\n\n[01:01:39] Positive feedback can sometimes backfire, right when we're pursuing our goals. What what is it about that?
\n\n[01:09:30] It is one hundred years in the future. What do you want to be remembered for?
\n\n[01:10:33] Random Round
\n\n[01:10:37] What's on your bucket list this year?
\n\n[01:10:52] What makes you cry?
\n\n[01:11:09] What's one of your favorite comfort foods?
\n\n[01:11:27] What's the last book you gave up on and stopped reading?
\n\n[01:12:08] What are you currently reading?
\n\n[01:12:45] What song do you got on repeat nowadays?
\n\n*The Artists of Data Science Social links: *
\n\nYouTube: https://www.youtube.com/c/TheArtistsofDataScience
\nInstagram: https://www.instagram.com/theartistsofdatascience/
\nFacebook https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/ArtistsOfData
Check it out and don't forget to register for future office hours: http://bit.ly/adsoh
\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\n\nListen to the latest episode: http://theartistsofdatascience.fireside.fm/john-vervaeke
\n\nThe Artists of Data Science YouTube: https://www.youtube.com/c/TheArtistsofDataScience
\n\nhttps://www.instagram.com/theartistsofdatascience/
\nhttps://facebook.com/TheArtistsOfDataScience
\nhttps://twitter.com/ArtistsOfData
Visit Eric online: https://twitter.com/iamericokon?lang=en
\nVideo of this episode: https://youtu.be/k6KOE4VDVpg
Highlights of the Show:
\n\n[00:00:53] Guest Introduction
\n\n[00:01:38] Where did you grow up and what was it like there?
\n\n[00:04:05] When you're coming up in high school, like, was this kind of expected of you to go into the family business? Was it that big at that time?
\n\n[00:05:53] What kind of jobs did you have coming up in the family business?
\n\n[00:11:48] How did you go from working in the family business to eventually getting into the podcast game? What was the most that transition?
\n\nEric: [00:31:32] Are you a believer in the law of attraction?
\n\nEric: [00:34:45] ...because you're a scientist, are you spiritual?
\n\nEric: [00:36:53] Have you ever been to mediums or anything like that?
\n\n[00:41:16] Would you rather be stuck on a broken ski lift or a broken elevator?
\n\n[00:42:12] What are you interested in that most people haven't heard of?
\n\nhttps://www.instagram.com/theartistsofdatascience/
\nhttps://facebook.com/TheArtistsOfDataScience
\nhttps://twitter.com/ArtistsOfData
Check it out and don't forget to register for future office hours: http://bit.ly/adsoh
\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\n\nListen to the latest episode: http://theartistsofdatascience.fireside.fm/john-vervaeke
\n\nThe Artists of Data Science YouTube: https://www.youtube.com/c/TheArtistsofDataScience
\n\nhttps://www.instagram.com/theartistsofdatascience/
\nhttps://facebook.com/TheArtistsOfDataScience
\nhttps://twitter.com/ArtistsOfData
Find John online: https://www.youtube.com/user/johnvervaeke
\nPodcast episode of the show: http://theartistsofdatascience.fireside.fm/john-vervaeke
Memorable Quotes from the Episode:
\n\n"Wisdom isn't about dealing with ignorance, wisdom is about dealing with foolishness, and foolishness has to do with misunderstanding. Understanding is to appropriately grasp the significance or relevance of what you know."
\n\n[00:41:26] "Your worldview is your ultimate home. It's the home of all homes, right? And so we had a bunch of practices that arose within the axial age worldview, The two, worlds worldview."
\n\nHighlights of the Show:
\n\n[00:00:29] Guest Introduction.
\n\n[00:02:40] Where did you grow up and what was it like there?
\n\n[00:05:38] Was it you just researching other religions and getting into other religions?
\n\n[00:12:14] "Problem formulation is doing a lot of the heavy work."
\n\n[00:17:35] How do we how do we know if we're stuck in like a local optimum?
\n\n[00:30:13] How do we increase insight?
\n\n[00:32:35] Can you help us understand what is the axial age? When did the first one start? Why do you think it started?
\n\n[00:42:36] What is the essence of the meaning crisis?
\n\n[00:42:49] Where do you go for wisdom?
\n\n[00:49:59] How do we cultivate wisdom within ourselves if there's kind of a scarcity of a wisdom institution and the kind of help us understand where the wisdom institution is as well?
\n\n[00:59:35] Apps like Clubhouse, for instance, do you think this is kind of helping to facilitate these wisdom institutions or does something need to kind of be more formal in place to make that happen?
\n\n[01:00:12] Propaganda and propaganda means it propagates, right?
\n\n[01:02:49] Random Round.
\n\n[01:02:55] What if something you can never seem to finish?
\n\n[01:03:25] What have you created that you are most proud of?
\n\n[01:03:38] What is an unpopular opinion that you have?
\n\n[01:03:57] Pet peeves.
","summary":"","date_published":"2021-10-08T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/347da8fa-19a3-45d5-a0f7-e21730c994fd.mp3","mime_type":"audio/mpeg","size_in_bytes":78653985,"duration_in_seconds":3930}]},{"id":"4b7ced51-0efc-4c64-b17e-df516fdb9bc5","title":"Data Science Happy Hour 52 | 01OCT2021","url":"https://theartistsofdatascience.fireside.fm/hh52","content_text":"Vote in the data community content creators awards! http://bit.ly/data-creators-awards\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh\n\nThe Artists of Data Science YouTube: https://www.youtube.com/c/TheArtistsofDataScience\n\nhttps://www.instagram.com/theartistsofdatascience/\nhttps://facebook.com/TheArtistsOfDataScience\nhttps://twitter.com/ArtistsOfData","content_html":"Vote in the data community content creators awards! http://bit.ly/data-creators-awards
\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh
\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\n\nThe Artists of Data Science YouTube: https://www.youtube.com/c/TheArtistsofDataScience
\n\nhttps://www.instagram.com/theartistsofdatascience/
\nhttps://facebook.com/TheArtistsOfDataScience
\nhttps://twitter.com/ArtistsOfData
Visit Madison Schott here:
\nhttps://www.instagram.com/maemaddie/
\nhttps://linktr.ee/maemaddie
Watch video of this episode:
\nhttps://www.youtube.com/watch?v=ODxVKFTt2II
MEMORABLE QUOTES FROM THE EPISODE
\n\n[00:21:49] "...I think a lot of people in the wellness community don't talk about is that one diet doesn't fit everyone you want to eat. What makes you feel good? Don't eat a diet just because someone else is saying that that the best for the environment or best for your health, like listen to your own body. It's so important."
\n\n[00:23:10] "…I guess a few things that have personally helped me a lot is sleep. I was definitely so sleep deprived throughout high school and college, and good sleep really makes a huge difference."
\n\n[00:32:53] "I guess people don't value their health as much as they should, too, which is honestly sad, but they don't realize how everything relates. Like, I think more and more people are kind of taking control of their health, but at least with young people, I think older people are still stuck in their ways and the ideas that are etched in their minds."
\n\nHIGHLIGHTS OF THE SHOW
\n\n[00:01:00] Guest Introduction
\n\n[00:05:35] How different is life now than what you imagined it would be?
\n\n[00:09:16] Where do you see Data Engineering headed in like the next two to five years?
\n\n[00:11:24] What aspiring Data engineers could do now to help prepare themselves for the future?
\n\n[00:12:14] What are some blogs or newsletter that you currently are following or subscribe to?
\n\n[00:13:30] What are your thoughts about data engineering becoming the new sexiest job of the century?
\n\n[00:14:23] What's the difference between a data architect and a data engineer?
\n\n[00:15:30] How are you thriving so far in the quarantine?
\n\n[00:24:13] Scientific secrets of optimal timing.
\n\n[00:26:55] How black coffee on an empty stomach affects your hormones.
\n\n[00:27:50] How have you been implementing this into your daily routine?
\n\n[00:29:31] What is your morning routine like?
\n\n[00:34:06] What are some skills that you think that are not technical at all that people in the data and the tech field should cultivate to continue their success in their career?
\n\n[00:35:13] Learning how to learn: How have you developed and cultivated that skill for yourself?
\n\n[00:42:30] What can the Data Community do to help foster the inclusion of women in Data Science types of roles?
\n\n[00:43:19] It's one hundred years in the future. What do you want to be remembered for?
\n\n[00:44:25] RANDOM ROUND
\n\n[00:44:27] When do you think the first video to hit $1 trillion views on YouTube will happen and what will it be about?
\n\n[00:45:12] When do you think the world will end, Madison?
\n\n[00:45:43] In your opinion, what do most people think within the first few seconds of meeting you for the first time?
\n\n[00:46:27] What are you currently reading, currently reading?
\n\n[00:48:52] What are you currently listening to? Speaking of music, what do you have on repeat?
\n\n[00:49:22] What is one of your favorite smells?
\n\n[00:49:37] What's something you wish you'd figured it out sooner?
\n\n[00:49:52] Do you ever sing when you're alone?
","summary":"","date_published":"2021-10-01T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/82d1a496-8b09-49a1-ac76-3030037004ac.mp3","mime_type":"audio/mpeg","size_in_bytes":73939631,"duration_in_seconds":3078}]},{"id":"7438510f-03f5-4857-9f32-0ee274f7c35d","title":"Comet ML Office Hour 31 | 26SEP2021","url":"https://theartistsofdatascience.fireside.fm/comet-ml-31","content_text":"Vote in the data community content creators awards! http://bit.ly/data-creators-awards\nCheckout Purdeep Sangha's episode here: https://theartistsofdatascience.fireside.fm/purdeep-sangha\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh\n\nYouTube: https://www.youtube.com/c/TheArtistsofDataScience\nInstagram: https://www.instagram.com/theartistsofdatascience/\nFacebook: https://facebook.com/TheArtistsOfDataScience\nTwitter: https://twitter.com/ArtistsOfData","content_html":"Vote in the data community content creators awards! http://bit.ly/data-creators-awards
\nCheckout Purdeep Sangha's episode here: https://theartistsofdatascience.fireside.fm/purdeep-sangha
\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh
Register for Sunday Sessions here: http://bit.ly/comet-ml-oh
\n\nYouTube: https://www.youtube.com/c/TheArtistsofDataScience
\nInstagram: https://www.instagram.com/theartistsofdatascience/
\nFacebook: https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/ArtistsOfData
Vote in the data community content creators awards! http://bit.ly/data-creators-awards
\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh
\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\n\nThe Artists of Data Science YouTube: https://www.youtube.com/c/TheArtistsofDataScience
\n\nhttps://www.instagram.com/theartistsofdatascience/
\nhttps://facebook.com/TheArtistsOfDataScience
\nhttps://twitter.com/ArtistsOfData
Visit Dennis Will's link: https://www.linkedin.com/in/dennis-will-226407168/
\nVideo of the episode: https://youtu.be/FvimZnYhzK0
MEMORABLE QUOTES FROM THE EPISODE
\n\n[00:07:10] "I think Python is actually a very good language to get into even as your first language, but you still should be open to what the language offers and what it's offered beyond Python. So I think C is a good starting point, but I think it might actually turn off a lot of people because it is very inherently difficult. Like a lot of concepts that seem like like the way pointers work, that's something that you don't even have to deal with in Python if you don't want to."
\n\n[00:12:00] "...usually companies don't want to spend that much money. So that's why you have to think up a solution that is very efficient, very memory efficient and in the end also gets the job done."
\n\n[00:13:24] "You have one big cluster which has several workers, and this allows you to process data in parallel. And what I don't like is that it is a very useful to know, but most in most cases, you don't need that. It's very expensive. It's only for a very huge data sets and companies like to use for every single thing. So a lot of the things you can actually do with other services like like serverless functions that are a good example."
\n\n[00:25:51] "A very good example that in the previous years has grown and a lot of companies say, we want to do machine learning, we want to do data science, but they don't even know what that means. That's why I think that it's going to get more important because for data science to be able to work properly, you need a good architecture to Data in the right place. And I think only going to get bigger from here."
\n\nHIGHLIGHTS OF THE SHOW
\n\n[00:00:55] Guest Introduction
\n\n[00:02:44] Talk to us about where you grew up and what was it like there?
\n\n[00:04:34] What's your comments on Art and Museums in Berlin?
\n\n[00:05:11] In high school, what did you think your future would look like?
\n\n[00:06:10] What was the language of choice back then?
\n\n[00:07:06] Do you think C would be the way to go?
\n\n[00:08:10] How did you get interested in Data engineering?
\n\n[00:09:32] What are a couple of concepts, maybe two to three concepts that you think would be extremely beneficial for a data scientist to learn about data science so that we can help make each other's lives easier?
\n\n[00:12:35] Do you use any frameworks or packages to help you with data engineering? Does anything like that exist that we should probably know about or I?
\n\n[00:14:24] There's always new stuff popping up, and you always have to try to keep up on stuff. How do you manage that? New tech comes out that you either hear or read about. What's your process for determining? Is this something I should spend my time?
\n\n[00:17:05] How did you find yourself getting into Azure?
\n\n[00:18:47] How important do you think being resourceful has been in your career?
\n\n[00:20:04] Difference between a Data architect and a Data engineer. So how are these two roles similar? How are they different?
\n\n[00:23:24] What are some of the things that you would ask your client so that you can figure out what it is that you need to go do?
\n\n[00:27:20] What aspiring Data engineers can do now to help prepare themselves for the future?
\n\n[00:30:13] What are some of your favorite misconceptions about what it is that a Data engineer does?
\n\n[00:31:30] What can a Data scientists do to make the lives of their Data engineering colleagues easier?
\n\n[00:33:09] Do you have any words of encouragement or advice to share with anyone who's afraid to ask questions because they don't want to look stupid?
\n\n[00:44:15] Do you have any tips or any ideas on how to work on a Data Engineering project?
\n\n[00:46:30] What are some tips you can leave with our audience on how we can be more valuable in our jobs?
\n\n[00:48:06] It's 100 years in the future. What do you want to be remembered for?
\n\n[00:49:58] Random Round
\n\n[00:50:02] When do you think the first video to hit one trillion views on YouTube will happen? And what will that video be about?
\n\n[00:53:20] What are you currently reading?
\n\n[00:54:30] What song do you currently have on repeat?
\n\n[00:55:48] What incredibly strong opinion do you have that is completely unimportant in the grand scheme of things?
\n\n[00:56:30] Do you have a signature dish that you're well known for?
\n\n[00:56:47] What is one of your favorite smells?
\n\n[00:57:30] What story does your family always talk about you?
\n\n[00:57:45] What's one of your favorite comfort foods
","summary":"","date_published":"2021-09-24T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/c1abd8c6-7c64-4c1a-b16c-46b956d41553.mp3","mime_type":"audio/mpeg","size_in_bytes":73246171,"duration_in_seconds":3660}]},{"id":"33a0af61-c1ed-40b6-a038-c5d56e6d63c0","title":"Comet ML Office Hour 30 | 19SEP2021","url":"https://theartistsofdatascience.fireside.fm/comet-ml-30","content_text":"Vote in the data community content creators awards! http://bit.ly/data-creators-awards\nCheckout Purdeep Sangha's episode here: https://theartistsofdatascience.fireside.fm/purdeep-sangha\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh\n\nYouTube: https://www.youtube.com/c/TheArtistsofDataScience\nInstagram: https://www.instagram.com/theartistsofdatascience/\nFacebook: https://facebook.com/TheArtistsOfDataScience\nTwitter: https://twitter.com/ArtistsOfData","content_html":"Vote in the data community content creators awards! http://bit.ly/data-creators-awards
\nCheckout Purdeep Sangha's episode here: https://theartistsofdatascience.fireside.fm/purdeep-sangha
\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh
Register for Sunday Sessions here: http://bit.ly/comet-ml-oh
\n\nYouTube: https://www.youtube.com/c/TheArtistsofDataScience
\nInstagram: https://www.instagram.com/theartistsofdatascience/
\nFacebook: https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/ArtistsOfData
Vote in the data community content creators awards! http://bit.ly/data-creators-awards
\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh
\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\n\nThe Artists of Data Science YouTube: https://www.youtube.com/c/TheArtistsofDataScience
\n\nhttps://www.instagram.com/theartistsofdatascience/
\nhttps://facebook.com/TheArtistsOfDataScience
\nhttps://twitter.com/ArtistsOfData
Watch the episode here: https://youtu.be/g_uP3beFcsc
\nwww.purdeepsangha.com
\nhttps://www.instagram.com/purdeepsangha
\nhttps://www.linkedin.com/in/purdeepsangha
MEMORABLE QUOTES FROM THE EPISODE:
\n\n[00:51:59] "...every decision you make requires thinking power, every conscious decision. And so the more decisions you make, the more energy you actually consume. So this is important, too, because a lot of people make decisions when they don't have the right energy and when they're not in the right state. And that impacts them as well."
\n\n[00:32:34] "If you have an identity that's aligned with your goal, you will be way more likely to achieve it than if your identity wasn't aligned with your goal."
\n\n[00:11:24] "I was talking to a gentleman the other day, a very successful CEO, and I was asking him about his goals. And he actually pointed out himself. He said he's not very motivated because he uses a strict logic when he sets goals. And it's very logical process for him. And so he's not excited. And I said, you know what you need to do, put some emotion behind it, because you have to be excited. You have to be driven. And this is shown from studies from Stanford that the more emotions you put behind your goals, the more likely you are to execute, the more passion you're going to be passionate, you're going to be the more consistent you're going to be, the more persistent you're going to be in achieving those goals. So there's a direct correlation between a lot of people think that goal setting is important. It definitely is. But even with goal setting, six months later, over 60 percent of the people drop those goals. And even though that they're looking at those goals every day, for example, they're just not premium enough. They're not exciting enough for them to keep going. That is the difference between an average goal and a premium goal is a premium goal will pull you through. You're not necessarily having to push yourself. You're actually drawn to it."
\n\n[01:00:44] "fear is actually one of those emotions that if it's not doing you any good, you have to learn from it and understand why you're fearful. And there's definitely a reason for it. And I would say out of all the emotions, fear is one of the ones that can be your biggest teacher. That's how I to take a look of fear. And I say, OK, what about this? Why am I feeling afraid of this? What is it? What is it in me? Is it a lack of skills that I that I feel I'm lacking? So there therefore I'm feeling this fear. If I am I thinking too far in the future and my being too pessimistic, there's always something that you can learn from fear. So ask yourself that. The other thing is, understand that most of your fear, unless you're actually being held at gunpoint or you're getting robbed or is is just an illusion of your brain. And understand that. So most of our fear, it's actually not based on the present moment. Most of our fears are based on future. If we take a look at worry, it's a it's a future based fear."
\n\n[00:23:19] "...systems are so fundamental because two things, it accelerates your results. But the second thing is, you know, you get to figure out what's not working for you as well."
\n\nHIGHLIGHTS OF THE SHOW:
\n\n[00:01:10] Guest Introduction
\n\n[00:02:56] Where did you grow up and what was it like there?
\n\n[00:05:17] What kind of kid were you and what did you think that your future would look like?
\n\n[00:08:23] How does discipline help you focus on which goal to pursue?
\n\n[00:10:10] What is a premium goal and what separates it from just like a regular core?
\n\n[00:12:35] What emotion should we put behind ourselves to achieve our goals?
\n\n[00:14:02] What's the trigger that we put in place in our mind?
\n\n[00:21:39] What is the difference between systems and goals, or do we need systems to help us achieve goals? How's that work?
\n\n[00:23:54] Talk to us about what our identity is.
\n\n[00:31:11] Talk to us about the relationship between our identities and our goals.
\n\n[00:35:44] What are beliefs and why are they important?
\n\n[00:41:35] How do we figure out if our beliefs are empowering or disempowering, if this is all we've had our entire life?
\n\n[00:47:00] What is the three step process that the brain goes through when it comes to making decisions?
\n\n[00:53:55] Four hours of decision making.
\n\n[00:57:57] What is fear? What's its purpose from the brain science kind of perspective?
\n\n[01:00:02] Do you have any other tips to wrestle that fear so that it pushes us from behind rather than stand in front of us?
\n\n[01:04:35] How can we combine our skills in such a way that we can multiply the value that we're able to create?
\n\n[01:08:20] What is the meaning of life?
\n\n[01:10:54] It's 100 years in the future, what do you want to be remembered for?
\n\n[01:12:17] RANDOM ROUND.
\n\n[01:12:20] What are you currently reading?
\n\n[01:13:25] What song do you have on repeat?
\n\n[01:14:41] Random Question Generator.
\n\n[01:14:41] Pet peeves.
\n\n[01:14:53] Would you rather be stuck on a broken ski lift or a broken elevator?
\n\n[01:15:30] What are you interested in that most people haven't heard of?
\n\n[01:16:11] What dumb accomplishment are you most proud of?
","summary":"","date_published":"2021-09-17T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/a35a48b9-ec61-4770-a59c-11e59e205db2.mp3","mime_type":"audio/mpeg","size_in_bytes":74922453,"duration_in_seconds":4680}]},{"id":"856b6fc3-b5b4-461c-ba4c-9ba79b9882b5","title":"Comet ML Office Hour 29 | 12SEP2021","url":"https://theartistsofdatascience.fireside.fm/comet-ml-29","content_text":"Vote in the data community content creators awards! http://bit.ly/data-creators-awards\nCheckout Max Frenzel's episode here: https://theartistsofdatascience.fireside.fm/max-frenzel\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh\n\nYouTube: https://www.youtube.com/c/TheArtistsofDataScience\nInstagram: https://www.instagram.com/theartistsofdatascience/\nFacebook: https://facebook.com/TheArtistsOfDataScience\nTwitter: https://twitter.com/ArtistsOfData","content_html":"Vote in the data community content creators awards! http://bit.ly/data-creators-awards
\nCheckout Max Frenzel's episode here: https://theartistsofdatascience.fireside.fm/max-frenzel
\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh
Register for Sunday Sessions here: http://bit.ly/comet-ml-oh
\n\nYouTube: https://www.youtube.com/c/TheArtistsofDataScience
\nInstagram: https://www.instagram.com/theartistsofdatascience/
\nFacebook: https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/ArtistsOfData
Vote in the data community content creators awards! http://bit.ly/data-creators-awards
\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh
\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\n\nThe Artists of Data Science YouTube: https://www.youtube.com/c/TheArtistsofDataScience
\n\nhttps://www.instagram.com/theartistsofdatascience/
\nhttps://facebook.com/TheArtistsOfDataScience
\nhttps://twitter.com/ArtistsOfData
Max Frenzel links:
\n\nhttps://maxfrenzel.com
\nhttps://www.linkedin.com/in/max-frenzel-60597361/
\nhttps://maxfrenzel.com/music
MEMORABLE QUOTES FROM THE EPISODE:
\n\n[00:44:04] "Rest ethic is something that we need to work on and we need to cultivate knowing when it's time to make that step away and then trusting in the process that will follow and not seeing it as a waste of time. That's probably the most difficult thing about a time off, but also the most valuable once you managed to really follow through."
\n\n[00:39:10] "…the brain doesn't tire like an arm and a leg all at once is variety. And I think that's really true there."
\n\n[00:18:28] "...free level hierarchy where at the very bottom is rest, rest, basically always ask the question. Rest for what? And the answer is usually, well, rest to do more work. And that's OK. Sometimes you just need to rest to then be refreshed for whatever else you want to do..."
\n\nHIGHLIGHTS OF THE SHOW:
\n\n[00:01:20] Guest Introduction
\n\n[00:03:25] Talk to us about where you grew up and what was it like there?
\n\n[00:11:36] How did work create time or did time create work like chicken or egg? How did this happen?
\n\n[00:17:52] What would Aristotle have to say about this, the way we're working in modern society?
\n\n[00:22:42] How would they (in ancient Greece or ancient Rome) have measured their productivity if they measured it at all?
\n\n[00:28:44] Talk to us about this epidemic that we have over a lot of burnout and how can we identify for ourselves if we're burning out?
\n\n[00:34:20] What does rest look like?
\n\n[00:37:34] What is it about rest in this way that helps us go from this feeling of just tense overwork to just suddenly just relax?
\n\n[00:39:27] The idea of mental crop rotation.
\n\n[00:41:17] When's the last time you were in the flow state?
\n\n[00:45:34] Max's Track: Dorobo
\n\n[00:46:47] How are you integrating into music or are you just like things like statistical samples of different beats that are out there and putting them together?
\n\n[00:53:59] How do you define creativity in the age of the knowledge worker?
\n\n[01:01:02] Do you have one of these little mini journal pads
\n\n[01:01:58] What can jazz musicians teach us about how to approach our careers and our work as Data scientists?
\n\n[01:06:06] "The multi dream theory"
\n\n[01:09:08] Is Data Science art or is it a science or Data science machine learning an art or purely a hard science? Where does science and art begin? Are they completely
\nseparate? How do you view this?
[01:11:18] How can somebody who doesn't really view themselves as a creative person actually realize that they are creative?
\n\n[01:18:06] Which is the one profile that you think we as a society can learn most from, given our current state of the world. And in what way can we learn the most from it?
\n\n[01:19:12] It's one hundred years in the future. What do you want to be remembered for?
\n\n[01:20:46] RANDOM ROUND
\n\n[01:21:00] What would you say is the most fundamental truth of physics that all humans should understand?
\n\n[01:23:27] When do you think the first video to hit one trillion views on YouTube will happen? And what will it be about?
\n\n[01:24:10] What do most people think within the first few seconds when they meet you for the first time?
\n\n[01:24:40] Do you think you have to achieve something in order to be worth something?
\n\n[01:25:20] What are you currently reading?
\n\n[01:27:06] What song do you have on repeat right now.
\n\n[01:28:04] What are you a natural at?
\n\n[01:28:43] What's your favorite piece of clothing you own?
\n\n[01:29:42] What are you interested in that most people haven't heard of?
","summary":"","date_published":"2021-09-10T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/b322dd79-d42b-4e28-845d-be430c65dfcd.mp3","mime_type":"audio/mpeg","size_in_bytes":90532911,"duration_in_seconds":5592}]},{"id":"75eab958-67bc-4c7b-9897-2cfdcf01ed61","title":"Data Science Happy Hour 48 | 03SEP2021","url":"https://theartistsofdatascience.fireside.fm/hh48","content_text":"Vote in the data community content creators awards! http://bit.ly/data-creators-awards\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh\n\nThe Artists of Data Science YouTube: https://www.youtube.com/c/TheArtistsofDataScience\n\nhttps://www.instagram.com/theartistsofdatascience/\nhttps://facebook.com/TheArtistsOfDataScience\nhttps://twitter.com/ArtistsOfData","content_html":"Vote in the data community content creators awards! http://bit.ly/data-creators-awards
\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh
\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\n\nThe Artists of Data Science YouTube: https://www.youtube.com/c/TheArtistsofDataScience
\n\nhttps://www.instagram.com/theartistsofdatascience/
\nhttps://facebook.com/TheArtistsOfDataScience
\nhttps://twitter.com/ArtistsOfData
Visit Tiffany Shlain's link here:
\n\nhttps://www.tiffanyshlain.com
\nhttps://www.instagram.com/tiffanyshlain
MEMORABLE QUOTES FROM THE EPISODE:
\n\n"[00:16:16] "...we never give our brain this what I think is the most magical space for the brain, which is to daydream and to just think on its own."
\n\n"[00:07:25] "...I think that the wisdom around the day of rest is that you need to separate work and rest. And the interesting thing about technology is that it blurred every boundary. You could work from the beach, you could work from your bedroom, you could work from anywhere, which, of course, you know, has had its benefits."
\n\n"[00:40:59] "...I think that the more women that are funding projects, the more women that are leading and the more women that are creating environments. So women can be mothers and in the workforce, the better. But I feel very hopeful."
\n\nHIGHLIGHTS OF THE SHOW:
\n\n[00:00:29] GUEST INTRODUCTION
\n\n[00:02:46] What is the story behind your hat and red lipstick?
\n\n[00:05:05] What is the Shabbat?
\n\n[00:13:39] Why is it that having this day of rest allows the human mind to do this big picture thinking that allows us to drive our culture and our civilization forward?
\n\n[00:27:46] How are algorithms manipulating our animal instincts?
\n\n[00:36:57] How are their algorithms impacting that development and refinement of these muscles in ourselves?
\n\n[00:40:28] How can we make sure that this current generation of women in tech don't go through those same struggles?
\n\n[00:43:55] RANDOM ROUND
\n\n[00:43:59] What is your favorite song to play on the ukulele?
\n\n[00:44:18] When do you think the first video to hit one trillion views on YouTube will happen? And what will that video be about?
\n\n[00:45:00] Do we have to achieve something in order to be worth something?
\n\n[00:45:29] What are you currently reading?
\n\n[00:46:19] What's something you learned in the last week?
\n\n[00:47:03] Who was your favorite teacher and why?
\n\n[00:47:32] What dumb accomplishment are you most proud of?
\n\n[00:48:03] What is your favorite city?
","summary":"","date_published":"2021-09-03T11:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/e7cd1f0c-2a66-4798-adcd-064440260e3e.mp3","mime_type":"audio/mpeg","size_in_bytes":71884174,"duration_in_seconds":2993}]},{"id":"5905b2e5-8519-4e6f-87ff-ee9057fde2af","title":"Comet ML Office Hour 28 | 29AUG2021","url":"https://theartistsofdatascience.fireside.fm/comet-ml-28","content_text":"Vote in the data community content creators awards! http://bit.ly/data-creators-awards\nCheckout Jeffery Li's episode here: https://theartistsofdatascience.fireside.fm/jeff-li\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh\n\nYouTube: https://www.youtube.com/c/TheArtistsofDataScience\nInstagram: https://www.instagram.com/theartistsofdatascience/\nFacebook: https://facebook.com/TheArtistsOfDataScience\nTwitter: https://twitter.com/ArtistsOfData","content_html":"Vote in the data community content creators awards! http://bit.ly/data-creators-awards
\nCheckout Jeffery Li's episode here: https://theartistsofdatascience.fireside.fm/jeff-li
\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh
Register for Sunday Sessions here: http://bit.ly/comet-ml-oh
\n\nYouTube: https://www.youtube.com/c/TheArtistsofDataScience
\nInstagram: https://www.instagram.com/theartistsofdatascience/
\nFacebook: https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/ArtistsOfData
Vote in the data community content creators awards! http://bit.ly/data-creators-awards
\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh
\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\n\nThe Artists of Data Science YouTube: https://www.youtube.com/c/TheArtistsofDataScience
\n\nhttps://www.instagram.com/theartistsofdatascience/
\nhttps://facebook.com/TheArtistsOfDataScience
\nhttps://twitter.com/ArtistsOfData
Vote in the data community content creators awards! http://bit.ly/data-creators-awards
\nCheckout Jonathan Tesser's episode here: https://theartistsofdatascience.fireside.fm/jonathan-tesser
\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh
Register for Sunday Sessions here: http://bit.ly/comet-ml-oh
\n\nYouTube: https://www.youtube.com/c/TheArtistsofDataScience
\nInstagram: https://www.instagram.com/theartistsofdatascience/
\nFacebook: https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/ArtistsOfData
MEMORABLE QUOTES
\n[00:11:51] “..and then once you kind of clearly define exactly the outcome that you’re aiming for, it makes it a little bit easier to start breaking it down into chunks.”
[00:28:51] “ There are so many things that are outside of control. Just don’t even worry about those but focus on the things you can control.”
\n\n[00:38:28] “…having too much of it can be crippling but having a little bit can actually motivate you and push you to continue to grow your skillset which, ultimately, is beneficial to you and everybody around you.”
\n\n[00:40:15] “Fear is a powerful emotion. If you can wrestle it and have it push you from behind rather than block you, you learn whatever it is you’ve got to learn and improve and grow.“
\n\n[00:54:45] “As you provide more value and you’re reliable and you are providing good work, your influence will gradually grow.”
\n\n[01:02:12] “ I guess I’d want to be remembered for somebody who took on very difficult ambitious challenges where I knew that I had little possibility of succeeding but I still did it anyway.”
\n\nHIGHLIGHTS FROM THE SHOW
\n\n[00:00:51] Guest introduction.
\n\n[00:02:54] Can you tell us a little bit about where you grew up and what it was like there?
\n\n[00:03:42] When you were in high school, what did you think your future would look like?
\n\n[00:04:43] What was the journey like from there to now?
\n\n[00:06:39] What were some of the resources you used to help you figure out how to become a better learner?
\n\n[00:08:37] Can you define what deliberate practice means?
\n\n[00:10:18] How do we apply the concept?
\n\n[00:15:23] How would you define a mental model?
\n\n[00:18:44] Which mental model would you say has had the biggest impact on the way you see the world?
\n\n[00:21:27] What does the problem-centric approach mean?
\n\n[00:25:47] Jeff talks about his secrets to getting multiple job offers.
\n\n[00:28:51] Did you ever feel emotionally invested in one given prospect?
\n\n[00:31:19] What would you say to someone scared of applying to job descriptions that look like they want the abilities of an entire team?
\n\n[00:33:51] What was common among all these interviews that you went for?
\n\n[00:36:21] What other nuanced things do you think a data scientist should really understand from school?
\n\n[00:37:52] What is your relationship with imposter syndrome like?
\n\n[00:40:15] Any words of encouragement for people trying to come back from an imposter syndrome?
\n\n[00:41:49] Talk to us about the importance of having a portfolio project.
\n\n[00:44:19] What are some of the biggest journalistic mistakes you’ve seen?
\n\n[00:45:21] Can you tell me how you would answer the “Tell me about yourself” question in an interview?
\n\n[00:47:20] If somebody wanted to a project using data from Spotify, what do you think would be a good project idea?
\n\n[00:49:20] How do you use deep learning to help you find dates here?
\n\n[00:51:21] What are some non-obvious skills that Data Scientists are missing that you think they should go and pick up?
\n\n[00:52:45] Do you keep a journal or anything like that?
\n\n[00:54:15] What tips can you share with data scientists for developing their leadership &influence skills?
\n\n[00:58:52] What are some harsh truths about being a data scientist that you want to leave our audience with?
\n\n[01:00:57] It’s 100 years in the future, what do you want to be remembered for?
\n\n[01:03:13] The Random Round.
","summary":"","date_published":"2021-08-27T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/39efdbc5-b8dd-4616-8fe7-16f5c77ffe91.mp3","mime_type":"audio/mpeg","size_in_bytes":100930711,"duration_in_seconds":4204}]},{"id":"edeb6cea-9512-4a8c-8bf3-410b31abb282","title":"Learn How to Use Publicly Available Web Data | Or Lenchner","url":"https://theartistsofdatascience.fireside.fm/or-lenchner","content_text":"Bright Data is a leading publicly available web data platform that enables organizations of all sizes to access data on the internet with complete transparency\n\nLearn more about Bright Data here: https://brightdata.com/\n\nLearn about the company DNA here: https://brightdata.com/dna\n\nFollow Or on LinkedIn: https://www.linkedin.com/in/orlenchner/\n\nOr on Twiter: https://twitter.com/orlench\n\nGet FREE sample datasets here: https://brightdata.com/products/data-sets\n\n[00:01:28] Guest Introduction\n\n[00:03:39] Talk to us about some of the wave you had to surf you had on the ride to here.\n\n[00:06:00] What's the importance that surfing has had in your life?\n\n[00:12:19] \"You don't need to reinvent everything from scratch\"\n\n[00:12:55] How do you develop this elusive skill of product?\n\n[00:18:42] How do you define publicly available data?\n\n[00:27:59] What are some some ways that Bright Data help get transparent view of the web?\n\n[00:30:44] What is alternative data?\n\n[00:33:58] How do you handle situations where people come and want to use data for some sketchy or shady things?\n\n[00:38:41] Are there any major shifts or trends in data collection?\n\n[00:41:07] Do you have any other success story just like with the HTI organization?\n\n[00:42:17] Where can people go to apply for the roles in Bright Data?\n\n[00:49:29] How can the benefits of open data be communicated to new audiences so that government data can be combined with Important privately owned data?\n\n[00:53:31] Would you think it would it would ever happen would ever be the case that we have something similar to GDPR?\n\n[00:54:00] Random Round\n\n[00:54:55] What do you want to be remembered for?\n\n[00:55:31] When do you think the first video to hit one trillion views on YouTube will happen? And what will that video be about?\n\n[00:56:27] So what's your favorite question to ask a candidate during a job interview and why?\n\n[00:57:42] In the data collection process and most of it is manually entered, the quality and consistency of this Data is poor. What are your thoughts? How can we improve this?\n\n[01:00:05] Strategic thinking, the teacher planning, strategic learning, which is most important for you and why?\n\n[01:01:36] What are you currently reading?\n\n[01:03:43] What's the story behind one of your scars?\n\n[01:04:04] What issue will you always speak your mind about?\n\n[01:04:26] Best piece of advice you've ever received?\n\ndatascience #machinelearning #ai #data #analytics #dataanalytics #mlops #artificialintelligence\n\ncommunity #mindset #philosophy #success\n\nhttps://www.instagram.com/theartistsofdatascience/\nhttps://facebook.com/TheArtistsOfDataScience\nhttps://twitter.com/ArtistsOfData","content_html":"Bright Data is a leading publicly available web data platform that enables organizations of all sizes to access data on the internet with complete transparency
\n\nLearn more about Bright Data here: https://brightdata.com/
\n\nLearn about the company DNA here: https://brightdata.com/dna
\n\nFollow Or on LinkedIn: https://www.linkedin.com/in/orlenchner/
\n\nOr on Twiter: https://twitter.com/orlench
\n\nGet FREE sample datasets here: https://brightdata.com/products/data-sets
\n\n[00:01:28] Guest Introduction
\n\n[00:03:39] Talk to us about some of the wave you had to surf you had on the ride to here.
\n\n[00:06:00] What's the importance that surfing has had in your life?
\n\n[00:12:19] "You don't need to reinvent everything from scratch"
\n\n[00:12:55] How do you develop this elusive skill of product?
\n\n[00:18:42] How do you define publicly available data?
\n\n[00:27:59] What are some some ways that Bright Data help get transparent view of the web?
\n\n[00:30:44] What is alternative data?
\n\n[00:33:58] How do you handle situations where people come and want to use data for some sketchy or shady things?
\n\n[00:38:41] Are there any major shifts or trends in data collection?
\n\n[00:41:07] Do you have any other success story just like with the HTI organization?
\n\n[00:42:17] Where can people go to apply for the roles in Bright Data?
\n\n[00:49:29] How can the benefits of open data be communicated to new audiences so that government data can be combined with Important privately owned data?
\n\n[00:53:31] Would you think it would it would ever happen would ever be the case that we have something similar to GDPR?
\n\n[00:54:00] Random Round
\n\n[00:54:55] What do you want to be remembered for?
\n\n[00:55:31] When do you think the first video to hit one trillion views on YouTube will happen? And what will that video be about?
\n\n[00:56:27] So what's your favorite question to ask a candidate during a job interview and why?
\n\n[00:57:42] In the data collection process and most of it is manually entered, the quality and consistency of this Data is poor. What are your thoughts? How can we improve this?
\n\n[01:00:05] Strategic thinking, the teacher planning, strategic learning, which is most important for you and why?
\n\n[01:01:36] What are you currently reading?
\n\n[01:03:43] What's the story behind one of your scars?
\n\n[01:04:04] What issue will you always speak your mind about?
\n\n[01:04:26] Best piece of advice you've ever received?
\n\nhttps://www.instagram.com/theartistsofdatascience/
\nhttps://facebook.com/TheArtistsOfDataScience
\nhttps://twitter.com/ArtistsOfData
Vote in the data community content creators awards! http://bit.ly/data-creators-awards
\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh
\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\n\nThe Artists of Data Science YouTube: https://www.youtube.com/c/TheArtistsofDataScience
\n\nhttps://www.instagram.com/theartistsofdatascience/
\nhttps://facebook.com/TheArtistsOfDataScience
\nhttps://twitter.com/ArtistsOfData
Find David Benjamin online:
\n\nTwitter: https://twitter.com/complexitydb
\nLinkedin: https://www.linkedin.com/in/davidbenjaminsyntegrity/
Memorable Quotes from the episode:
\n\n[00:14:17] "Every time you hit something complex, it's new, it's different. There is no playbook, there is no checklist. And really what you have to do is get all the right people involved in sort of sharing what they see. No believe, understand, get them buying into the right way to to move forward. And the last thing I'll say is, you know, the right way to move forward when it's complex is to try things."
\n\n[00:17:41] "...you start to see that really the key to dealing with complexity is to get people get their fingerprints all over the solution, get their fingerprints all over the strategy. And I can't imagine a situation where doing Data strategy is only complicated because, again, the market, the business you're in, the people who work there, you know, it's going to be different every time. And of course, are you going on and on."
\n\nHighlights of the show:
\n\n[00:01:29] Guest Introduction
\n\n[00:02:53] Where did you grow up and what was it like there?
\n\n[00:03:31] What did you imagine your future would be like?
\n\n[00:05:07] What types of things were you doing that then eventually led to to this being the thing that you chose to pursue?
\n\n[00:06:22] What does it mean to be a systems thinker?
\n\n[00:12:36] What's your favorite example of a problem that on the surface looks like it fits the description of complicated, but as you start to dig a little bit deeper, it turns out that it's actually complex.
\n\n[00:14:07] How is the problem solving process different for a complicated versus a complex problem?
\n\n[00:17:38] The nature of complexity.
\n\n[00:19:39] How do we best deal with that? How do we best deal with something we've never dealt with before without knowing what's going to work and what's not going to work?
\n\n[00:24:40] What is DIKW model?
\n\n[00:32:53] Letting go is a huge part of good leadership.
\n\n[00:37:57] What are some questions that we can ask ourselves so that we can find a way forward in these types of situations?
\n\n[00:39:57] Can we inject requisite variety into our world in different ways?
\n\n[00:48:00] What's the number one book of Stafford Lehr that myself and the audience would really benefit from his thoughts?
\n\n[00:48:55] What's the difference between constructing a good question and asking a question?
\n\n[00:51:39] What are rules of queueing as you've discussed in your book?
\n\n[00:53:18] How do faulty assumptions make us ask bad questions?
\n\n[00:55:33] Why is it that when we are having these face to face types of interactions and discussions that we're able to create complexity?
\n\n[00:57:27] Does that have an effect on anything when we're working together?
\n\n[00:59:31] Is cracking complexity, an art or a science?
\n\n[01:01:33] How do we create serendipity when we're working on complex problems and maybe when we're working isolated from people?
\n\n[01:04:34] RANDON ROUND
\n\n[01:04:49] It's one hundred years in the future. What do you want to be remembered for?
\n\n[01:08:23] What do most people think within the first few seconds when they meet you for the first time?
\n\n[01:09:09] Do you think you have to achieve something in order to be worth something?
\n\n[01:09:59] What are you currently reading?
\n\n[01:11:39] What song do you have on repeat?
\n\n[01:12:28] What's the story behind one of your scars?
\n\n[01:13:02] What is one of the greatest values that guides your life?
\n\n[01:13:45] If you could have any superpower, what would it be and why?
","summary":"in this episode David Benjamin talks about ","date_published":"2021-08-20T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/18b1e43e-5c2d-4397-9ad8-91a11b7dafb5.mp3","mime_type":"audio/mpeg","size_in_bytes":91044570,"duration_in_seconds":4551}]},{"id":"c32fa511-71c3-49e9-bd4d-8bbc09ade031","title":"Comet ML Office Hour 26 | 15AUG2021","url":"https://theartistsofdatascience.fireside.fm/comet-ml-26","content_text":"Vote in the data community content creators awards! http://bit.ly/data-creators-awards\nCheckout Jonathan Tesser's episode here: https://theartistsofdatascience.fireside.fm/jonathan-tesser\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh\n\nYouTube: https://www.youtube.com/c/TheArtistsofDataScience\nInstagram: https://www.instagram.com/theartistsofdatascience/\nFacebook: https://facebook.com/TheArtistsOfDataScience\nTwitter: https://twitter.com/ArtistsOfData","content_html":"Vote in the data community content creators awards! http://bit.ly/data-creators-awards
\nCheckout Jonathan Tesser's episode here: https://theartistsofdatascience.fireside.fm/jonathan-tesser
\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh
Register for Sunday Sessions here: http://bit.ly/comet-ml-oh
\n\nYouTube: https://www.youtube.com/c/TheArtistsofDataScience
\nInstagram: https://www.instagram.com/theartistsofdatascience/
\nFacebook: https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/ArtistsOfData
Vote in the data community content creators awards! http://bit.ly/data-creators-awards
\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh
\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\n\nThe Artists of Data Science YouTube: https://www.youtube.com/c/TheArtistsofDataScience
\n\nhttps://www.instagram.com/theartistsofdatascience/
\nhttps://facebook.com/TheArtistsOfDataScience
\nhttps://twitter.com/ArtistsOfData
Memorable Quotes from the Episode:
\n\n[00:13:04] "When I was doing martial arts, I came up with a real clear understanding of what it takes to succeed, because if you're in a fight, you better be engaged in that fight. Otherwise you're going to get broken bones or worse, you know. So what I found first and foremost with martial arts, it's a meditation."
\n\n[00:21:54] "... Fear is based on what happened in the past, might also be happening in the future when, in fact, we are we're in a position right now to change it. So if you're self-aware and you know that to be true and by the way, fear, if you don't have any empirical evidence, is just a story."
\n\n[00:36:57] "A lot of people get caught up in their competency. Can I trust that I'm competent enough to be able to do that? How many people play small when they have a whole lot more to offer because they don't trust themselves and then the trusting of others? If you're in leadership, you want to build trust with your team members. You've got to be able to trust them that, you know, they're going to say what they'll do, what they say they will do. You gotta trust that they're working in your own or your best interest, as well as their own best interests, and of course, that applies to you, too..."
\n\n[00:45:08] "By the way, when you hear a lot of negative voices, they're not our voices. They were planted there originally by somebody else who in some way or another told you you weren't good enough. Whether it's a parent or a teacher or a boss doesn't matter. You carry that message with yourself."
\n\nHighlights of the Show:
\n\n[00:01:18] Guest Introduction
\n\n[00:02:34] Where did you grow up and what was it like there?
\n\n[00:07:41] What did you think your future would would be?
\n\n[00:14:39] Struggling with follow through and consistency, what do we have to do to overcome that?
\n\n[00:17:02] What's the first thing you see every morning? Would that constitute a system?
\n\n[00:19:42] Systems, they're not like pulleys and levers and crazy things. It's simple. You can make it easy for yourself.
\n\n[00:20:24] What self-awareness is and why is that so important as the first step in any change process?
\n\n[00:24:09] Knowing your name and social security number doesn't mean you are self aware.
\n\n[00:25:31] "...stay open and curious and stay out of judgment."
\n\n[00:30:46] Four types of characters of self-awareness.
\n\n[00:46:29] How can we wrestle the fear?
\n\n[00:53:41] Do we need to prove to ourselves that we're good enough?
\n\n[00:57:27] "The privilege of a lifetime is being who you are."
\n\n[01:01:48] RANDOM ROUND
\n\n[01:01:51] What do you want to be remembered for?
\n\n[01:02:00] What do most people think within the first few seconds when they meet you for the first time?
\n\n[01:03:15] Do you think you have to achieve something in order to be worth something?
\n\n[01:03:57] What are you currently reading?
\n\n[01:05:17] What song do you have on repeat?
\n\n[01:06:03] What is one of the great values that guides your life?
\n\n[01:06:30] What story does your family always tell about you?
\n\n[01:06:50] What's the best thing you got from one of your parents?
\n\n[01:07:04] What's your favorite book?
","summary":"","date_published":"2021-08-13T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/19682979-75d9-444a-bfaf-38222fb61290.mp3","mime_type":"audio/mpeg","size_in_bytes":100252560,"duration_in_seconds":4175}]},{"id":"7378054a-284f-4788-bbd7-f8e1c70000f2","title":"Comet ML Office Hour 25 | 08AUG2021","url":"https://theartistsofdatascience.fireside.fm/comet-ml-25","content_text":"Vote in the data community content creators awards! http://bit.ly/data-creators-awards\nCheckout Jonathan Tesser's episode here: https://theartistsofdatascience.fireside.fm/jonathan-tesser\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh\n\nYouTube: https://www.youtube.com/c/TheArtistsofDataScience\nInstagram: https://www.instagram.com/theartistsofdatascience/\nFacebook: https://facebook.com/TheArtistsOfDataScience\nTwitter: https://twitter.com/ArtistsOfData","content_html":"Vote in the data community content creators awards! http://bit.ly/data-creators-awards
\nCheckout Jonathan Tesser's episode here: https://theartistsofdatascience.fireside.fm/jonathan-tesser
\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh
Register for Sunday Sessions here: http://bit.ly/comet-ml-oh
\n\nYouTube: https://www.youtube.com/c/TheArtistsofDataScience
\nInstagram: https://www.instagram.com/theartistsofdatascience/
\nFacebook: https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/ArtistsOfData
Vote in the data community content creators awards! http://bit.ly/data-creators-awards
\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh
\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\n\nThe Artists of Data Science YouTube: https://www.youtube.com/c/TheArtistsofDataScience
\n\nhttps://www.instagram.com/theartistsofdatascience/
\nhttps://facebook.com/TheArtistsOfDataScience
\nhttps://twitter.com/ArtistsOfData
Memorable Quotes from the episode:
\n\n[00:21:08] "...I would give and I would give and I would give and I would help. And those people would just leave my life like they got what they needed from me and they left. And it created this idea that the human experience isn't such a wonderful thing. You have to you have to force people to be. It sounds awful, but you really have to put them on the spot and say, if you want to stick around, I'm going to show you what it means to stick around."
\n\n[00:24:37] "...mentorship for me just means how can I grow? How can I grow as a person? How can I become better? How can I how can this personal development hamster wheel continue to turn in a positive direction? And so each person I talked to, I say to them, I'm like, you are my inspiration."
\n\nHighlights of the episode:
\n\n[00:01:00] Guest Introduction
\n\n[00:03:07] Realizing that Jonathan is a data guy!
\n\n[00:09:11] Should it be something that we strongly have an opinion about or should we just do it for the audience out there?
\n\n[00:11:43] Where in the agreement when you sign up for LinkedIn, does it say don't talk about personal life?
\n\n[00:23:32] It's hard to give advice to people when you don't know them.
\n\n[00:24:29] What are some good ways find a good mentor?
\n\n[00:33:17] Ninety five percent of people don't want to put the work in.
\n\n[00:33:27] How do you deal with the notion of not really interacting with more people?
\n\n[00:35:22] Can selfishness be a virtue?
\n\n[00:37:34] What are some other self skills that highly analytical people are missing?
\n\n[00:42:32] RANDOM ROUND.
\n\n[00:42:56] It's one hundred years in the future. What do you want to be remembered for?
\n\n[00:43:32] When do you think the first video to hit one trillion views on YouTube will happen, and what will that video be about?
\n\n[00:44:33] Do you think you have to achieve something in order to be worth something?
\n\n[00:45:45] What are you currently reading?
\n\n[00:47:09] What song differently have on repeat?
\n\n[00:47:45] What dumb accomplishment are you most proud of?
\n\n[00:48:19] What makes you cry?
\n\n[00:48:35] What's your favorite Candy?
","summary":"","date_published":"2021-08-06T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/a163eb10-0217-474b-b863-ef1758c9d1b9.mp3","mime_type":"audio/mpeg","size_in_bytes":71921800,"duration_in_seconds":2995}]},{"id":"4ea498be-6037-48e9-96b0-e09d1b5afef8","title":"Comet ML Office Hour 24 | 01AUG2021","url":"https://theartistsofdatascience.fireside.fm/comet-ml-24","content_text":"Vote in the data community content creators awards! http://bit.ly/data-creators-awards\nCheckout Lillian Pierson's episode here: https://theartistsofdatascience.fireside.fm/lillian-pierson\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh\n\nYouTube: https://www.youtube.com/c/TheArtistsofDataScience\nInstagram: https://www.instagram.com/theartistsofdatascience/\nFacebook: https://facebook.com/TheArtistsOfDataScience\nTwitter: https://twitter.com/ArtistsOfData","content_html":"Vote in the data community content creators awards! http://bit.ly/data-creators-awards
\nCheckout Lillian Pierson's episode here: https://theartistsofdatascience.fireside.fm/lillian-pierson
\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh
Register for Sunday Sessions here: http://bit.ly/comet-ml-oh
\n\nYouTube: https://www.youtube.com/c/TheArtistsofDataScience
\nInstagram: https://www.instagram.com/theartistsofdatascience/
\nFacebook: https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/ArtistsOfData
Vote in the data community content creators awards! http://bit.ly/data-creators-awards
\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh
\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\n\nThe Artists of Data Science YouTube: https://www.youtube.com/c/TheArtistsofDataScience
\n\nhttps://www.instagram.com/theartistsofdatascience/
\nhttps://facebook.com/TheArtistsOfDataScience
\nhttps://twitter.com/ArtistsOfData
MEMORABLE QUOTES FROM EPISODE:
\n\n[00:13:05] "If you're an implementation person and you love implementing, that's awesome because you don't have to do you don't have to learn the people skills, you don't have to become a leader, so on and so forth. And you can still land jobs."
\n\nHIGHLIGHTS FROM THE SHOW:
\n\n[00:01:29] GUEST INTRODUCTION
\n\n[00:39:00] Where'd you grow up and what was it like there?
\n\n[00:04:44] So when you're in high school, what did you think your future would look like?
\n\n[00:12:08] Everybody wants to break into data science but nobody is willing to appreciate.
\n\n[00:18:19] I learned how to do technical strategy.
\n\n[00:22:43] Do you have to know what skill sets you're working with?
\n\n[00:25:40] How can you get more information from your stakeholders?
\n\n[00:27:23] What a day in the life of a Data entrepreneur is like?
\n\n[00:32:11] How are you managing your time on a day to day basis?
\n\n[00:41:48] How is your experience been working with the coach?
\n\n[00:49:50] What do you want to be remembered for 100 years in the future?
\n\n[00:50:24] When do you think the first video to hit one trillion views on YouTube will happen?
\n\n[00:53:11] What are you currently reading?
\n\n[00:57:11] What song are you currently listening to on repeat?
\n\n[01:00:01] Who are some of your heroes?
\n\nThe Artists of Data Science on social media:
\n\nhttps://www.instagram.com/theartistsofdatascience/
\nhttps://facebook.com/TheArtistsOfDataScience
\nhttps://twitter.com/ArtistsOfData
Vote in the data community content creators awards! http://bit.ly/data-creators-awards
\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh
\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\n\nThe Artists of Data Science YouTube: https://www.youtube.com/c/TheArtistsofDataScience
\n\nhttps://www.instagram.com/theartistsofdatascience/
\nhttps://facebook.com/TheArtistsOfDataScience
\nhttps://twitter.com/ArtistsOfData
MEMORABLE QUOTES
\n\n[00:06:46] “If you’re right there when people want something, then you are setting up to really want to do something.”
\n\n[00:25:22] “Entrepreneurship is like staring the abyss and chewing broken glass broken glass.”
\n\n[00:27:09] “It’s the being able to manage your emotions in the context of failure….And that again, the notion of persistence, the ability to fail and keep going, the ability to just reconfigure, to pivot, to go to look for something new, to create not to shut down.”
\n\n[00:29:27] “It seems like entrepreneurship it’s almost like a middle-skill. It’s not just a particular skill. It’s you know, it requires a whole bunch of different skills and requires a high level of Activation energy, being able to lead yourself and pursue.”
\n\n[00:36:21] “ To think about what someone needs, when it hasn’t been completely expressed, takes a tremendous amount of empathy.”
\n\nHIGHLIGHTS FROM THE SHOW
\n\n[00:00:29] Guest Introduction
\n\n[00:03:25] How did you first get introduced to Artificial Intelligence?
\n\n[00:06:39] Have things progressed along as you thought they would?
\n\n[00:11:4] What was the general sentiment about AI back then?
\n\n[00:16:19] Is this idea talk about big representation versus big data?
\n\n[00:19:11] How does trying to predict our precognition work within that context of big representation and networks?
\n\n[00:20:09] How did that lead you down the path of studying billionaires?
\n\n[00:21:50] What led you to study habits of mind in particular?
\n\n[00:23:31] How would you define the concept of habit of mind?
\n\n[00:24:00] What would your definition of an entrepreneur be?
\n\n[00:25:05] Is there a key missing skill to entrepreneurship?
\n\n[00:32:18] When it comes to producers, builders, and sells, what would you say is the biggest point of similarity between these two?
\n\n[00:34:58] In what way would you say these two diverge the most?
\n\n[00:36:05] Define empathetic imagination for us.
\n\n[00:37:33] What is it that allows producers to see what others can’t?
\n\n[00:40:41] What is it that allows them to have their margins of empathetic insights with imagination.
\n\n[00:42:35] Is this concept of diet, beverage, and thinking going down?
\n\n[00:47:41] Talk to us about this concept of duality of time.
\n\n[00:52:06] Would you mind describing what transient hypofrontality is and how we can use it?
\n\n[00:55:21] What is inventive execution and why is it that begins with design?
\n\n[00:59:21] It’s 100 years in the future. What do you want to be remembered for?
\n\n[1:00:07] The Random Round.
","summary":"","date_published":"2021-07-23T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/505e8448-2cff-41e9-bb14-95a50de57215.mp3","mime_type":"audio/mpeg","size_in_bytes":89993596,"duration_in_seconds":3748}]},{"id":"22604433-8b14-47f0-92f8-f071fb73aa5f","title":"Comet ML Office Hour 23 | 18JUL2021","url":"https://theartistsofdatascience.fireside.fm/comet-ml-23","content_text":"Vote in the data community content creators awards! http://bit.ly/data-creators-awards\nCheckout James Altucher episode here: https://theartistsofdatascience.fireside.fm/james-altucher\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh\n\nYouTube: https://www.youtube.com/c/TheArtistsofDataScience\nInstagram: https://www.instagram.com/theartistsofdatascience/\nFacebook: https://facebook.com/TheArtistsOfDataScience\nTwitter: https://twitter.com/ArtistsOfData","content_html":"Vote in the data community content creators awards! http://bit.ly/data-creators-awards
\nCheckout James Altucher episode here: https://theartistsofdatascience.fireside.fm/james-altucher
\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh
Register for Sunday Sessions here: http://bit.ly/comet-ml-oh
\n\nYouTube: https://www.youtube.com/c/TheArtistsofDataScience
\nInstagram: https://www.instagram.com/theartistsofdatascience/
\nFacebook: https://facebook.com/TheArtistsOfDataScience
\nTwitter: https://twitter.com/ArtistsOfData
Vote in the data community content creators awards! http://bit.ly/data-creators-awards
\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh
\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\n\nThe Artists of Data Science YouTube: https://www.youtube.com/c/TheArtistsofDataScience
\n\nhttps://www.instagram.com/theartistsofdatascience/
\nhttps://facebook.com/TheArtistsOfDataScience
\nhttps://twitter.com/ArtistsOfData
MEMORABLE QUOTES
\n\n[00:05:04] “ Luck favors the prepared”
\n\n[00:06:18] “Creativity is a muscle and like any other muscle in the human body, it atrophies without use.”e you’ll get on
\n\n[00:12:34] “Money is just a byproduct of you just doing great things right. If you improve just that one percent of those four dimensions of your life, you'll get one percent better every day.”
\n\n[00:17:45] “ You don’t need to necessarily know in the beginning of something what’s going to be the path to success. That is the whole point of beginning in the first place, it’s that you don’t know yet.”
\n\n[00:18:14] “…that’s the thing, you are always going to learn even if an experiment doesn't work out. You are always going to learn what doesn't work or you are going to learn something new.”
\n\n[00:24:42] “.. And it’s very important now to focus on your own opportunities because nobody else is going to do it for you.”
\n\n[00:28:56] “ Figure out the micro-skills that you are good at and focus on those.”
\n\n[00:34:28]”So I think fear I think actually not only is fear something that is important, but it’s important as a qualifier for doing something. Don’t do something unless you’re afraid of how it’ll turn out.”
\n\n[00:40:40] “…because if you don’t help yourself, you can’t help others.”
\n\n[00:41:43] “ If you are angry all the time, you are not going to really be able to spread a message of love and hope and peace and be able to help people.”
\n\n[01:02:14] “ That's why it's better to be the best in the world at the intersection of two disparate fields than the best in the world at a field that has existed for thousands of years.”
\n\nHIGHLIGHTS FROM THE SHOW
\n\n[00:00:28] Guest introduction.
\n\n[00:04:06] What would you say was your best birthday ever?
\n\n[00:04:45] How do you define luck and what is your relationship with it?
\n\n[00:07:57] How was your inner monologue different in the times that you felt lucky versus the times that you felt unlucky?
\n\n[00:09:58] What book number do you have coming out?
\n\n[00:12:34] Tell us what the ten-thousand-hour rule means and why you are rebelling against it?
\n[00:16:50] When it comes to doing experiments, do we want them towards one direction, or should it be more wide ranging?
[00:17:59] When we’re coming up with experiments, should we try to design them in a way that we make sure we are learning something? Or just try it out to see if works?
\n\n[00:21:09] What would you say is the core message that you have across your books that you want people to take away? and how does this book expand that core message?
\n\n[00:25:29] Do you think the choose yourself era would be possible without the internet?
\n\n[00:27:29] Will you please articulate on your definition of entrepreneurship?
\n\n[00:30:02] What are some of your favorite myths surrounding the entrepreneurs or surrounding the concept of entrepreneurship?
\n\n[00:31:44] Would you say there is a specific personality trait that makes someone complete?
\n\n[00:33:02] How did you get into standup comedy? Or was this just a burning desire that you’ve had for so many years.
\n\n[00:33:05] How do you wrestle with fear so it’s pushing you from behind rather that standing in front of you?
\n\n[00:35:31] How can someone you embrace fear and just go for it?
\n\n[00:38:37] Talk to us about the economies that came before the idea economy and what makes this economy difference from those.
\n\n[00:42:37] What do you feed your idea muscle?
\n\n[00:44:21] How do we have idea sex?
\n\n[00:46:50] So what do you say separates a good idea from a bad idea?
\n\n[00:47:45] How James came onto the show.
\n\n[00:48:51] What stands out as some of your bad ideas from 2020?
\n\n[00:50:17] James tells us about his upcoming book on Amazon with Charlamagne tha god.
\n\n[00:51:14] What’s an idea that when you think back on now, makes you laugh?
\n\n[00:52:45] What would you say are some ideas that an enterprising person could seize as a potential opportunity in this pandemic?
\n\n[00:55:02] James tells us about his Google Principle/Effect.
\n\n[00:56:11] Would you mind talking to us about how you created a software to pick stocks?
\n\n[00:59:29] What is it about finding intersection of ideas that leads to huge value creation for yourself and for society?
\n\n[01:04:55] When does it make sense to accept equity from a company?
\n\n[01:06:39] When we are in a negotiation setting, how do we get our salary or equity shares up to where we want?
\n\n[01:09:19] Are you a fan of business self-help books?
\n\n[01:10:37] It’s 100 years in the future, what do you want to be remembered for?
\n\n[01:11:08] The Random Round.
Special Guest: James Altucher.
","summary":"","date_published":"2021-07-16T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/febbed88-bef8-4764-996b-608423c3ef2e.mp3","mime_type":"audio/mpeg","size_in_bytes":78156087,"duration_in_seconds":4883}]},{"id":"ab7053a3-8352-4bec-9404-7cdb8267bff9","title":"Comet ML Office Hour 22 | 11JUL2021","url":"https://theartistsofdatascience.fireside.fm/comet-ml-22","content_text":"Vote in the data community content creators awards! http://bit.ly/data-creators-awards\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh\n\nThe Artists of Data Science YouTube: https://www.youtube.com/c/TheArtistsofDataScience","content_html":"Vote in the data community content creators awards! http://bit.ly/data-creators-awards
\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh
\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\n\nThe Artists of Data Science YouTube: https://www.youtube.com/c/TheArtistsofDataScience
","summary":"Data Science community in depth discussion about the artificial intelligence and data science. How to become a data scientist. ","date_published":"2021-07-15T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/ab7053a3-8352-4bec-9404-7cdb8267bff9.mp3","mime_type":"audio/mpeg","size_in_bytes":91457827,"duration_in_seconds":3810}]},{"id":"a4ad685b-bd4f-4270-82f5-bab5fa69bdfe","title":"Data Science Happy Hour 40 | 09JUL2021","url":"https://theartistsofdatascience.fireside.fm/hh40","content_text":"Vote in the data community content creators awards! http://bit.ly/data-creators-awards\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh\n\nThe Artists of Data Science YouTube: https://www.youtube.com/c/TheArtistsofDataScience","content_html":"Vote in the data community content creators awards! http://bit.ly/data-creators-awards
\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh
\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\n\nThe Artists of Data Science YouTube: https://www.youtube.com/c/TheArtistsofDataScience
","summary":"Data Science community in depth discussion about the artificial intelligence and data science. How to become a data scientist. ","date_published":"2021-07-11T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/a4ad685b-bd4f-4270-82f5-bab5fa69bdfe.mp3","mime_type":"audio/mpeg","size_in_bytes":87097645,"duration_in_seconds":4354}]},{"id":"dcf08ae9-68c8-4be6-a02c-ad9ca054057e","title":"Learning How To Learn | Barbara Oakley","url":"https://theartistsofdatascience.fireside.fm/barbara-oakley","content_text":"Barbara's published in outlets as varied as the Proceedings of the National Academy of Sciences, the Wall Street Journal, and The New York Times. She’s also authored 10 books on topics ranging from the negative aspects of altruism to career development in bioengineering.\n\nIf you’re one of the 2.7 million people who have taken her course on Coursera, you might recognize her as the instructor of the world’s most popular online course: Learning How To Learn: Powerful mental tools to help you master tough subjects.\n\nMEMORABLE QUOTES \n\n[00:09:44] “Lady luck favors those who try.”\n\n[00:11:02] “Sometimes people are so agonizing about which problems they should study intensively that they don’t do any of it. And so what I’m trying to encourage people to do is to just start to speak of a problem that feels like it might be an important one so that you are not memorizing it instead of you.”\n\n[00:16:06] “I think being willing to go back to first principles and think independently and work in a corner all by yourself but still be willing to have people look at what you have come up with because they can shoot legitimate holes and things.”\n\n[00:33:54] “… I think that’s the kind of deep mastery that occurs when you’ve learned both ways really well because sometimes stuff will just come to you quickly.”\n\n[00:37:33] “… well, you can’t get from there to here, if you are really focusing on this one little area, you are stuck. And the only way or the best way to get yourself unstuck is to get your focus off what you’re doing.”\n\n[00:45:16] “…I think there’s this ying and yang of going deep into work and not being distracted,\n is really important but also being aware that sometimes it’s OK to be distracted.”\n\nHIGHLIGHTS FROM THE SHOW \n\n[00:00:38] Guest Introduction.\n\n[00:03:22] What type of kid were you in high school. What did you think your future was going to look like when you were in high school?\n\n[00:07:53] What was it like, forcing yourself to get better at math and learning, convincing yourself that she can learn it?\n\n[00:12:10] what would you say your relationship with luck is? And how have you managed to create your own luck in life?\n\n[00:18:21] What role do self image and self perception play when we are trying to learn something or even advancing in our own field?\n\n[00:21:39] Do you count specialization the same as being pigeonholed?\n\n[00:27:15]Barbara asks Harpreet if he’s thought about writing a book.\n\n[00:28:44] How have you used transfer to your advantage during your varied career?\n\n[00:35:10] Can you describe the focus and diffuse mode of thinking and how they work together to help us solve problems?\n\n[00:42:43] How do you know it’s time to get a diffuse mode break?\n\n[00:47:47] What are the differences between the default mode network and the reticular activating system? And what effects do they have on creativity?\n\n[00:53:26] What would you say is the difference between Art and Science?\n\n[00:55:39] What role does creativity play in Science?\n\n[01:00:28] How do you find out what it is that you are actually creative at? What can we do to help us recognize that?\n\n[01:02:11] What is the relationship between emotional state and our capacity to learn things?\n\n[01:08:04] Doesn’t your brain make you think how you can change your brain by thinking?\n\n[01:10:51] What implication does this have for the way we talk to ourselves, our inner monologue, and our inability to think?\n[01:12:08] Is there a relationship between your self talk and your particular activating system or even your default mode network?\n\n[01:18:43] Barbara tells about the latest book that she’s reading and enjoying.\n\n[01:19:26] It’s 100 years in the future, what do you want to be remembered for?\n\n[01:19:53] The Random RoundSpecial Guest: Barbara Oakley.","content_html":"Barbara's published in outlets as varied as the Proceedings of the National Academy of Sciences, the Wall Street Journal, and The New York Times. She’s also authored 10 books on topics ranging from the negative aspects of altruism to career development in bioengineering.
\n\nIf you’re one of the 2.7 million people who have taken her course on Coursera, you might recognize her as the instructor of the world’s most popular online course: Learning How To Learn: Powerful mental tools to help you master tough subjects.
\n\nMEMORABLE QUOTES
\n\n[00:09:44] “Lady luck favors those who try.”
\n\n[00:11:02] “Sometimes people are so agonizing about which problems they should study intensively that they don’t do any of it. And so what I’m trying to encourage people to do is to just start to speak of a problem that feels like it might be an important one so that you are not memorizing it instead of you.”
\n\n[00:16:06] “I think being willing to go back to first principles and think independently and work in a corner all by yourself but still be willing to have people look at what you have come up with because they can shoot legitimate holes and things.”
\n\n[00:33:54] “… I think that’s the kind of deep mastery that occurs when you’ve learned both ways really well because sometimes stuff will just come to you quickly.”
\n\n[00:37:33] “… well, you can’t get from there to here, if you are really focusing on this one little area, you are stuck. And the only way or the best way to get yourself unstuck is to get your focus off what you’re doing.”
\n\n[00:45:16] “…I think there’s this ying and yang of going deep into work and not being distracted,
\n is really important but also being aware that sometimes it’s OK to be distracted.”
HIGHLIGHTS FROM THE SHOW
\n\n[00:00:38] Guest Introduction.
\n\n[00:03:22] What type of kid were you in high school. What did you think your future was going to look like when you were in high school?
\n\n[00:07:53] What was it like, forcing yourself to get better at math and learning, convincing yourself that she can learn it?
\n\n[00:12:10] what would you say your relationship with luck is? And how have you managed to create your own luck in life?
\n\n[00:18:21] What role do self image and self perception play when we are trying to learn something or even advancing in our own field?
\n\n[00:21:39] Do you count specialization the same as being pigeonholed?
\n\n[00:27:15]Barbara asks Harpreet if he’s thought about writing a book.
\n\n[00:28:44] How have you used transfer to your advantage during your varied career?
\n\n[00:35:10] Can you describe the focus and diffuse mode of thinking and how they work together to help us solve problems?
\n\n[00:42:43] How do you know it’s time to get a diffuse mode break?
\n\n[00:47:47] What are the differences between the default mode network and the reticular activating system? And what effects do they have on creativity?
\n\n[00:53:26] What would you say is the difference between Art and Science?
\n\n[00:55:39] What role does creativity play in Science?
\n\n[01:00:28] How do you find out what it is that you are actually creative at? What can we do to help us recognize that?
\n\n[01:02:11] What is the relationship between emotional state and our capacity to learn things?
\n\n[01:08:04] Doesn’t your brain make you think how you can change your brain by thinking?
\n\n[01:10:51] What implication does this have for the way we talk to ourselves, our inner monologue, and our inability to think?
\n[01:12:08] Is there a relationship between your self talk and your particular activating system or even your default mode network?
[01:18:43] Barbara tells about the latest book that she’s reading and enjoying.
\n\n[01:19:26] It’s 100 years in the future, what do you want to be remembered for?
\n\n[01:19:53] The Random Round
Special Guest: Barbara Oakley.
","summary":"","date_published":"2021-07-09T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/dcf08ae9-68c8-4be6-a02c-ad9ca054057e.mp3","mime_type":"audio/mpeg","size_in_bytes":87812343,"duration_in_seconds":5487}]},{"id":"6b5c8fbb-c576-46a2-a95f-f5c2ec15a950","title":"Comet ML Office Hour 21 | 04JUL2021","url":"https://theartistsofdatascience.fireside.fm/comet-ml-21","content_text":"Vote in the data community content creators awards! http://bit.ly/data-creators-awards\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh\n\nThe Artists of Data Science YouTube: https://www.youtube.com/c/TheArtistsofDataScience","content_html":"Vote in the data community content creators awards! http://bit.ly/data-creators-awards
\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh
\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\n\nThe Artists of Data Science YouTube: https://www.youtube.com/c/TheArtistsofDataScience
","summary":"Data Science community in depth discussion about the artificial intelligence and data science. How to become a data scientist. ","date_published":"2021-07-08T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/6b5c8fbb-c576-46a2-a95f-f5c2ec15a950.mp3","mime_type":"audio/mpeg","size_in_bytes":85291035,"duration_in_seconds":3553}]},{"id":"137d52fa-2878-4438-9660-10af684cd62b","title":"Data Science Happy Hour 39 | 02JUL2021","url":"https://theartistsofdatascience.fireside.fm/hh39","content_text":"Get 70% off of Data Science Dream Job today! http://dsdj.co/artists70\n\nVote in the data community content creators awards! http://bit.ly/data-creators-awards\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh\n\nThe Artists of Data Science YouTube: https://www.youtube.com/c/TheArtistsofDataScience","content_html":"Get 70% off of Data Science Dream Job today! http://dsdj.co/artists70
\n\nVote in the data community content creators awards! http://bit.ly/data-creators-awards
\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh
\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\n\nThe Artists of Data Science YouTube: https://www.youtube.com/c/TheArtistsofDataScience
","summary":"Data Science community in depth discussion about the artificial intelligence and data science. How to become a data scientist. ","date_published":"2021-07-04T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/137d52fa-2878-4438-9660-10af684cd62b.mp3","mime_type":"audio/mpeg","size_in_bytes":69891987,"duration_in_seconds":4367}]},{"id":"3108c751-efb7-476a-8b48-2d139da72012","title":"The Shape of Geometry | Jordan Ellenberg","url":"https://theartistsofdatascience.fireside.fm/jordan-ellenberg","content_text":"MEMORABLE QUOTES FROM THE EPISODE:\n\n[00:29:37] \"The coin flips aren't influencing each other. Without that assumption, the law of large numbers might not be true.\"\n\n[00:13:01] \"...mathematics is the art of giving the same name to different things, which is an amazing insight, because it's so true to what we are doing in mathematics that, you know, on a very basic level, we use the fact that a triangle over here and the same triangle over here have the same properties we don't really worry about.\"\n\n[00:16:52] \"...it means constructing this classical Greek way where there's only two tools you're allowed to use a compass and a straight edge. So it allows you to draw a straight line between two points or something that allows you to draw circles.\"\n\nJordan Ellengberg's twitter: https://twitter.com/JSEllenberg\n\nHIGHLIGHTS FROM THE SHOW\n\n[00:01:19] Guest Introduction\n\n[00:03:02] Where you grew up and what it was like there?\n\n[00:03:58] What were you like in high school?\n\n[00:04:49] Jordan talks about geometry\n\n[00:05:24] Talks about being a biostatistician\n\n[00:08:10] Even William Wordsworth cared about math\n\n[00:10:35] Modern translation of Euclid's work\n\n[00:12:32] How is a circle the same as a square in topology?\n\n[00:25:32] What what does the law of large numbers have to do with free will?\n\n[00:32:20] Did you start to look at the history of the ideas by writing books, or was this something that you're always interested in?\n\n[00:40:26] Is language markov chain?\n\n[00:50:09] Relationship between the Golden Ratio and eigenvalues\n\n[00:57:14] What does Galileo knew about a thrown object?\n\n[01:01:28] RANDOM ROUND\n\n[01:02:13] What are you currently reading?\n\n[01:02:40] What song do you have on repeat?\n\n[01:03:16] What is your theme song?\n\n[01:04:01] What is something you can never seem to finish?\n\n[01:04:27] What's one place you have traveled to that you never want to go back to?\n\n[01:04:45] To the last one from here. What's the worst movie you've ever seen?\n\n[01:05:02] What's your favorite candy.\n\n[01:05:14] How can people connect with you and where can we find you online?Special Guest: Jordan Ellenberg.","content_html":"MEMORABLE QUOTES FROM THE EPISODE:
\n\n[00:29:37] "The coin flips aren't influencing each other. Without that assumption, the law of large numbers might not be true."
\n\n[00:13:01] "...mathematics is the art of giving the same name to different things, which is an amazing insight, because it's so true to what we are doing in mathematics that, you know, on a very basic level, we use the fact that a triangle over here and the same triangle over here have the same properties we don't really worry about."
\n\n[00:16:52] "...it means constructing this classical Greek way where there's only two tools you're allowed to use a compass and a straight edge. So it allows you to draw a straight line between two points or something that allows you to draw circles."
\n\nJordan Ellengberg's twitter: https://twitter.com/JSEllenberg
\n\nHIGHLIGHTS FROM THE SHOW
\n\n[00:01:19] Guest Introduction
\n\n[00:03:02] Where you grew up and what it was like there?
\n\n[00:03:58] What were you like in high school?
\n\n[00:04:49] Jordan talks about geometry
\n\n[00:05:24] Talks about being a biostatistician
\n\n[00:08:10] Even William Wordsworth cared about math
\n\n[00:10:35] Modern translation of Euclid's work
\n\n[00:12:32] How is a circle the same as a square in topology?
\n\n[00:25:32] What what does the law of large numbers have to do with free will?
\n\n[00:32:20] Did you start to look at the history of the ideas by writing books, or was this something that you're always interested in?
\n\n[00:40:26] Is language markov chain?
\n\n[00:50:09] Relationship between the Golden Ratio and eigenvalues
\n\n[00:57:14] What does Galileo knew about a thrown object?
\n\n[01:01:28] RANDOM ROUND
\n\n[01:02:13] What are you currently reading?
\n\n[01:02:40] What song do you have on repeat?
\n\n[01:03:16] What is your theme song?
\n\n[01:04:01] What is something you can never seem to finish?
\n\n[01:04:27] What's one place you have traveled to that you never want to go back to?
\n\n[01:04:45] To the last one from here. What's the worst movie you've ever seen?
\n\n[01:05:02] What's your favorite candy.
\n\n[01:05:14] How can people connect with you and where can we find you online?
Special Guest: Jordan Ellenberg.
","summary":"Jordan Ellenberg talks about his journey in geometry and its relation to data science and ai.","date_published":"2021-07-02T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/3108c751-efb7-476a-8b48-2d139da72012.mp3","mime_type":"audio/mpeg","size_in_bytes":95607763,"duration_in_seconds":3982}]},{"id":"a152eca0-d3dd-40f5-96aa-83b9ff60592a","title":"Comet ML Office Hour 20 | 27JUN2021","url":"https://theartistsofdatascience.fireside.fm/comet-ml-20","content_text":"Get 70% off of Data Science Dream Job today! http://dsdj.co/artists70\n\nVote in the data community content creators awards! http://bit.ly/data-creators-awards\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh\n\nThe Artists of Data Science YouTube: https://www.youtube.com/c/TheArtistsofDataScience","content_html":"Get 70% off of Data Science Dream Job today! http://dsdj.co/artists70
\n\nVote in the data community content creators awards! http://bit.ly/data-creators-awards
\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh
\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\n\nThe Artists of Data Science YouTube: https://www.youtube.com/c/TheArtistsofDataScience
","summary":"Data Science community in depth discussion about the artificial intelligence and data science. How to become a data scientist. ","date_published":"2021-06-30T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/a152eca0-d3dd-40f5-96aa-83b9ff60592a.mp3","mime_type":"audio/mpeg","size_in_bytes":89682230,"duration_in_seconds":3736}]},{"id":"02279213-d6ed-4a4d-9179-d360ce210966","title":"Data Science Happy Hour 38 | 25JUN2021","url":"https://theartistsofdatascience.fireside.fm/oh38","content_text":"Get 70% off of Data Science Dream Job today! http://dsdj.co/artists70\n\nVote in the data community content creators awards! http://bit.ly/data-creators-awards\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh\n\nThe Artists of Data Science YouTube: https://www.youtube.com/c/TheArtistsofDataScience","content_html":"Get 70% off of Data Science Dream Job today! http://dsdj.co/artists70
\n\nVote in the data community content creators awards! http://bit.ly/data-creators-awards
\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh
\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\n\nThe Artists of Data Science YouTube: https://www.youtube.com/c/TheArtistsofDataScience
","summary":"Data Science community in depth discussion about the artificial intelligence and data science. How to become a data scientist. ","date_published":"2021-06-27T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/02279213-d6ed-4a4d-9179-d360ce210966.mp3","mime_type":"audio/mpeg","size_in_bytes":87671609,"duration_in_seconds":4383}]},{"id":"53afb36a-c827-4339-a665-7077cb300c3e","title":"Our Nearest Neighbour | Ken Jee","url":"https://theartistsofdatascience.fireside.fm/ken-jee","content_text":"You may recognize Ken from YouTube - where he’s amassed 140,000+ subscribers and over 3 million video views.\n\nMEMORABLE QUOTES \n\n[00:07:21] “… I think some people will think That’s just because they have those qualities, that they remain fixed, that they are unable to change these aspects about themselves, that reinvention is not something possible for them. So they are just going to continue on this path and just continue living up to whatever box they’ve kind of put themselves in.”\n\n[00:08:11] “ If I set out everything that I’m going to do and make it as easy for myself to succeed as possible, I’m likely going to find more success than if I just started with everything jumbled.”\n\n[00:10:21] “… because you’re putting on that different character. You’re becoming that other person. You’re becoming who you’d like to be, who the last person was that wasn’t living up to standards, whatever that might be.”\n\n[00:11:03] “ You can choose to remove that belief system, install a new one and reinvent yourself that way and just kind of reinvent the way you think about things.”\n\n[00:11:57] “ I think intelligence is a function of a lot of things and that’s not something we should really care about… You can be successful without being too bright. You just have to understand how systems work, whatever they might be.”\n\n[00:12:56] “Understanding that you can do something, believing that you are capable of growth or change over time and eventually, tackling those things head on and proving to yourself that you can do those things.”\n\n[00:12:58] “ it’s kind of a step process, you’ve got to believe, you’ve got to see some success and then you’ve got to just run with it as much as you can over time.”\n\n[00:37:54] “ Curiosity is what encourages you to keep learning new skills which you are inevitably going to need in this profession.”\n\n[00:44:00] “ Humans are still some of the most powerful computers walking around, know we are incredibly attuned to in particular speech or some of these different systems and we can still make good decisions beyond what a computer can do, if we are trained properly.”\n\nHIGHLIGHTS FROM THE SHOW \n\n[00:00:48] Guest Introduction \n\n[00:03:13] Ken tells us about the Silver Plaque button he got for 100,000 YouTube subscribers\n\n[00:04:54] Talk to us about where you grew up and what it was like there\n\n[00:07:58] What would you say if given the opportunity to help somebody who sees things differently?\n\n[00:11:21] Have you ever had to stay put without having to really change your surroundings but just the internal stuff?\n\n[00:13:13] How you can reinvent yourself through belief systems\n\n[00:13:51] What Ken think about luck and it’s interpretation \n\n[00:16:02] Are you right now, curious or interested in anything?\n\n[00:19:23] How did you find yourself in the data world?\n\n[00:21:57] Do you think you need graduate training to become a data scientist?\n\n[00:24:03] As an educator, in 10 to 15 years, do you think the education “ ecosystem “ is going to be changed? Do you think college is going to be something you need to do?\n\n[00:27:55] Why Ken thinks Schooling breeds interpersonal skills.\n\n[00:29:29] Why 66days? of Data Science Science Challenge and what do you think were some of the hardest things to unlearn in that process?\n\n[00:33:09] What would you say was your least favorite thing to relearn about Data Science?\n\n[00:34:23] In the process of learning new habits, what is the one habit you decided not to pick up because you found it irrelevant to the world of data science?\n\n[00:36:08] When you are breaking down large problems into chunks, do you have a systematic way to do it? Or does it vary with every problem you are facing at the time?\n\n[00:36:41] What is your favorite question that people ask you about breaking into data science?\n\n[00:37:47] What do you think is the most underrated skill that a data scientist can have?\n\n[00:40:45] When it comes to projects, what do you think is more important? Content or Process?\n\n[00:43:40] What do you think the biggest misconception data scientists have about data science is?\n\n[00:45:40] What would you say is the biggest misconception that aspiring data scientists have about data science.\n\n[00:46:46] When someone says the bigger picture, what does it mean to you?\n\n[00:48:49] How do you decide what next you’ll spend time learning or developing?\n\n[00: 52:53] Tell us about your Ken’s Nearest Neighbor’s podcast\n\n[00:56:35] it’s 100 years in the future. What do you want to be remembered for?\n\n[00:57:41] The Random Round Special Guest: Ken Jee.","content_html":"You may recognize Ken from YouTube - where he’s amassed 140,000+ subscribers and over 3 million video views.
\n\nMEMORABLE QUOTES
\n\n[00:07:21] “… I think some people will think That’s just because they have those qualities, that they remain fixed, that they are unable to change these aspects about themselves, that reinvention is not something possible for them. So they are just going to continue on this path and just continue living up to whatever box they’ve kind of put themselves in.”
\n\n[00:08:11] “ If I set out everything that I’m going to do and make it as easy for myself to succeed as possible, I’m likely going to find more success than if I just started with everything jumbled.”
\n\n[00:10:21] “… because you’re putting on that different character. You’re becoming that other person. You’re becoming who you’d like to be, who the last person was that wasn’t living up to standards, whatever that might be.”
\n\n[00:11:03] “ You can choose to remove that belief system, install a new one and reinvent yourself that way and just kind of reinvent the way you think about things.”
\n\n[00:11:57] “ I think intelligence is a function of a lot of things and that’s not something we should really care about… You can be successful without being too bright. You just have to understand how systems work, whatever they might be.”
\n\n[00:12:56] “Understanding that you can do something, believing that you are capable of growth or change over time and eventually, tackling those things head on and proving to yourself that you can do those things.”
\n\n[00:12:58] “ it’s kind of a step process, you’ve got to believe, you’ve got to see some success and then you’ve got to just run with it as much as you can over time.”
\n\n[00:37:54] “ Curiosity is what encourages you to keep learning new skills which you are inevitably going to need in this profession.”
\n\n[00:44:00] “ Humans are still some of the most powerful computers walking around, know we are incredibly attuned to in particular speech or some of these different systems and we can still make good decisions beyond what a computer can do, if we are trained properly.”
\n\nHIGHLIGHTS FROM THE SHOW
\n\n[00:00:48] Guest Introduction
\n\n[00:03:13] Ken tells us about the Silver Plaque button he got for 100,000 YouTube subscribers
\n\n[00:04:54] Talk to us about where you grew up and what it was like there
\n\n[00:07:58] What would you say if given the opportunity to help somebody who sees things differently?
\n\n[00:11:21] Have you ever had to stay put without having to really change your surroundings but just the internal stuff?
\n\n[00:13:13] How you can reinvent yourself through belief systems
\n\n[00:13:51] What Ken think about luck and it’s interpretation
\n\n[00:16:02] Are you right now, curious or interested in anything?
\n\n[00:19:23] How did you find yourself in the data world?
\n\n[00:21:57] Do you think you need graduate training to become a data scientist?
\n\n[00:24:03] As an educator, in 10 to 15 years, do you think the education “ ecosystem “ is going to be changed? Do you think college is going to be something you need to do?
\n\n[00:27:55] Why Ken thinks Schooling breeds interpersonal skills.
\n\n[00:29:29] Why 66days? of Data Science Science Challenge and what do you think were some of the hardest things to unlearn in that process?
\n\n[00:33:09] What would you say was your least favorite thing to relearn about Data Science?
\n\n[00:34:23] In the process of learning new habits, what is the one habit you decided not to pick up because you found it irrelevant to the world of data science?
\n\n[00:36:08] When you are breaking down large problems into chunks, do you have a systematic way to do it? Or does it vary with every problem you are facing at the time?
\n\n[00:36:41] What is your favorite question that people ask you about breaking into data science?
\n\n[00:37:47] What do you think is the most underrated skill that a data scientist can have?
\n\n[00:40:45] When it comes to projects, what do you think is more important? Content or Process?
\n\n[00:43:40] What do you think the biggest misconception data scientists have about data science is?
\n\n[00:45:40] What would you say is the biggest misconception that aspiring data scientists have about data science.
\n\n[00:46:46] When someone says the bigger picture, what does it mean to you?
\n\n[00:48:49] How do you decide what next you’ll spend time learning or developing?
\n\n[00: 52:53] Tell us about your Ken’s Nearest Neighbor’s podcast
\n\n[00:56:35] it’s 100 years in the future. What do you want to be remembered for?
\n\n[00:57:41] The Random Round
Special Guest: Ken Jee.
","summary":"","date_published":"2021-06-25T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/53afb36a-c827-4339-a665-7077cb300c3e.mp3","mime_type":"audio/mpeg","size_in_bytes":100281124,"duration_in_seconds":4177}]},{"id":"96a9fcb9-d6a8-49bb-9ae3-40f52f71c36d","title":"Comet ML Office Hour 19 | 20JUN2021","url":"https://theartistsofdatascience.fireside.fm/comet-ml-19","content_text":"Don't have time to listen to the whole episode? Check out the summary here: https://www.comet.ml/site/comet-office-hours-recap-for-june-20-2021/\n\nComet provides a self-hosted and cloud-based meta machine learning platform allowing data scientists and teams to track, compare, explain and optimize experiments and models.\n\nBacked by thousands of users and multiple Fortune 100 companies, Comet provides insights and data to build better, more accurate AI models while improving productivity, collaboration and visibility across teams.\n\nRegister for future sessions here: http://bit.ly/comet-ml-oh\n\nCheckout Comet ML by visiting: https://www.comet.ml/","content_html":"Don't have time to listen to the whole episode? Check out the summary here: https://www.comet.ml/site/comet-office-hours-recap-for-june-20-2021/
\n\nComet provides a self-hosted and cloud-based meta machine learning platform allowing data scientists and teams to track, compare, explain and optimize experiments and models.
\n\nBacked by thousands of users and multiple Fortune 100 companies, Comet provides insights and data to build better, more accurate AI models while improving productivity, collaboration and visibility across teams.
\n\nRegister for future sessions here: http://bit.ly/comet-ml-oh
\n\nCheckout Comet ML by visiting: https://www.comet.ml/
","summary":"","date_published":"2021-06-24T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/96a9fcb9-d6a8-49bb-9ae3-40f52f71c36d.mp3","mime_type":"audio/mpeg","size_in_bytes":30942764,"duration_in_seconds":3500}]},{"id":"63ac608d-1c38-47ce-b0ac-54981a648b6a","title":"Data Science Happy Hour 37 | 18JUN2021","url":"https://theartistsofdatascience.fireside.fm/oh37","content_text":"Get 70% off of Data Science Dream Job today! http://dsdj.co/artists70\n\nVote in the data community content creators awards! http://bit.ly/data-creators-awards\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-ohSpecial Guest: Vin Vashishta.","content_html":"Get 70% off of Data Science Dream Job today! http://dsdj.co/artists70
\n\nVote in the data community content creators awards! http://bit.ly/data-creators-awards
\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh
\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
Special Guest: Vin Vashishta.
","summary":"","date_published":"2021-06-20T14:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/63ac608d-1c38-47ce-b0ac-54981a648b6a.mp3","mime_type":"audio/mpeg","size_in_bytes":36637982,"duration_in_seconds":4081}]},{"id":"128c3571-788c-43d8-b2e4-4c15dbe8b833","title":"The Philosophy of Sentientism | Jamie Woodhouse","url":"https://theartistsofdatascience.fireside.fm/jamie-woodhouse","content_text":"MEMORABLE QUOTES \n\n[00:06:11] “ When we are choosing what to believe, we should choose evidence and reason.”\n\n[00:11:39] “ ..if you more morality is primarily concerned with reducing suffering and well-being, you know, why not have a moral circle that includes all beings capable of suffering?”\n\n[00:14:24] “ Our evidence is never going to be perfect. Our reasoning is highly unlikely to be perfect. It’s all we have but it’s highly unlikely to be perfect. So we should be deeply skeptical about all our beliefs outside the formal system.”\n\n[00:15:49] “The real challenge is the ability to have compassion for people you disagree with, even have compassion for people that you think are immoral or are causing harm…doesn’t mean you are weak.”\n\n[00:49:14] “I think stoicism is a really good way of helping you balance what you can have an effect on, what you can’t accept, the things you can’t control and try and have a positive influence on other things.”\n\n[00:51:48] “ We should try and do the most good we possibly can and we should use evidence and reason and rationality to work out what is most effective to do so. Instead of just emotionally doing what feels good, we should actually tend to think through how we can mitigate the most possible suffering of ascensions as well.”\n\nHIGHLIGHTS FROM THE SHOW\n\n[00:00:37] Guest introduction\n\n[00:02:15] Where did you grow up? And what was it like there?\n\n[00:03:04] What kind of kid were are you in high school?\n\n[00:04:10] How different is life now than what you had imagined it was going to be like when you were that age?\n\n[00:04:36] What was your journey like then, from those years till now? What have you been up to?\n\n[00:05:55] What is the philosophy of Sentientism? And how did your experience help shape the philosophy itself?\n\n[00:12:51] Would that be kind of the line in the sand if we have to binary classify?\n\n[00:14:07] How would an ideal standup ideal practitioner of sentimentalism behave in their daily lives? \n\n[00:19:45] Does it also encompass the belief that there are differences between us and animals? Why do we even see a difference between man and animals?\n\n[00:26:36] How to encompass the entire spectrum in your sphere of compassion\n\n[00:29:47] Is evidence-based reason and compassion for all sentient beings just a capacity to process information in some really fast and interesting ways?\n\n[00:35:09] How does sentimentalism account for AI?\n\n[00:40:06] Why should humans be the ones to include other things in their moral circle when other things might not include us in their moral circle?\n\n[00:46:57] What does compassionate retribution look like?\n\n[00:49:03] How can we practice compassion a little more in our daily lives?\n\n[00:53:54] Jamie tells us about how sentientism starts in infants and how it is trained out of them by society.\n\n[00:55:59] It’s 100 years in the future. What do you want to be remembered for?\n\n[00:58:31] The Random RoundSpecial Guest: Jamie Woodhouse.","content_html":"MEMORABLE QUOTES
\n\n[00:06:11] “ When we are choosing what to believe, we should choose evidence and reason.”
\n\n[00:11:39] “ ..if you more morality is primarily concerned with reducing suffering and well-being, you know, why not have a moral circle that includes all beings capable of suffering?”
\n\n[00:14:24] “ Our evidence is never going to be perfect. Our reasoning is highly unlikely to be perfect. It’s all we have but it’s highly unlikely to be perfect. So we should be deeply skeptical about all our beliefs outside the formal system.”
\n\n[00:15:49] “The real challenge is the ability to have compassion for people you disagree with, even have compassion for people that you think are immoral or are causing harm…doesn’t mean you are weak.”
\n\n[00:49:14] “I think stoicism is a really good way of helping you balance what you can have an effect on, what you can’t accept, the things you can’t control and try and have a positive influence on other things.”
\n\n[00:51:48] “ We should try and do the most good we possibly can and we should use evidence and reason and rationality to work out what is most effective to do so. Instead of just emotionally doing what feels good, we should actually tend to think through how we can mitigate the most possible suffering of ascensions as well.”
\n\nHIGHLIGHTS FROM THE SHOW
\n\n[00:00:37] Guest introduction
\n\n[00:02:15] Where did you grow up? And what was it like there?
\n\n[00:03:04] What kind of kid were are you in high school?
\n\n[00:04:10] How different is life now than what you had imagined it was going to be like when you were that age?
\n\n[00:04:36] What was your journey like then, from those years till now? What have you been up to?
\n\n[00:05:55] What is the philosophy of Sentientism? And how did your experience help shape the philosophy itself?
\n\n[00:12:51] Would that be kind of the line in the sand if we have to binary classify?
\n\n[00:14:07] How would an ideal standup ideal practitioner of sentimentalism behave in their daily lives?
\n\n[00:19:45] Does it also encompass the belief that there are differences between us and animals? Why do we even see a difference between man and animals?
\n\n[00:26:36] How to encompass the entire spectrum in your sphere of compassion
\n\n[00:29:47] Is evidence-based reason and compassion for all sentient beings just a capacity to process information in some really fast and interesting ways?
\n\n[00:35:09] How does sentimentalism account for AI?
\n\n[00:40:06] Why should humans be the ones to include other things in their moral circle when other things might not include us in their moral circle?
\n\n[00:46:57] What does compassionate retribution look like?
\n\n[00:49:03] How can we practice compassion a little more in our daily lives?
\n\n[00:53:54] Jamie tells us about how sentientism starts in infants and how it is trained out of them by society.
\n\n[00:55:59] It’s 100 years in the future. What do you want to be remembered for?
\n\n[00:58:31] The Random Round
Special Guest: Jamie Woodhouse.
","summary":"","date_published":"2021-06-18T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/128c3571-788c-43d8-b2e4-4c15dbe8b833.mp3","mime_type":"audio/mpeg","size_in_bytes":77617940,"duration_in_seconds":3880}]},{"id":"a323b5a5-fee9-4558-9682-164a3fb1530c","title":"Comet ML Office Hour 18 | 13JUN2021","url":"https://theartistsofdatascience.fireside.fm/comet-ml-18","content_text":"Don't have time to listen to the whole episode? Check out the summary here: https://www.comet.ml/site/comet-office-hours-recap-for-june-13-2021\n\nComet provides a self-hosted and cloud-based meta machine learning platform allowing data scientists and teams to track, compare, explain and optimize experiments and models.\n\nBacked by thousands of users and multiple Fortune 100 companies, Comet provides insights and data to build better, more accurate AI models while improving productivity, collaboration and visibility across teams.\n\nRegister for future sessions here: http://bit.ly/comet-ml-oh\n\nCheckout Comet ML by visiting: https://www.comet.ml/\n\n[00:00:58] Community member Marin talks about his journey transitioning into data science.\n\n[00:03:17] Marin talks about why he was scared to embark upon his job search journey\n\n[00:06:11] What were some of your challenges as you were making this transition?\n\n[00:10:37] The failure of networking\n\n[00:16:04] How do you maintain your focus and not just get off track? \n\n[00:28:31] Harpreet realizes that the advice he has just given comes from a privileged position and talks about how he maintained focus and momentum when he was on the journey to BECOMING a data scientist.\n\n[00:34:15] Asha describes “The Steve Jobs Syndrome”\n\n[00:37:03] How to go about finding a job and using LinkedIn effectively\n\n[00:42:17] Mark comes by and we discuss his desire to get on the speaker circuit\n\n[00:44:32] Mark talks about his experience as a data scientist at startups\n\n[00:52:35] Mark talks about how he keeps his momentum up\n\n[00:56:14] Community member Chris asks a question around how much he should know about cloud SQL databases","content_html":"Don't have time to listen to the whole episode? Check out the summary here: https://www.comet.ml/site/comet-office-hours-recap-for-june-13-2021
\n\nComet provides a self-hosted and cloud-based meta machine learning platform allowing data scientists and teams to track, compare, explain and optimize experiments and models.
\n\nBacked by thousands of users and multiple Fortune 100 companies, Comet provides insights and data to build better, more accurate AI models while improving productivity, collaboration and visibility across teams.
\n\nRegister for future sessions here: http://bit.ly/comet-ml-oh
\n\nCheckout Comet ML by visiting: https://www.comet.ml/
\n\n[00:00:58] Community member Marin talks about his journey transitioning into data science.
\n\n[00:03:17] Marin talks about why he was scared to embark upon his job search journey
\n\n[00:06:11] What were some of your challenges as you were making this transition?
\n\n[00:10:37] The failure of networking
\n\n[00:16:04] How do you maintain your focus and not just get off track?
\n\n[00:28:31] Harpreet realizes that the advice he has just given comes from a privileged position and talks about how he maintained focus and momentum when he was on the journey to BECOMING a data scientist.
\n\n[00:34:15] Asha describes “The Steve Jobs Syndrome”
\n\n[00:37:03] How to go about finding a job and using LinkedIn effectively
\n\n[00:42:17] Mark comes by and we discuss his desire to get on the speaker circuit
\n\n[00:44:32] Mark talks about his experience as a data scientist at startups
\n\n[00:52:35] Mark talks about how he keeps his momentum up
\n\n[00:56:14] Community member Chris asks a question around how much he should know about cloud SQL databases
","summary":"","date_published":"2021-06-17T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/a323b5a5-fee9-4558-9682-164a3fb1530c.mp3","mime_type":"audio/mpeg","size_in_bytes":39971646,"duration_in_seconds":4489}]},{"id":"3086531a-812e-486f-a58d-f4eeb5d790e3","title":"Data Science Happy Hour 36 | 11JUN2021","url":"https://theartistsofdatascience.fireside.fm/oh36","content_text":"Get 70% off of Data Science Dream Job today! http://dsdj.co/artists70\n\nVote in the data community content creators awards! http://bit.ly/data-creators-awards\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-ohSpecial Guests: Greg Coquillo, Mikiko Bazeley, and Vin Vashishta.","content_html":"Get 70% off of Data Science Dream Job today! http://dsdj.co/artists70
\n\nVote in the data community content creators awards! http://bit.ly/data-creators-awards
\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh
\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
Special Guests: Greg Coquillo, Mikiko Bazeley, and Vin Vashishta.
","summary":"","date_published":"2021-06-13T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/3086531a-812e-486f-a58d-f4eeb5d790e3.mp3","mime_type":"audio/mpeg","size_in_bytes":51860530,"duration_in_seconds":5779}]},{"id":"62efd5fc-d29b-44b8-841c-3fa45a0cf3a3","title":"A Data Scientist From Down Under | Steve Nouri","url":"https://theartistsofdatascience.fireside.fm/steve-nouri","content_text":"Steve is a data science leader who is evolving the way people look at AI and innovation. \n\nHe’s an entrepreneur, investor, author, academic and technical manager by profession. And he’s on a mission to inspire people through his involvement in the latest technology trends and projects.\n\nFind Steve Online\n\nLinkedIn: https://www.linkedin.com/in/stevenouri\n\nQUOTES\n\n[00:17:54] \"What I found through this journey: it's about consistency. You will not get any benefit because of one anomaly. And people will not start trusting you just because one of your posts went super viral. It's a long game.\"\n\n[00:20:10] \"That was another achievement for me, to overcome that feeling and fear of feeling reserved and vulnerable...\"\n\n[00:39:14] \"I believe a thought leader is a person that shares their own opinion and has some original content added to the latest trends and cutting edge.\" \n\nHIGHLIGHTS FROM THE SHOW\n\n[00:01:42] Guest Introduction\n\n[00:03:02] Where did you grow up and what was it like there?\n\n[00:07:07] What were you like in high school?\n\n[00:08:17] Steve talks about entrepreneurship\n\n[00:12:46] Steve’s tips for becoming big on LinkedIn\n\n[00:16:14] How to go viral on LinkedIn\n\n[00:18:24] What is it like to have your posts viewed by millions of people?\n\n[00:21:46] Why you should stop being scared and just post it!\n\n[00:24:29] What is your favorite type of message or your favorite type of question to get from your audience?\n\n[00:29:21] Please don’t message Steve asking him to do your homework…\n\n[00:30:08] What type of questions annoy you? \n\n[00:34:06] How Steve finds cool content\n\n[00:37:45] What Steve follows to stay up on industry trends\n\n[00:39:04] What does being a thought leader mean to you?\n\n[00:41:13] Where do you see the field of A.I. headed in the next five years\n\n[00:45:07] Do you see a certain industry really starting to bloom and blossom because of the COVID world that we now live in? \n\n[00:48:03] Do you think that data scientists necessarily need to pursue graduate training?\n\n[00:56:04] Learn how to learn\n\n[00:58:01] Can’t land a data science job? Listen here\n\n[01:02:29] It is one hundred years in the future, what do you want to be remembered for?\n\n[01:04:00] Random RoundSpecial Guest: Steve Nouri.","content_html":"Steve is a data science leader who is evolving the way people look at AI and innovation.
\n\nHe’s an entrepreneur, investor, author, academic and technical manager by profession. And he’s on a mission to inspire people through his involvement in the latest technology trends and projects.
\n\nFind Steve Online
\n\nLinkedIn: https://www.linkedin.com/in/stevenouri
\n\nQUOTES
\n\n[00:17:54] "What I found through this journey: it's about consistency. You will not get any benefit because of one anomaly. And people will not start trusting you just because one of your posts went super viral. It's a long game."
\n\n[00:20:10] "That was another achievement for me, to overcome that feeling and fear of feeling reserved and vulnerable..."
\n\n[00:39:14] "I believe a thought leader is a person that shares their own opinion and has some original content added to the latest trends and cutting edge."
\n\nHIGHLIGHTS FROM THE SHOW
\n\n[00:01:42] Guest Introduction
\n\n[00:03:02] Where did you grow up and what was it like there?
\n\n[00:07:07] What were you like in high school?
\n\n[00:08:17] Steve talks about entrepreneurship
\n\n[00:12:46] Steve’s tips for becoming big on LinkedIn
\n\n[00:16:14] How to go viral on LinkedIn
\n\n[00:18:24] What is it like to have your posts viewed by millions of people?
\n\n[00:21:46] Why you should stop being scared and just post it!
\n\n[00:24:29] What is your favorite type of message or your favorite type of question to get from your audience?
\n\n[00:29:21] Please don’t message Steve asking him to do your homework…
\n\n[00:30:08] What type of questions annoy you?
\n\n[00:34:06] How Steve finds cool content
\n\n[00:37:45] What Steve follows to stay up on industry trends
\n\n[00:39:04] What does being a thought leader mean to you?
\n\n[00:41:13] Where do you see the field of A.I. headed in the next five years
\n\n[00:45:07] Do you see a certain industry really starting to bloom and blossom because of the COVID world that we now live in?
\n\n[00:48:03] Do you think that data scientists necessarily need to pursue graduate training?
\n\n[00:56:04] Learn how to learn
\n\n[00:58:01] Can’t land a data science job? Listen here
\n\n[01:02:29] It is one hundred years in the future, what do you want to be remembered for?
\n\n[01:04:00] Random Round
Special Guest: Steve Nouri.
","summary":"","date_published":"2021-06-11T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/62efd5fc-d29b-44b8-841c-3fa45a0cf3a3.mp3","mime_type":"audio/mpeg","size_in_bytes":100511906,"duration_in_seconds":4187}]},{"id":"07ba4a29-36e8-46fb-a16c-cf1fb27b2599","title":"Comet ML Office Hour 17 | 06JUN2021","url":"https://theartistsofdatascience.fireside.fm/comet-ml-17","content_text":"Read the recap of the epiosde here: https://www.comet.ml/site/comet-office-hours-recap-for-june-6-2021/\n\nComet provides a self-hosted and cloud-based meta machine learning platform allowing data scientists and teams to track, compare, explain and optimize experiments and models.\n\nBacked by thousands of users and multiple Fortune 100 companies, Comet provides insights and data to build better, more accurate AI models while improving productivity, collaboration and visibility across teams.\n\nRegister for future sessions here: http://bit.ly/comet-ml-oh\n\nCheckout Comet ML by visiting: https://www.comet.ml/\n\nCheckout the latest FREE e-book from Comet - Building Effective Machine Learning Teams: https://bit.ly/3bWrJ0O\n\nOr on Twitter: https://twitter.com/CometML\n\nOn YouTube: https://www.youtube.com/channel/UCmN63HKvfXSCS-UwVwmK8Hw\n\nVote in the data community content creators awards! http://bit.ly/data-creators-awards\n\nCheck it out and don't forget to register for Friday Happy Hour sessions: http://bit.ly/adsoh\n\nWatch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm\n\nCheckout the interview I had with the Super Data Science Podcast: https://www.superdatascience.com/podcast/landing-your-data-science-dream-job\n\n[00:00:09] What advice would you give to the younger version of yourself?\n\n[00:07:14] Don’t be afraid of posting on LinkedIn\n\n[00:08:07] What are some topics from statistics I should learn?\n\n[00:12:48] A high level (and incomplete) overview of the ML process\n\n[00:21:27] “Whenever I’m faced with a new problem, I’m stuck”\n\n[00:28:15] A question on pricing optimization and Harpreet stumbles his way through it\n\n[00:39:09] What’s your approach to reading books?","content_html":"Read the recap of the epiosde here: https://www.comet.ml/site/comet-office-hours-recap-for-june-6-2021/
\n\nComet provides a self-hosted and cloud-based meta machine learning platform allowing data scientists and teams to track, compare, explain and optimize experiments and models.
\n\nBacked by thousands of users and multiple Fortune 100 companies, Comet provides insights and data to build better, more accurate AI models while improving productivity, collaboration and visibility across teams.
\n\nRegister for future sessions here: http://bit.ly/comet-ml-oh
\n\nCheckout Comet ML by visiting: https://www.comet.ml/
\n\nCheckout the latest FREE e-book from Comet - Building Effective Machine Learning Teams: https://bit.ly/3bWrJ0O
\n\nOr on Twitter: https://twitter.com/CometML
\n\nOn YouTube: https://www.youtube.com/channel/UCmN63HKvfXSCS-UwVwmK8Hw
\n\nVote in the data community content creators awards! http://bit.ly/data-creators-awards
\n\nCheck it out and don't forget to register for Friday Happy Hour sessions: http://bit.ly/adsoh
\n\nWatch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm
\n\nCheckout the interview I had with the Super Data Science Podcast: https://www.superdatascience.com/podcast/landing-your-data-science-dream-job
\n\n[00:00:09] What advice would you give to the younger version of yourself?
\n\n[00:07:14] Don’t be afraid of posting on LinkedIn
\n\n[00:08:07] What are some topics from statistics I should learn?
\n\n[00:12:48] A high level (and incomplete) overview of the ML process
\n\n[00:21:27] “Whenever I’m faced with a new problem, I’m stuck”
\n\n[00:28:15] A question on pricing optimization and Harpreet stumbles his way through it
\n\n[00:39:09] What’s your approach to reading books?
","summary":"","date_published":"2021-06-10T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/07ba4a29-36e8-46fb-a16c-cf1fb27b2599.mp3","mime_type":"audio/mpeg","size_in_bytes":31161545,"duration_in_seconds":3514}]},{"id":"2f9a1a92-ee8a-4f70-8266-2fceabaa50d5","title":"Data Science Happy Hour 35 | 04JUN2021","url":"https://theartistsofdatascience.fireside.fm/oh35","content_text":"Get 70% off of Data Science Dream Job today! http://dsdj.co/artists70\n\nVote in the data community content creators awards! http://bit.ly/data-creators-awards\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh\n\n[00:01:32] What were you confused about when you first started to break into data science?\n\n[00:02:54] “What exactly hiring managers were really looking for was something that I found really confusing. “ – Dave Langer\n\n[00:04:04] “And then you study like mad and you get all these things and then you land on the job and you're making PowerBI dashboards all day.”\n\n[00:05:22] Eric Sims speaks on it\n\n[00:07:54] Do you even need a undergrad degree to be in data science?\n\n[00:11:49] “I feel like a wasted my time in grad school. I could learn this shit on my own.” \n\n[00:12:45] Greg Coquillo speaks on this topic\n\n[00:15:25] Antonio weighs in\n\n[00:19:21] We hear from James, Dave Langer, and Vivian on this topic\n\n[00:25:39] Greg Coquillo talks about minimizing risk during the job search process\n\n[00:33:48] Martin looks for some insight for the job search process location. \n\n[00:36:49] Dave Langer gives Martion some really valuable advice\n\n[00:39:25] Greg talks about how to stand out from the crown\n\n[00:45:20] Russell Willis on network advice\n\n[00:48:54] Breaking into data science from a data analyst role\n\n[00:49:33] Philosopher Harpreet comes out and talks about imposter syndrome\n\n[00:52:30] Greg on how to overcome imposter syndrome and how to talk about your previous experience in an interview\n\n[00:57:48] Dave Langer on imposter syndrome\n\n[01:02:03] Ben Taylor is in the house!\n\n[01:03:15] Ben Taylor on all of the above","content_html":"Get 70% off of Data Science Dream Job today! http://dsdj.co/artists70
\n\nVote in the data community content creators awards! http://bit.ly/data-creators-awards
\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh
\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\n\n[00:01:32] What were you confused about when you first started to break into data science?
\n\n[00:02:54] “What exactly hiring managers were really looking for was something that I found really confusing. “ – Dave Langer
\n\n[00:04:04] “And then you study like mad and you get all these things and then you land on the job and you're making PowerBI dashboards all day.”
\n\n[00:05:22] Eric Sims speaks on it
\n\n[00:07:54] Do you even need a undergrad degree to be in data science?
\n\n[00:11:49] “I feel like a wasted my time in grad school. I could learn this shit on my own.”
\n\n[00:12:45] Greg Coquillo speaks on this topic
\n\n[00:15:25] Antonio weighs in
\n\n[00:19:21] We hear from James, Dave Langer, and Vivian on this topic
\n\n[00:25:39] Greg Coquillo talks about minimizing risk during the job search process
\n\n[00:33:48] Martin looks for some insight for the job search process location.
\n\n[00:36:49] Dave Langer gives Martion some really valuable advice
\n\n[00:39:25] Greg talks about how to stand out from the crown
\n\n[00:45:20] Russell Willis on network advice
\n\n[00:48:54] Breaking into data science from a data analyst role
\n\n[00:49:33] Philosopher Harpreet comes out and talks about imposter syndrome
\n\n[00:52:30] Greg on how to overcome imposter syndrome and how to talk about your previous experience in an interview
\n\n[00:57:48] Dave Langer on imposter syndrome
\n\n[01:02:03] Ben Taylor is in the house!
\n\n[01:03:15] Ben Taylor on all of the above
","summary":"","date_published":"2021-06-06T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/2f9a1a92-ee8a-4f70-8266-2fceabaa50d5.mp3","mime_type":"audio/mpeg","size_in_bytes":39926432,"duration_in_seconds":4433}]},{"id":"a8784ff3-ce50-411f-aeb8-4823b5de2de1","title":"The Only Way To Rise is Laterally | Arjun Sachdev","url":"https://theartistsofdatascience.fireside.fm/arjun-sachdev","content_text":"We've got a conversations episode today! Arjun is a good friend of mine and co-host of the Rising Laterally podcast!\n\nConnect with Arjun Online\n\nLinkedIn: https://www.linkedin.com/in/shiftsinperspective/\n\nTwitter: https://twitter.com/RisingLaterally\n\nPodcast: https://www.risinglaterally.com/\n\nFind Rising Laterally wherever you get your favorite podcasts and be sure to leave them a five-star review!\n\nQUOTES \n[00:17:02] “ Everyone’s playing a game basically and the first thing you have to do is figure out where the value lies for the person that you’re talking to or interacting with.”\n\n[00:19:44] “If we were truly truly truly evolved and we really were human in every situation, we would be able to get over the things that are human conditions that hold us back.”\n\n[00:23:18] “Mathematics is interesting in so far as it occupies our reasoning and inventive powers. But there is nothing to learn about reasoning and invention if the motive and the purpose of the most conspicuous step remain incomprehensible.”\n\n[00:25:19] “Be more kind, be more compassionate…. If you are really going to be empathetic, you have to be able to feel pain. You have to actually be able to put yourself in some of someone’s shoes.”\n\n[00:27:40] “By reading a book, you can download decades in days, just all that compressed information in a book here.” \n\n[00:38:45] “Just do something, because when you are doing something, you are living.”\n\n[00:57:59] “I just want to be remembered for someone who had his own opinions, brought energy, brought passion, and along the way, hopefully, help shift some perspectives.”\n\n[00:59:39] “…you have to be the catalyst…to say what’s never been said, do what’s never been done before. Draw, paint, sing, sculpt, dance, act what’s never been done before.”\n\nHIGHLIGHTS FROM THE SHOW \n\n[00:00:26] Guest Introduction\n\n[00:03:15] Arjun tells us about where he grew up and what it was like there\n\n[00:04:57] We learn about Argun’s experience living in the states as a child of Indian immigrants \n\n[00:06:38] How Arjun left his job of 10 years to pursue his own career path\n\n[00:09:48] What has the process for getting to know and study yourself on a deeper level been like?\n\n[00:13:19] Arjun talks about the one thing that helps him to stay focused\n\n[00:14:06] What Arjun does when he is running \n\n[00:15:31] How he struggles with paying attention to audiobooks and why sleep is important \n\n[00:17:02] Arjun shares his thoughts and memories about the Game theory \n\n[00:18:56] What are the three things you are grateful for today?\n\n[00:19:41] How do you feel when our ability to think is taken for granted?\n\n[00:26:24] How long have you known your co-host, Jay?\n\n[00:27:40] Arjun talks about the journey so far and the challenges he came across \n\n[00:31:19] How Arjun decides what option to test when faced with multiple choices \n\n[00:32:51] How to navigate social media as a data scientist \n\n[00:40:52] Arjun makes mention of a near-death experience. He tells us more about it\n\n[00:46:03] How do you do with goal setting?\n\n[00:52:49] Have you thought about time differently?\n\n[00:57:45] It’s 100 years in the future, what do you want to be remembered for?\n\n[00:59:39] The Random Round Special Guest: Arjun Sachdev.","content_html":"We've got a conversations episode today! Arjun is a good friend of mine and co-host of the Rising Laterally podcast!
\n\nConnect with Arjun Online
\n\nLinkedIn: https://www.linkedin.com/in/shiftsinperspective/
\n\nTwitter: https://twitter.com/RisingLaterally
\n\nPodcast: https://www.risinglaterally.com/
\n\nFind Rising Laterally wherever you get your favorite podcasts and be sure to leave them a five-star review!
\n\nQUOTES
\n[00:17:02] “ Everyone’s playing a game basically and the first thing you have to do is figure out where the value lies for the person that you’re talking to or interacting with.”
[00:19:44] “If we were truly truly truly evolved and we really were human in every situation, we would be able to get over the things that are human conditions that hold us back.”
\n\n[00:23:18] “Mathematics is interesting in so far as it occupies our reasoning and inventive powers. But there is nothing to learn about reasoning and invention if the motive and the purpose of the most conspicuous step remain incomprehensible.”
\n\n[00:25:19] “Be more kind, be more compassionate…. If you are really going to be empathetic, you have to be able to feel pain. You have to actually be able to put yourself in some of someone’s shoes.”
\n\n[00:27:40] “By reading a book, you can download decades in days, just all that compressed information in a book here.”
\n\n[00:38:45] “Just do something, because when you are doing something, you are living.”
\n\n[00:57:59] “I just want to be remembered for someone who had his own opinions, brought energy, brought passion, and along the way, hopefully, help shift some perspectives.”
\n\n[00:59:39] “…you have to be the catalyst…to say what’s never been said, do what’s never been done before. Draw, paint, sing, sculpt, dance, act what’s never been done before.”
\n\nHIGHLIGHTS FROM THE SHOW
\n\n[00:00:26] Guest Introduction
\n\n[00:03:15] Arjun tells us about where he grew up and what it was like there
\n\n[00:04:57] We learn about Argun’s experience living in the states as a child of Indian immigrants
\n\n[00:06:38] How Arjun left his job of 10 years to pursue his own career path
\n\n[00:09:48] What has the process for getting to know and study yourself on a deeper level been like?
\n\n[00:13:19] Arjun talks about the one thing that helps him to stay focused
\n\n[00:14:06] What Arjun does when he is running
\n\n[00:15:31] How he struggles with paying attention to audiobooks and why sleep is important
\n\n[00:17:02] Arjun shares his thoughts and memories about the Game theory
\n\n[00:18:56] What are the three things you are grateful for today?
\n\n[00:19:41] How do you feel when our ability to think is taken for granted?
\n\n[00:26:24] How long have you known your co-host, Jay?
\n\n[00:27:40] Arjun talks about the journey so far and the challenges he came across
\n\n[00:31:19] How Arjun decides what option to test when faced with multiple choices
\n\n[00:32:51] How to navigate social media as a data scientist
\n\n[00:40:52] Arjun makes mention of a near-death experience. He tells us more about it
\n\n[00:46:03] How do you do with goal setting?
\n\n[00:52:49] Have you thought about time differently?
\n\n[00:57:45] It’s 100 years in the future, what do you want to be remembered for?
\n\n[00:59:39] The Random Round
Special Guest: Arjun Sachdev.
","summary":"","date_published":"2021-06-04T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/a8784ff3-ce50-411f-aeb8-4823b5de2de1.mp3","mime_type":"audio/mpeg","size_in_bytes":39017178,"duration_in_seconds":4269}]},{"id":"f6aa3f23-5f20-4307-ac59-56268bef8627","title":"Comet ML Office Hours 16 | 30MAY2021","url":"https://theartistsofdatascience.fireside.fm/comet-ml-16","content_text":"Checkout the episode recap here: https://www.comet.ml/site/comet-office-hours-recap-for-may-23rd-and-may-30th/\nComet provides a self-hosted and cloud-based meta machine learning platform allowing data scientists and teams to track, compare, explain and optimize experiments and models.\n\nBacked by thousands of users and multiple Fortune 100 companies, Comet provides insights and data to build better, more accurate AI models while improving productivity, collaboration and visibility across teams.\n\nRegister for future sessions here: http://bit.ly/comet-ml-oh\n\nCheckout Comet ML by visiting: https://www.comet.ml/\n\nCheckout the latest FREE e-book from Comet - Building Effective Machine Learning Teams: https://bit.ly/3bWrJ0O\n\nOr on Twitter: https://twitter.com/CometML\n\nOn YouTube: https://www.youtube.com/channel/UCmN63HKvfXSCS-UwVwmK8Hw\n\nVote in the data community content creators awards! http://bit.ly/data-creators-awards\n\nCheck it out and don't forget to register for Friday Happy Hour sessions: http://bit.ly/adsoh\n\nWatch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm\n\nCheckout the interview I had with the Super Data Science Podcast: https://www.superdatascience.com/podcast/landing-your-data-science-dream-job\n\n[00:01:03] Harpreet talks about how his entire life is recorded \n\n[00:03:54] Zoom fatigue is a thing\n\n[00:06:26] A community member talks about a cool project they worked on\n\n[00:08:50] You don’t need to do everything you know how to do in a project\n\n[00:10:00] How to be a better writer\n\n[00:17:04] Write for you a year ago\n\n[00:21:02] We talk about the relative importance of data science certificates\n\n[00:25:01] Be in motion\n\n[00:29:55] Networking is important\n\n[00:34:00] It doesn’t matter which bootcamp you enroll in\n\n[00:41:32] Which data visualization tool should I use?\n\n[00:51:59] John David of the How to Get an Analytics Job Podcast stops by the show\n\n[00:58:29] How important is graduate education to becoming a data scientist? ","content_html":"Checkout the episode recap here: https://www.comet.ml/site/comet-office-hours-recap-for-may-23rd-and-may-30th/
\nComet provides a self-hosted and cloud-based meta machine learning platform allowing data scientists and teams to track, compare, explain and optimize experiments and models.
Backed by thousands of users and multiple Fortune 100 companies, Comet provides insights and data to build better, more accurate AI models while improving productivity, collaboration and visibility across teams.
\n\nRegister for future sessions here: http://bit.ly/comet-ml-oh
\n\nCheckout Comet ML by visiting: https://www.comet.ml/
\n\nCheckout the latest FREE e-book from Comet - Building Effective Machine Learning Teams: https://bit.ly/3bWrJ0O
\n\nOr on Twitter: https://twitter.com/CometML
\n\nOn YouTube: https://www.youtube.com/channel/UCmN63HKvfXSCS-UwVwmK8Hw
\n\nVote in the data community content creators awards! http://bit.ly/data-creators-awards
\n\nCheck it out and don't forget to register for Friday Happy Hour sessions: http://bit.ly/adsoh
\n\nWatch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm
\n\nCheckout the interview I had with the Super Data Science Podcast: https://www.superdatascience.com/podcast/landing-your-data-science-dream-job
\n\n[00:01:03] Harpreet talks about how his entire life is recorded
\n\n[00:03:54] Zoom fatigue is a thing
\n\n[00:06:26] A community member talks about a cool project they worked on
\n\n[00:08:50] You don’t need to do everything you know how to do in a project
\n\n[00:10:00] How to be a better writer
\n\n[00:17:04] Write for you a year ago
\n\n[00:21:02] We talk about the relative importance of data science certificates
\n\n[00:25:01] Be in motion
\n\n[00:29:55] Networking is important
\n\n[00:34:00] It doesn’t matter which bootcamp you enroll in
\n\n[00:41:32] Which data visualization tool should I use?
\n\n[00:51:59] John David of the How to Get an Analytics Job Podcast stops by the show
\n\n[00:58:29] How important is graduate education to becoming a data scientist?
","summary":"","date_published":"2021-06-03T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/f6aa3f23-5f20-4307-ac59-56268bef8627.mp3","mime_type":"audio/mpeg","size_in_bytes":46968065,"duration_in_seconds":5202}]},{"id":"864f1f05-67c9-42d8-b43e-14110781639c","title":"Data Science Happy Hour 34 | 28MAY2021","url":"https://theartistsofdatascience.fireside.fm/oh34","content_text":"Get 70% off of Data Science Dream Job today! http://dsdj.co/artists70\n\nVote in the data community content creators awards! http://bit.ly/data-creators-awards\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh\n\n[00:00:09] We open with the question: What’s a belief you had at the beginning of your data career that, looking back on now, realize was probably false?\n\n[00:13:33] What’s an underserved topic in data science content?\n\n[00:24:13] What are some good resources for MLOps? (Shout out to my friends at Comet ML)\n\n[00:31:49] How to have those hard conversations with stakeholders when projects fail?\n\n[00:36:33] MLOps again\n\n[00:42:15] Interview strategies\n\n[00:51:36] Nasdaq’s stack overflow\n\n[00:59:08] Does anyone else have suggestions for how to be more productive? \n\n[01:01:40] Leveling up in your career\n\n[01:10:01] Lots of book recommendations","content_html":"Get 70% off of Data Science Dream Job today! http://dsdj.co/artists70
\n\nVote in the data community content creators awards! http://bit.ly/data-creators-awards
\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh
\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\n\n[00:00:09] We open with the question: What’s a belief you had at the beginning of your data career that, looking back on now, realize was probably false?
\n\n[00:13:33] What’s an underserved topic in data science content?
\n\n[00:24:13] What are some good resources for MLOps? (Shout out to my friends at Comet ML)
\n\n[00:31:49] How to have those hard conversations with stakeholders when projects fail?
\n\n[00:36:33] MLOps again
\n\n[00:42:15] Interview strategies
\n\n[00:51:36] Nasdaq’s stack overflow
\n\n[00:59:08] Does anyone else have suggestions for how to be more productive?
\n\n[01:01:40] Leveling up in your career
\n\n[01:10:01] Lots of book recommendations
","summary":"","date_published":"2021-05-30T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/864f1f05-67c9-42d8-b43e-14110781639c.mp3","mime_type":"audio/mpeg","size_in_bytes":39738894,"duration_in_seconds":4493}]},{"id":"137bdb37-43fc-48c0-8c97-1d51ca49705e","title":"Your Beliefs Aren't Reality | Dave Gray","url":"https://theartistsofdatascience.fireside.fm/dave-gray","content_text":"Dave is a possibilitarian who believes that anything is possible. And if he thinks something that he wants is impossible, he will devise an experiment to test that assumption.\n\nFIND DAVE ONLINE\n\nTwitter: https://twitter.com/davegray\n\nQUOTES\n\n[00:04:09] \"I think one of the things that surprised me the most is that I was able to somehow find a way through creativity to be financially successful. Which I never somehow never really expected.\"\n\n[00:07:09] \"People tend to use the word creative as a way to describe a kind of a personality trait. Oh, I'm creative. She's creative. He's creative. That's a creative person. And I think there's a lot of connotations that go with that creative meaning...But when you think about what the word creation means, to create something is to bring something new into the world...So creation is the process by which we bring new things into the world that weren't there before. And I think everyone has the potential within them to do that in different ways. \"\n\n[00:13:21] \"Well, reality is unknowable, right? In some way, we all have different experiences of reality. Each one of us has a unique set of experiences, but none of us can know all all of reality.\"\n\n[00:21:30] \"Liminal thinking is being cultivating a mindset where you can find those thresholds, stand on those thresholds between one thing and another.\"\n\nHIGHLIGHTS FROM THE SHOW\n\n[00:01:36] Guest introduction\n\n[00:02:54] We learn about Dave’s background\n\n[00:03:57] How different is life now than what you had imagined it would be?\n\n[00:04:46] What was your journey like to now?\n\n[00:05:30] Was there a particular experience that helped you develop this philosophy around creativity?\n\n[00:06:53] How would you define creativity and how can somebody who doesn't see themselves as a creative individual tap into the creativity that they have naturally?\n\n[00:08:38] How to “connect the dots” \n\n[00:09:39] What do you think is the difference between science and art? \n\n[00:11:43] So in science, we kind of have like that the scientific method. Do you think there's a method to creativity? Can creativity be systematized? \n\n[00:12:44] What is a belief and how is a belief different from reality?\n\n[00:16:12] Can we make sense of the world without beliefs?\n\n[00:19:43] What is liminal thinking?\n\n[00:23:46] How do we create beliefs?\n\n[00:30:36] What are some pitfalls of mistaking belief for reality that you've seen play out organizations?\n\n[00:35:10] Self-sealing logic\n\n[00:39:58] The pyramid of beliefs\n\n[00:42:53] The loopiness of your belief system\n\n[00:46:07] Should we test to falsify our beliefs?\n\n[00:51:43] Johari Window \n\n[00:53:50] Check your cognitive blind spots\n\n[00:55:26] Try on different beliefs\n\n[00:57:13] How can we use storytelling to understand people's beliefs?\n\n[01:00:39] How can we use stories to help persuade people to buy into our ideas?\n\n[01:04:21] How can we make sure that we're not asking questions in such a way that we're almost setting up the response to get an answer that will conform to what we want to hear?\n\n[01:08:03] It is one hundred years in the future: What do you want to be remembered for?\n\n[01:08:23] The Random RoundSpecial Guest: Dave Gray.","content_html":"Dave is a possibilitarian who believes that anything is possible. And if he thinks something that he wants is impossible, he will devise an experiment to test that assumption.
\n\nFIND DAVE ONLINE
\n\nTwitter: https://twitter.com/davegray
\n\nQUOTES
\n\n[00:04:09] "I think one of the things that surprised me the most is that I was able to somehow find a way through creativity to be financially successful. Which I never somehow never really expected."
\n\n[00:07:09] "People tend to use the word creative as a way to describe a kind of a personality trait. Oh, I'm creative. She's creative. He's creative. That's a creative person. And I think there's a lot of connotations that go with that creative meaning...But when you think about what the word creation means, to create something is to bring something new into the world...So creation is the process by which we bring new things into the world that weren't there before. And I think everyone has the potential within them to do that in different ways. "
\n\n[00:13:21] "Well, reality is unknowable, right? In some way, we all have different experiences of reality. Each one of us has a unique set of experiences, but none of us can know all all of reality."
\n\n[00:21:30] "Liminal thinking is being cultivating a mindset where you can find those thresholds, stand on those thresholds between one thing and another."
\n\nHIGHLIGHTS FROM THE SHOW
\n\n[00:01:36] Guest introduction
\n\n[00:02:54] We learn about Dave’s background
\n\n[00:03:57] How different is life now than what you had imagined it would be?
\n\n[00:04:46] What was your journey like to now?
\n\n[00:05:30] Was there a particular experience that helped you develop this philosophy around creativity?
\n\n[00:06:53] How would you define creativity and how can somebody who doesn't see themselves as a creative individual tap into the creativity that they have naturally?
\n\n[00:08:38] How to “connect the dots”
\n\n[00:09:39] What do you think is the difference between science and art?
\n\n[00:11:43] So in science, we kind of have like that the scientific method. Do you think there's a method to creativity? Can creativity be systematized?
\n\n[00:12:44] What is a belief and how is a belief different from reality?
\n\n[00:16:12] Can we make sense of the world without beliefs?
\n\n[00:19:43] What is liminal thinking?
\n\n[00:23:46] How do we create beliefs?
\n\n[00:30:36] What are some pitfalls of mistaking belief for reality that you've seen play out organizations?
\n\n[00:35:10] Self-sealing logic
\n\n[00:39:58] The pyramid of beliefs
\n\n[00:42:53] The loopiness of your belief system
\n\n[00:46:07] Should we test to falsify our beliefs?
\n\n[00:51:43] Johari Window
\n\n[00:53:50] Check your cognitive blind spots
\n\n[00:55:26] Try on different beliefs
\n\n[00:57:13] How can we use storytelling to understand people's beliefs?
\n\n[01:00:39] How can we use stories to help persuade people to buy into our ideas?
\n\n[01:04:21] How can we make sure that we're not asking questions in such a way that we're almost setting up the response to get an answer that will conform to what we want to hear?
\n\n[01:08:03] It is one hundred years in the future: What do you want to be remembered for?
\n\n[01:08:23] The Random Round
Special Guest: Dave Gray.
","summary":"","date_published":"2021-05-28T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/137bdb37-43fc-48c0-8c97-1d51ca49705e.mp3","mime_type":"audio/mpeg","size_in_bytes":86734437,"duration_in_seconds":4336}]},{"id":"956d076f-8696-4ef7-8ac9-5f2ce0e3f176","title":"Comet ML Office Hours 15 | 23MAY2021","url":"https://theartistsofdatascience.fireside.fm/comet-ml-15","content_text":"Checkout the episode recap here: https://www.comet.ml/site/comet-office-hours-recap-for-may-23rd-and-may-30th/\n\nComet provides a self-hosted and cloud-based meta machine learning platform allowing data scientists and teams to track, compare, explain and optimize experiments and models.\n\nBacked by thousands of users and multiple Fortune 100 companies, Comet provides insights and data to build better, more accurate AI models while improving productivity, collaboration and visibility across teams.\n\nRegister for future sessions here: http://bit.ly/comet-ml-oh\n\nCheckout Comet ML by visiting: https://www.comet.ml/\n\nCheckout the latest FREE e-book from Comet - Building Effective Machine Learning Teams: https://bit.ly/3bWrJ0O\n\nOr on Twitter: https://twitter.com/CometML\n\nOn YouTube: https://www.youtube.com/channel/UCmN63HKvfXSCS-UwVwmK8Hw\n\nVote in the data community content creators awards! http://bit.ly/data-creators-awards\n\nCheck it out and don't forget to register for Friday Happy Hour sessions: http://bit.ly/adsoh\n\nWatch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm\n\nCheckout the interview I had with the Super Data Science Podcast: https://www.superdatascience.com/podcast/landing-your-data-science-dream-job\n\n[00:01:08] How to deal with the confusion you face while learning new things\n\n[00:04:09] Dealing with failed data science projects\n\n[00:06:58] How do you go about making sure you collect the right kind of data in the first place? \n\n[00:09:29] Start with three questions\n\n[00:11:59] The balance between learning technical stuff and learning how to solve actual problems\n\n[00:15:28] How are you overcoming learning struggles? \n\n[00:18:07] Learning vs doing\n\n[00:21:58] When the data doesn’t support much predictive power\n\n[00:28:04] Everyone will become a data scientist, eventually\n\n[00:30:38] The importance of domain knowledge\n\n[00:35:03] Define failure up front\n\n[00:37:56] Is low code the end of data science as we know it?\n\n[00:42:19] Reproducibility\n\n[00:48:25] Adam with some controversy\n\n[01:02:36] How do you do personal inventory on your skills?","content_html":"Checkout the episode recap here: https://www.comet.ml/site/comet-office-hours-recap-for-may-23rd-and-may-30th/
\n\nComet provides a self-hosted and cloud-based meta machine learning platform allowing data scientists and teams to track, compare, explain and optimize experiments and models.
\n\nBacked by thousands of users and multiple Fortune 100 companies, Comet provides insights and data to build better, more accurate AI models while improving productivity, collaboration and visibility across teams.
\n\nRegister for future sessions here: http://bit.ly/comet-ml-oh
\n\nCheckout Comet ML by visiting: https://www.comet.ml/
\n\nCheckout the latest FREE e-book from Comet - Building Effective Machine Learning Teams: https://bit.ly/3bWrJ0O
\n\nOr on Twitter: https://twitter.com/CometML
\n\nOn YouTube: https://www.youtube.com/channel/UCmN63HKvfXSCS-UwVwmK8Hw
\n\nVote in the data community content creators awards! http://bit.ly/data-creators-awards
\n\nCheck it out and don't forget to register for Friday Happy Hour sessions: http://bit.ly/adsoh
\n\nWatch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm
\n\nCheckout the interview I had with the Super Data Science Podcast: https://www.superdatascience.com/podcast/landing-your-data-science-dream-job
\n\n[00:01:08] How to deal with the confusion you face while learning new things
\n\n[00:04:09] Dealing with failed data science projects
\n\n[00:06:58] How do you go about making sure you collect the right kind of data in the first place?
\n\n[00:09:29] Start with three questions
\n\n[00:11:59] The balance between learning technical stuff and learning how to solve actual problems
\n\n[00:15:28] How are you overcoming learning struggles?
\n\n[00:18:07] Learning vs doing
\n\n[00:21:58] When the data doesn’t support much predictive power
\n\n[00:28:04] Everyone will become a data scientist, eventually
\n\n[00:30:38] The importance of domain knowledge
\n\n[00:35:03] Define failure up front
\n\n[00:37:56] Is low code the end of data science as we know it?
\n\n[00:42:19] Reproducibility
\n\n[00:48:25] Adam with some controversy
\n\n[01:02:36] How do you do personal inventory on your skills?
","summary":"Comet provides a self-hosted and cloud-based meta machine learning platform allowing data scientists and teams to track, compare, explain and optimize experiments and models.\r\n\r\nBacked by thousands of users and multiple Fortune 100 companies, Comet provides insights and data to build better, more accurate AI models while improving productivity, collaboration and visibility across teams.\r\n","date_published":"2021-05-27T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/956d076f-8696-4ef7-8ac9-5f2ce0e3f176.mp3","mime_type":"audio/mpeg","size_in_bytes":38416352,"duration_in_seconds":4265}]},{"id":"921c716c-b403-45bc-8d77-ca1115cbe377","title":"Data Science Happy Hour 33 | 21MAY2021","url":"https://theartistsofdatascience.fireside.fm/oh-33","content_text":"My friend Vivianne takes over for my while I celebrate my wife's birthday - Happy Birthday Romie!\n\nVote in the data community content creators awards! http://bit.ly/data-creators-awards\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh\n\nWatch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm\n\nCheckout the interview I had with the Super Data Science Podcast: https://www.superdatascience.com/podcast/landing-your-data-science-dream-job\n\n[00:00:55] Barack Obama believes in UFOs\n\n[00:06:06] How do you guys structure your week?\n\n[00:08:20] 70% is ready enough\n\n[00:13:36] At the start of your career, it’s 80/20…as you move along, it’s 50/50\n\n[00:15:23] What does career growth look like – is moving into a strategic role the only way up?\n\n[00:15:48] What career growth looks like at Verizon\n\n[00:17:59] What’s the career path look like for people who want to stick to technical work but still move up?\n\n[00:19:28] Vin talks about some awesome work experiences he had\n\n[00:21:22] Using your hands versus using your brain\n\n[00:27:06] Do I have to quit what I love doing to get paid more? \n\n[00:28:27] How do we define impact for business and does growth have to be tied to business impact itself?\n\n[00:31:33] Vin on why companies need intelligent compensation packages\n\n[00:34:09] Greg on prioritization and the decision matrix\n\n[00:37:04] Some great dialog on company culture and it’s impact on innovation\n\n[00:41:27] What are all these titles about “Staff Data Scientist”, “Principal Data Scientist”, “Distinguished Data Scientist” all about? \n\n[00:43:50] The reality of what it's like to be one of these people with an inflated title. \n\n[00:45:06] Would you say that companies are sometimes reluctant to give that title to people?\n\n[00:46:16] The highest paid data scientist that Vin has seen\n\n[00:46:53] Could it be that it is the domain that dictates that kind of range?\n\n[00:47:34] Can you pick up the culture of a company from a job description?\n\n[00:50:15] People culture vs technical culture \n\n[00:56:18] Vacation policies \n\n[00:57:38] Inspect the failure culture of an organizationSpecial Guests: Greg Coquillo and Vin Vashishta.","content_html":"My friend Vivianne takes over for my while I celebrate my wife's birthday - Happy Birthday Romie!
\n\nVote in the data community content creators awards! http://bit.ly/data-creators-awards
\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh
\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\n\nWatch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm
\n\nCheckout the interview I had with the Super Data Science Podcast: https://www.superdatascience.com/podcast/landing-your-data-science-dream-job
\n\n[00:00:55] Barack Obama believes in UFOs
\n\n[00:06:06] How do you guys structure your week?
\n\n[00:08:20] 70% is ready enough
\n\n[00:13:36] At the start of your career, it’s 80/20…as you move along, it’s 50/50
\n\n[00:15:23] What does career growth look like – is moving into a strategic role the only way up?
\n\n[00:15:48] What career growth looks like at Verizon
\n\n[00:17:59] What’s the career path look like for people who want to stick to technical work but still move up?
\n\n[00:19:28] Vin talks about some awesome work experiences he had
\n\n[00:21:22] Using your hands versus using your brain
\n\n[00:27:06] Do I have to quit what I love doing to get paid more?
\n\n[00:28:27] How do we define impact for business and does growth have to be tied to business impact itself?
\n\n[00:31:33] Vin on why companies need intelligent compensation packages
\n\n[00:34:09] Greg on prioritization and the decision matrix
\n\n[00:37:04] Some great dialog on company culture and it’s impact on innovation
\n\n[00:41:27] What are all these titles about “Staff Data Scientist”, “Principal Data Scientist”, “Distinguished Data Scientist” all about?
\n\n[00:43:50] The reality of what it's like to be one of these people with an inflated title.
\n\n[00:45:06] Would you say that companies are sometimes reluctant to give that title to people?
\n\n[00:46:16] The highest paid data scientist that Vin has seen
\n\n[00:46:53] Could it be that it is the domain that dictates that kind of range?
\n\n[00:47:34] Can you pick up the culture of a company from a job description?
\n\n[00:50:15] People culture vs technical culture
\n\n[00:56:18] Vacation policies
\n\n[00:57:38] Inspect the failure culture of an organization
Special Guests: Greg Coquillo and Vin Vashishta.
","summary":"","date_published":"2021-05-23T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/921c716c-b403-45bc-8d77-ca1115cbe377.mp3","mime_type":"audio/mpeg","size_in_bytes":32547416,"duration_in_seconds":3639}]},{"id":"0a2518ed-ee93-4519-9b29-afa5d8b5fcd1","title":"Comet ML Office Hours 14 - 16MAY2021","url":"https://theartistsofdatascience.fireside.fm/comet-ml-14","content_text":"Comet provides a self-hosted and cloud-based meta machine learning platform allowing data scientists and teams to track, compare, explain and optimize experiments and models.\n\nBacked by thousands of users and multiple Fortune 100 companies, Comet provides insights and data to build better, more accurate AI models while improving productivity, collaboration and visibility across teams.\n\nRegister for future sessions here: http://bit.ly/comet-ml-oh\n\nCheckout Comet ML by visiting: https://www.comet.ml/\n\nCheckout the latest FREE e-book from Comet - Building Effective Machine Learning Teams: https://bit.ly/3bWrJ0O\n\nOr on Twitter: https://twitter.com/CometML\n\nOn YouTube: https://www.youtube.com/channel/UCmN63HKvfXSCS-UwVwmK8Hw\n\nVote in the data community content creators awards! http://bit.ly/data-creators-awards\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh\n\nCheck it out and don't forget to register for Friday Happy Hour sessions: http://bit.ly/adsoh\n\nWatch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm\n\nCheckout the interview I had with the Super Data Science Podcast: https://www.superdatascience.com/podcast/landing-your-data-science-dream-job\n\n[00:00:50] Say hello to Austin, Comet’s new Head of Community!\n\n[00:03:03] How do you make learning fun?\n\n[00:09:20] Learning as a six-step process\n\n[00:16:42] We talk about the enigmatic Denis Rothman\n\n[00:18:17] The importance of keyboard shortcuts\n\n[00:27:46] How to handle requests to speak at events\n\n[00:40:28] We start talking about information theory somehow\n\n[00:45:08] How to become a better teacher\n\n[00:52:19] Don’t have too many tabs open in your brain!","content_html":"Comet provides a self-hosted and cloud-based meta machine learning platform allowing data scientists and teams to track, compare, explain and optimize experiments and models.
\n\nBacked by thousands of users and multiple Fortune 100 companies, Comet provides insights and data to build better, more accurate AI models while improving productivity, collaboration and visibility across teams.
\n\nRegister for future sessions here: http://bit.ly/comet-ml-oh
\n\nCheckout Comet ML by visiting: https://www.comet.ml/
\n\nCheckout the latest FREE e-book from Comet - Building Effective Machine Learning Teams: https://bit.ly/3bWrJ0O
\n\nOr on Twitter: https://twitter.com/CometML
\n\nOn YouTube: https://www.youtube.com/channel/UCmN63HKvfXSCS-UwVwmK8Hw
\n\nVote in the data community content creators awards! http://bit.ly/data-creators-awards
\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\n\nCheck it out and don't forget to register for Friday Happy Hour sessions: http://bit.ly/adsoh
\n\nWatch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm
\n\nCheckout the interview I had with the Super Data Science Podcast: https://www.superdatascience.com/podcast/landing-your-data-science-dream-job
\n\n[00:00:50] Say hello to Austin, Comet’s new Head of Community!
\n\n[00:03:03] How do you make learning fun?
\n\n[00:09:20] Learning as a six-step process
\n\n[00:16:42] We talk about the enigmatic Denis Rothman
\n\n[00:18:17] The importance of keyboard shortcuts
\n\n[00:27:46] How to handle requests to speak at events
\n\n[00:40:28] We start talking about information theory somehow
\n\n[00:45:08] How to become a better teacher
\n\n[00:52:19] Don’t have too many tabs open in your brain!
","summary":"","date_published":"2021-05-23T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/0a2518ed-ee93-4519-9b29-afa5d8b5fcd1.mp3","mime_type":"audio/mpeg","size_in_bytes":33418109,"duration_in_seconds":3777}]},{"id":"e4cf2015-89ac-465e-9545-86ec6913ec64","title":"Choose Who You Become | Chase Caprio","url":"https://theartistsofdatascience.fireside.fm/chase-caprio","content_text":"Chase is the Lead Data Analyst for Impact Theory, where he advises and makes data driven decisions for Tom Bilyeu, Impact Theory Comics, Women of Impact, and the Health Theory channels and shows\n\nFIND CHASE ONLINE\n\nInstagram: https://www.instagram.com/chaycap\n\nLinkedIn: https://www.linkedin.com/in/chasecaprio/\n\nHIGHLIGHTS FROM THE SHOW\n\n[00:02:45] We learn a bit about Chase’s background\n\n[00:04:09] What did you think your future would look like?\n\n[00:08:17] How Chase cultivated his mindset\n\n[00:10:32] How Chase got into the data and analytics world\n\n[00:12:36] What Chase’s day-to-day is like\n\n[00:16:00] Some metrics that Chase is interested in when analyzing data\n\n[00:18:24] What is an intrapreneur?\n\n[00:21:20] The Impact Theory Belief System\n\n[00:24:03] Which aspect of human cognition do you find to be the most fascinating?\n\n[00:26:39] Fight the lizard brain\n\n[00:29:06] Exercising emotional intelligence\n\n[00:32:50] The dot collector system\n\n[00:36:16] Handling imposter syndrome\n\n[00:40:18] What's the growth mindset mean to you?\n\n[00:42:49] In what ways do you think the growth mindset has changed your life and your relationship with yourself?\n\n[00:44:58] What is the false growth mindset?\n\n[00:46:14] How can we clearly identify our goals so that we know where to begin our journey?\n\n[00:47:50] Once we gain clarity on what it is that we truly want, how can we start taking the first steps to get there?\n\n[00:48:58] The Dip\n\n[00:51:57] Chase talks about his interest in philosophy\n\n[00:54:23] Do shit that’s difficult, everyday.\n\n[00:57:38] It's one hundred years in the future, what do you want to be remembered for?\n\n[00:59:01] The Random RoundSpecial Guest: Chase Caprio.","content_html":"Chase is the Lead Data Analyst for Impact Theory, where he advises and makes data driven decisions for Tom Bilyeu, Impact Theory Comics, Women of Impact, and the Health Theory channels and shows
\n\nFIND CHASE ONLINE
\n\nInstagram: https://www.instagram.com/chaycap
\n\nLinkedIn: https://www.linkedin.com/in/chasecaprio/
\n\nHIGHLIGHTS FROM THE SHOW
\n\n[00:02:45] We learn a bit about Chase’s background
\n\n[00:04:09] What did you think your future would look like?
\n\n[00:08:17] How Chase cultivated his mindset
\n\n[00:10:32] How Chase got into the data and analytics world
\n\n[00:12:36] What Chase’s day-to-day is like
\n\n[00:16:00] Some metrics that Chase is interested in when analyzing data
\n\n[00:18:24] What is an intrapreneur?
\n\n[00:21:20] The Impact Theory Belief System
\n\n[00:24:03] Which aspect of human cognition do you find to be the most fascinating?
\n\n[00:26:39] Fight the lizard brain
\n\n[00:29:06] Exercising emotional intelligence
\n\n[00:32:50] The dot collector system
\n\n[00:36:16] Handling imposter syndrome
\n\n[00:40:18] What's the growth mindset mean to you?
\n\n[00:42:49] In what ways do you think the growth mindset has changed your life and your relationship with yourself?
\n\n[00:44:58] What is the false growth mindset?
\n\n[00:46:14] How can we clearly identify our goals so that we know where to begin our journey?
\n\n[00:47:50] Once we gain clarity on what it is that we truly want, how can we start taking the first steps to get there?
\n\n[00:48:58] The Dip
\n\n[00:51:57] Chase talks about his interest in philosophy
\n\n[00:54:23] Do shit that’s difficult, everyday.
\n\n[00:57:38] It's one hundred years in the future, what do you want to be remembered for?
\n\n[00:59:01] The Random Round
Special Guest: Chase Caprio.
","summary":"","date_published":"2021-05-21T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/e4cf2015-89ac-465e-9545-86ec6913ec64.mp3","mime_type":"audio/mpeg","size_in_bytes":83633180,"duration_in_seconds":4181}]},{"id":"77b9e72f-0f4f-4277-bfc0-cd79a82571c3","title":"Data Science Happy Hour 32 | 14MAY2021","url":"https://theartistsofdatascience.fireside.fm/oh-32","content_text":"Vote in the data community content creators awards! http://bit.ly/data-creators-awards\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh\n\nWatch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm\n\nCheckout the interview I had with the Super Data Science Podcast: https://www.superdatascience.com/podcast/landing-your-data-science-dream-job\n\nHighlights from this episode\n\n[00:02:30] Vin tells us about his crazy week\n\n[00:03:40] How do you best prioritize things you're working on? \n\n[00:18:00] Cryptocurrencies, Elon Musk, and investing\n\n[00:26:34] Do different strategies, along with the sentiments that are playing around, affect how a certain crypto value is going to scale from time to time? \n\n[00:31:41] What are some things that you would do to set yourself up for success when moving to an engineering team for the first time in your career? \n\n[00:35:31] How to communicate with engineers\n\n[00:39:37] Focus on delivering something right away that's going to show your value, even if it's just something small that you think you can do in a week. \n\n[00:42:02] Learning new tools is the easiest part. Forging new relationships, managing these relationships, and maintaining them…that's what you want to focus on\n\n[00:44:11] Should data scientist act with more logic than emotion? What is that balance between logic and emotion? \n\n[00:53:19] Statistics is not actually a super hard science. \n\n[00:55:18] You have to understand that there's a lot of people that are like me that don't have natural empathy\n\n[01:01:55] Jennifer shares some great news!\n\n[01:03:44] Learning JavaSpecial Guests: Greg Coquillo, Mikiko Bazeley, Santona Tuli, PhD, and Vin Vashishta.","content_html":"Vote in the data community content creators awards! http://bit.ly/data-creators-awards
\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh
\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\n\nWatch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm
\n\nCheckout the interview I had with the Super Data Science Podcast: https://www.superdatascience.com/podcast/landing-your-data-science-dream-job
\n\nHighlights from this episode
\n\n[00:02:30] Vin tells us about his crazy week
\n\n[00:03:40] How do you best prioritize things you're working on?
\n\n[00:18:00] Cryptocurrencies, Elon Musk, and investing
\n\n[00:26:34] Do different strategies, along with the sentiments that are playing around, affect how a certain crypto value is going to scale from time to time?
\n\n[00:31:41] What are some things that you would do to set yourself up for success when moving to an engineering team for the first time in your career?
\n\n[00:35:31] How to communicate with engineers
\n\n[00:39:37] Focus on delivering something right away that's going to show your value, even if it's just something small that you think you can do in a week.
\n\n[00:42:02] Learning new tools is the easiest part. Forging new relationships, managing these relationships, and maintaining them…that's what you want to focus on
\n\n[00:44:11] Should data scientist act with more logic than emotion? What is that balance between logic and emotion?
\n\n[00:53:19] Statistics is not actually a super hard science.
\n\n[00:55:18] You have to understand that there's a lot of people that are like me that don't have natural empathy
\n\n[01:01:55] Jennifer shares some great news!
\n\n[01:03:44] Learning Java
Special Guests: Greg Coquillo, Mikiko Bazeley, Santona Tuli, PhD, and Vin Vashishta.
","summary":"","date_published":"2021-05-16T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/77b9e72f-0f4f-4277-bfc0-cd79a82571c3.mp3","mime_type":"audio/mpeg","size_in_bytes":40417113,"duration_in_seconds":4492}]},{"id":"9237cee2-47c5-4db9-b8ba-df54e70a33fa","title":"Explainable Data Science | Denis Rothman","url":"https://theartistsofdatascience.fireside.fm/denis-rothman","content_text":"Denis is an expert in explainable AI (XAI) and today he’s here to talk to us about how to explore machine learning model results, review key influencing variables and variable relationships, detect and handle bias, and ethics issues - among many, many other things.\n\nFIND DENIS ONLINE\n\nLinkedIn: https://www.linkedin.com/in/denis-rothman-0b034043/\n\nWebsite: http://www.eco-ai-horizons.com/\n\nHIGHLIGHTS FROM THE SHOW\n\n[00:01:32] Guest introduction\n\n[00:02:49] Tell us a little bit about where you grew up and what was it like there?\nyour map behind you. When I was 15, I was thinking, I want to discover the world.\n\n[00:07:08] How important do you think it is for data scientists and machine learning practitioners focus solely entirely on just math and data science, but to expose themselves to a number of different topics?\n\n[00:09:27] Where are your ideas? Where's the blueprints? What are you creating?\n\n[00:10:30] Why Denis got into artificial intelligence\n\n[00:14:06] Denis talks about his fascination with world religions and how it led to his pursuit of the truth\n\n[00:17:01] Denis has been in the game since 1978, he’s what he’s seen change and remain the same since then\n\n[00:19:28] Where Denis thinks the field is headed in the near future – but not before he tells us why he’s always scribbling math formulas all over his books\n\n[00:23:09] Denis’ problem solving triangle\n\n[00:26:08] What’s the scariest application of AI going to be?\n\n[00:33:40] What the pre-Google era was like, for all you youngins\n\n[00:35:04] The individual must be shaped, he must be made to react, in the way that our culture wants him to\n\n[00:38:57] We start going off on some interesting tangents\n\n[00:40:46] How do we ensure that we are building systems that are ethical?\n\n[00:55:31] How do you view data science machine learning? An art or purely a hard science?\n\n[00:59:12] It is s one hundred years in the future - what do you want to be remembered for?\n\n[01:02:03] The Random RoundSpecial Guest: Denis Rothamn.","content_html":"Denis is an expert in explainable AI (XAI) and today he’s here to talk to us about how to explore machine learning model results, review key influencing variables and variable relationships, detect and handle bias, and ethics issues - among many, many other things.
\n\nFIND DENIS ONLINE
\n\nLinkedIn: https://www.linkedin.com/in/denis-rothman-0b034043/
\n\nWebsite: http://www.eco-ai-horizons.com/
\n\nHIGHLIGHTS FROM THE SHOW
\n\n[00:01:32] Guest introduction
\n\n[00:02:49] Tell us a little bit about where you grew up and what was it like there?
\nyour map behind you. When I was 15, I was thinking, I want to discover the world.
[00:07:08] How important do you think it is for data scientists and machine learning practitioners focus solely entirely on just math and data science, but to expose themselves to a number of different topics?
\n\n[00:09:27] Where are your ideas? Where's the blueprints? What are you creating?
\n\n[00:10:30] Why Denis got into artificial intelligence
\n\n[00:14:06] Denis talks about his fascination with world religions and how it led to his pursuit of the truth
\n\n[00:17:01] Denis has been in the game since 1978, he’s what he’s seen change and remain the same since then
\n\n[00:19:28] Where Denis thinks the field is headed in the near future – but not before he tells us why he’s always scribbling math formulas all over his books
\n\n[00:23:09] Denis’ problem solving triangle
\n\n[00:26:08] What’s the scariest application of AI going to be?
\n\n[00:33:40] What the pre-Google era was like, for all you youngins
\n\n[00:35:04] The individual must be shaped, he must be made to react, in the way that our culture wants him to
\n\n[00:38:57] We start going off on some interesting tangents
\n\n[00:40:46] How do we ensure that we are building systems that are ethical?
\n\n[00:55:31] How do you view data science machine learning? An art or purely a hard science?
\n\n[00:59:12] It is s one hundred years in the future - what do you want to be remembered for?
\n\n[01:02:03] The Random Round
Special Guest: Denis Rothamn.
","summary":"","date_published":"2021-05-14T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/9237cee2-47c5-4db9-b8ba-df54e70a33fa.mp3","mime_type":"audio/mpeg","size_in_bytes":96468184,"duration_in_seconds":4823}]},{"id":"23fe6c2d-06e6-4851-87b2-26cfa6a1c3eb","title":"Comet ML Office Hours 13 | 09MAY2021","url":"https://theartistsofdatascience.fireside.fm/comet-ml-13","content_text":"Comet provides a self-hosted and cloud-based meta machine learning platform allowing data scientists and teams to track, compare, explain and optimize experiments and models.\n\nBacked by thousands of users and multiple Fortune 100 companies, Comet provides insights and data to build better, more accurate AI models while improving productivity, collaboration and visibility across teams.\n\nRegister for future sessions here: http://bit.ly/comet-ml-oh\n\nCheckout Comet ML by visiting: https://www.comet.ml/\n\nCheckout the latest FREE e-book from Comet - Building Effective Machine Learning Teams: https://bit.ly/3bWrJ0O\n\nOr on Twitter: https://twitter.com/CometML\n\nOn YouTube: https://www.youtube.com/channel/UCmN63HKvfXSCS-UwVwmK8Hw\n\nVote in the data community content creators awards! http://bit.ly/data-creators-awards\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh\n\nWatch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm\n\nCheckout the interview I had with the Super Data Science Podcast: https://www.superdatascience.com/podcast/landing-your-data-science-dream-job","content_html":"Comet provides a self-hosted and cloud-based meta machine learning platform allowing data scientists and teams to track, compare, explain and optimize experiments and models.
\n\nBacked by thousands of users and multiple Fortune 100 companies, Comet provides insights and data to build better, more accurate AI models while improving productivity, collaboration and visibility across teams.
\n\nRegister for future sessions here: http://bit.ly/comet-ml-oh
\n\nCheckout Comet ML by visiting: https://www.comet.ml/
\n\nCheckout the latest FREE e-book from Comet - Building Effective Machine Learning Teams: https://bit.ly/3bWrJ0O
\n\nOr on Twitter: https://twitter.com/CometML
\n\nOn YouTube: https://www.youtube.com/channel/UCmN63HKvfXSCS-UwVwmK8Hw
\n\nVote in the data community content creators awards! http://bit.ly/data-creators-awards
\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh
\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\n\nWatch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm
\n\nCheckout the interview I had with the Super Data Science Podcast: https://www.superdatascience.com/podcast/landing-your-data-science-dream-job
","summary":"","date_published":"2021-05-13T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/23fe6c2d-06e6-4851-87b2-26cfa6a1c3eb.mp3","mime_type":"audio/mpeg","size_in_bytes":32575590,"duration_in_seconds":2890}]},{"id":"08cba686-68ac-4e3a-be51-77ede0836b76","title":"Data Science Happy Hour 31 | 07MAY2021","url":"https://theartistsofdatascience.fireside.fm/oh31","content_text":"We kick this happy hour with the question - \"How do you tell people they have no clue what they're talking about?\"\n\nVote in the data community content creators awards! http://bit.ly/data-creators-awards\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh\n\nWatch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm\n\nCheckout the interview I had with the Super Data Science Podcast: https://www.superdatascience.com/podcast/landing-your-data-science-dream-job","content_html":"We kick this happy hour with the question - "How do you tell people they have no clue what they're talking about?"
\n\nVote in the data community content creators awards! http://bit.ly/data-creators-awards
\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh
\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\n\nWatch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm
\n\nCheckout the interview I had with the Super Data Science Podcast: https://www.superdatascience.com/podcast/landing-your-data-science-dream-job
","summary":"","date_published":"2021-05-09T13:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/08cba686-68ac-4e3a-be51-77ede0836b76.mp3","mime_type":"audio/mpeg","size_in_bytes":33497447,"duration_in_seconds":3441}]},{"id":"bc0e1e7e-676e-481d-ad7a-7532da7e6d54","title":"The Only Chipotle in All of India | Prachi Thakur","url":"https://theartistsofdatascience.fireside.fm/prachi-thakur","content_text":"Prachi is a diversity researcher and trainer in Tourism & Hospitality. She's got three years of interacting, collecting and documenting solo female traveller's experiences, her efforts in training are streamlined towards making travel industry more inclusive.\n\nWe have a great conversation covering topics ranging from how to connect and network with people on LinkedIn, the importance of not being weird in your communications, what it's like traveling as a solo female in India, and the diversity in a huge country like India.Special Guest: Prachi Thakur.","content_html":"Prachi is a diversity researcher and trainer in Tourism & Hospitality. She's got three years of interacting, collecting and documenting solo female traveller's experiences, her efforts in training are streamlined towards making travel industry more inclusive.
\n\nWe have a great conversation covering topics ranging from how to connect and network with people on LinkedIn, the importance of not being weird in your communications, what it's like traveling as a solo female in India, and the diversity in a huge country like India.
Special Guest: Prachi Thakur.
","summary":"","date_published":"2021-05-07T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/bc0e1e7e-676e-481d-ad7a-7532da7e6d54.mp3","mime_type":"audio/mpeg","size_in_bytes":35088413,"duration_in_seconds":3718}]},{"id":"0b6c83ff-2a08-4dc9-92fb-187f82a794e6","title":"Comet ML Office Hours 12 - 02MAY2021","url":"https://theartistsofdatascience.fireside.fm/comet-ml-12","content_text":"The best Comet Office Hour Session to date! Wow - we're picking up steam on Sundays. Be sure to tune into this one, some great conversation and discussions.\n\nRegister for future sessions here: http://bit.ly/comet-ml-oh\n\nCheckout Comet ML by visiting: https://www.comet.ml/\n\nCheckout the latest FREE e-book from Comet - Building Effective Machine Learning Teams: https://bit.ly/3bWrJ0O\n\nOr on Twitter: https://twitter.com/CometML\n\nOn YouTube: https://www.youtube.com/channel/UCmN63HKvfXSCS-UwVwmK8Hw\n\nVote in the data community content creators awards! http://bit.ly/data-creators-awards\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh\n\nWatch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm\n\nCheckout the interview I had with the Super Data Science Podcast: https://www.superdatascience.com/podcast/landing-your-data-science-dream-job\n\n[00:01:40] How to gain skill in data science\n\n[00:07:39] Where can we find data for a project?\n\n[00:09:44] Data science project ideas using open data\n\n[00:12:59] Sharing code with potential hiring managers and using good frameworks for projects\n\n[00:17:01] Do tiny projects to gain skill\n\n[00:19:16] A community member shares her experience learning data science while coming from a non-technical background\n\n[00:26:11] How do I do “research and stuff”?\n\n[00:31:43] Why you need to ask questions and not just go off and do things\n\n[00:35:27] Docker, Kubernetes, and Clouds…oh my! What does this have to do with data science? (I provide several non-satisfactory explanations)\n\n[00:45:06] What do I need to study?!\n\n[00:52:31] Networking\n\n[00:53:52] Resume review\n\n[01:05:50] What tools do I need to know for data science?","content_html":"The best Comet Office Hour Session to date! Wow - we're picking up steam on Sundays. Be sure to tune into this one, some great conversation and discussions.
\n\nRegister for future sessions here: http://bit.ly/comet-ml-oh
\n\nCheckout Comet ML by visiting: https://www.comet.ml/
\n\nCheckout the latest FREE e-book from Comet - Building Effective Machine Learning Teams: https://bit.ly/3bWrJ0O
\n\nOr on Twitter: https://twitter.com/CometML
\n\nOn YouTube: https://www.youtube.com/channel/UCmN63HKvfXSCS-UwVwmK8Hw
\n\nVote in the data community content creators awards! http://bit.ly/data-creators-awards
\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh
\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\n\nWatch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm
\n\nCheckout the interview I had with the Super Data Science Podcast: https://www.superdatascience.com/podcast/landing-your-data-science-dream-job
\n\n[00:01:40] How to gain skill in data science
\n\n[00:07:39] Where can we find data for a project?
\n\n[00:09:44] Data science project ideas using open data
\n\n[00:12:59] Sharing code with potential hiring managers and using good frameworks for projects
\n\n[00:17:01] Do tiny projects to gain skill
\n\n[00:19:16] A community member shares her experience learning data science while coming from a non-technical background
\n\n[00:26:11] How do I do “research and stuff”?
\n\n[00:31:43] Why you need to ask questions and not just go off and do things
\n\n[00:35:27] Docker, Kubernetes, and Clouds…oh my! What does this have to do with data science? (I provide several non-satisfactory explanations)
\n\n[00:45:06] What do I need to study?!
\n\n[00:52:31] Networking
\n\n[00:53:52] Resume review
\n\n[01:05:50] What tools do I need to know for data science?
","summary":"Comet provides a self-hosted and cloud-based meta machine learning platform allowing data scientists and teams to track, compare, explain and optimize experiments and models. Backed by thousands of users and multiple Fortune 100 companies, Comet provides insights and data to build better, more accurate AI models while improving productivity, collaboration and visibility across teams.\r\n","date_published":"2021-05-06T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/0b6c83ff-2a08-4dc9-92fb-187f82a794e6.mp3","mime_type":"audio/mpeg","size_in_bytes":41718698,"duration_in_seconds":4377}]},{"id":"360046a7-062d-42c1-9914-54e8c8c554b3","title":"Data Science Happy Hour 30 | 30APR2021","url":"https://theartistsofdatascience.fireside.fm/oh30","content_text":"We kick this happy hour with the question - \"Why'd you get into data science?\"\n\nThere's literally any number of jobs that we could have chosen to take on, what was it about data science that got you excited?\n\nWe also chat about the data science maturity models, time management, and more!\n\nVote in the data community content creators awards! http://bit.ly/data-creators-awards\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh\n\nWatch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm\n\nCheckout the interview I had with the Super Data Science Podcast: https://www.superdatascience.com/podcast/landing-your-data-science-dream-job","content_html":"We kick this happy hour with the question - "Why'd you get into data science?"
\n\nThere's literally any number of jobs that we could have chosen to take on, what was it about data science that got you excited?
\n\nWe also chat about the data science maturity models, time management, and more!
\n\nVote in the data community content creators awards! http://bit.ly/data-creators-awards
\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh
\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\n\nWatch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm
\n\nCheckout the interview I had with the Super Data Science Podcast: https://www.superdatascience.com/podcast/landing-your-data-science-dream-job
","summary":"","date_published":"2021-05-02T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/360046a7-062d-42c1-9914-54e8c8c554b3.mp3","mime_type":"audio/mpeg","size_in_bytes":46176190,"duration_in_seconds":4782}]},{"id":"de8efc21-f72e-4333-9128-20f76dbbd330","title":"Your Job Doesn't Define YOU | Eleanor Tweddell","url":"https://theartistsofdatascience.fireside.fm/eleanor-tweddell","content_text":"Eleanor comes on the show to help us reframe how losing your job could be the best thing that ever happened to you.\n\nShe’s got 23 years of corporate experience working for brands like Costa, RAC, Virgin Atlantic, Vodafone, in functions ranging from sales, to leadership development and corporate communications.\n\nCONNECT WITH ELEANOR ONLINE\n\nLinkedIn: https://www.linkedin.com/in/eleanor-tweddell\n\nWebsite: https://www.anotherdoor.co.uk/\n\nQUOTES\n\n[00:13:04] \"What will be, will be. That's all you can do. All you can do is the best thing. And one of the turning points for me was not to attach to outcomes.\"\n\n[00:17:49] \"Don't get too hung up about if this is the right career move, if things aren't quite working in your way, play with things.\"\n\nHIGHLIGHTS FROM THE SHOW\n\n[00:01:51] Guest introduction\n\n[00:02:58] We learn about Eleanor and where’s from\n\n[00:05:19] How different is your life now than what you had imagined it would be?\n\n[00:06:01] Why Eleanor wrote a book about why losing your job can be the best thing that ever happened to you\n\n[00:07:34] What tips can you share with the audience to help manage that initial shock of losing their job and not spiral downwards into despair?\n\n[00:12:04] How to handle the stress of anticipation when you’re waiting to hear back from a job offer\n\n[00:14:28] Control what you can during the job search process, ignore the rest\n\n[00:17:08] How to move from being stunned into inaction and start taking action\n\n[00:19:45] It’s OK to wallow, here’s how to do it productively\n\n[00:21:50] Flip your mind!\n\n[00:24:16] You’re gonna say GOOD\n\n[00:26:49] Why is it good to get out of the comfort zone and into this space that you talk about in your book called The Stretch Zone and the Stress Zone?\n\n[00:29:47] How to move past the paralysis of having so much to do\n\n[00:32:09] Start with WHY\n\n[00:35:21] Decide-o-phobia\n\n[00:36:39] Create a plan to be unstuck\n\n[00:40:43] How to answer the “Tell me about yourself” question\n\n[00:43:37] The dream job matrix\n\n[00:46:33] How to talk about why you’re making a career transition\n\n[00:48:39] What are some key elements of a good LinkedIn profile?\n\n[00:53:19] What's the LinkedIn dilemma?\n\n[00:57:06] The mindset that we need to thrive.\n\n[01:00:59] It’s 100 years in the future, what do you want to be remembered for?\n\n[01:02:17] The Random RoundSpecial Guest: Eleanor Tweddell.","content_html":"Eleanor comes on the show to help us reframe how losing your job could be the best thing that ever happened to you.
\n\nShe’s got 23 years of corporate experience working for brands like Costa, RAC, Virgin Atlantic, Vodafone, in functions ranging from sales, to leadership development and corporate communications.
\n\nCONNECT WITH ELEANOR ONLINE
\n\nLinkedIn: https://www.linkedin.com/in/eleanor-tweddell
\n\nWebsite: https://www.anotherdoor.co.uk/
\n\nQUOTES
\n\n[00:13:04] "What will be, will be. That's all you can do. All you can do is the best thing. And one of the turning points for me was not to attach to outcomes."
\n\n[00:17:49] "Don't get too hung up about if this is the right career move, if things aren't quite working in your way, play with things."
\n\nHIGHLIGHTS FROM THE SHOW
\n\n[00:01:51] Guest introduction
\n\n[00:02:58] We learn about Eleanor and where’s from
\n\n[00:05:19] How different is your life now than what you had imagined it would be?
\n\n[00:06:01] Why Eleanor wrote a book about why losing your job can be the best thing that ever happened to you
\n\n[00:07:34] What tips can you share with the audience to help manage that initial shock of losing their job and not spiral downwards into despair?
\n\n[00:12:04] How to handle the stress of anticipation when you’re waiting to hear back from a job offer
\n\n[00:14:28] Control what you can during the job search process, ignore the rest
\n\n[00:17:08] How to move from being stunned into inaction and start taking action
\n\n[00:19:45] It’s OK to wallow, here’s how to do it productively
\n\n[00:21:50] Flip your mind!
\n\n[00:24:16] You’re gonna say GOOD
\n\n[00:26:49] Why is it good to get out of the comfort zone and into this space that you talk about in your book called The Stretch Zone and the Stress Zone?
\n\n[00:29:47] How to move past the paralysis of having so much to do
\n\n[00:32:09] Start with WHY
\n\n[00:35:21] Decide-o-phobia
\n\n[00:36:39] Create a plan to be unstuck
\n\n[00:40:43] How to answer the “Tell me about yourself” question
\n\n[00:43:37] The dream job matrix
\n\n[00:46:33] How to talk about why you’re making a career transition
\n\n[00:48:39] What are some key elements of a good LinkedIn profile?
\n\n[00:53:19] What's the LinkedIn dilemma?
\n\n[00:57:06] The mindset that we need to thrive.
\n\n[01:00:59] It’s 100 years in the future, what do you want to be remembered for?
\n\n[01:02:17] The Random Round
Special Guest: Eleanor Tweddell.
","summary":"","date_published":"2021-04-30T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/de8efc21-f72e-4333-9128-20f76dbbd330.mp3","mime_type":"audio/mpeg","size_in_bytes":37884675,"duration_in_seconds":4296}]},{"id":"d7c00d74-bd6c-4931-8406-846e9462dd36","title":"Comet ML Office Hours 11 - 25APRIL2021","url":"https://theartistsofdatascience.fireside.fm/comet-ml-11","content_text":"Comet provides a self-hosted and cloud-based meta machine learning platform allowing data scientists and teams to track, compare, explain and optimize experiments and models. Backed by thousands of users and multiple Fortune 100 companies, Comet provides insights and data to build better, more accurate AI models while improving productivity, collaboration and visibility across teams.\n\nRegister for future sessions here: http://bit.ly/comet-ml-oh\n\nCheckout Comet ML by visiting: https://www.comet.ml/\n\nCheckout the latest FREE e-book from Comet - Building Effective Machine Learning Teams: https://bit.ly/3bWrJ0O\n\nOr on Twitter: https://twitter.com/CometML\n\nOn YouTube: https://www.youtube.com/channel/UCmN63HKvfXSCS-UwVwmK8Hw\n\nVote in the data community content creators awards! http://bit.ly/data-creators-awards\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh\n\nWatch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm\n\nCheckout the interview I had with the Super Data Science Podcast: https://www.superdatascience.com/podcast/landing-your-data-science-dream-job","content_html":"Comet provides a self-hosted and cloud-based meta machine learning platform allowing data scientists and teams to track, compare, explain and optimize experiments and models. Backed by thousands of users and multiple Fortune 100 companies, Comet provides insights and data to build better, more accurate AI models while improving productivity, collaboration and visibility across teams.
\n\nRegister for future sessions here: http://bit.ly/comet-ml-oh
\n\nCheckout Comet ML by visiting: https://www.comet.ml/
\n\nCheckout the latest FREE e-book from Comet - Building Effective Machine Learning Teams: https://bit.ly/3bWrJ0O
\n\nOr on Twitter: https://twitter.com/CometML
\n\nOn YouTube: https://www.youtube.com/channel/UCmN63HKvfXSCS-UwVwmK8Hw
\n\nVote in the data community content creators awards! http://bit.ly/data-creators-awards
\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh
\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\n\nWatch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm
\n\nCheckout the interview I had with the Super Data Science Podcast: https://www.superdatascience.com/podcast/landing-your-data-science-dream-job
","summary":"Comet provides a self-hosted and cloud-based meta machine learning platform allowing data scientists and teams to track, compare, explain and optimize experiments and models. Backed by thousands of users and multiple Fortune 100 companies, Comet provides insights and data to build better, more accurate AI models while improving productivity, collaboration and visibility across teams.","date_published":"2021-04-29T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/d7c00d74-bd6c-4931-8406-846e9462dd36.mp3","mime_type":"audio/mpeg","size_in_bytes":38515500,"duration_in_seconds":4028}]},{"id":"99bb177e-869a-4dcd-8266-21551ca92557","title":"Data Science Happy Hour 29 | 23APR2021","url":"https://theartistsofdatascience.fireside.fm/oh29","content_text":"We kick this happy hour with the question - \"Why'd you get into data science?\"\n\nThere's literally any number of jobs that we could have chosen to take on, what was it about data science that got you excited?\n\nWe also chat about the data science maturity models, time management, and more!\n\nVote in the data community content creators awards! http://bit.ly/data-creators-awards\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh\n\nWatch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm\n\nCheckout the interview I had with the Super Data Science Podcast: https://www.superdatascience.com/podcast/landing-your-data-science-dream-jobSpecial Guests: Greg Coquillo and Vin Vashishta.","content_html":"We kick this happy hour with the question - "Why'd you get into data science?"
\n\nThere's literally any number of jobs that we could have chosen to take on, what was it about data science that got you excited?
\n\nWe also chat about the data science maturity models, time management, and more!
\n\nVote in the data community content creators awards! http://bit.ly/data-creators-awards
\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh
\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\n\nWatch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm
\n\nCheckout the interview I had with the Super Data Science Podcast: https://www.superdatascience.com/podcast/landing-your-data-science-dream-job
Special Guests: Greg Coquillo and Vin Vashishta.
","summary":"We kick this happy hour with the question - \"Why'd you get into data science?\"\r\n\r\nThere's literally any number of jobs that we could have chosen to take on, what was it about data science that got you excited?\r\n\r\nWe also chat about the data science maturity models, time management, and more!\r\n","date_published":"2021-04-25T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/99bb177e-869a-4dcd-8266-21551ca92557.mp3","mime_type":"audio/mpeg","size_in_bytes":40505132,"duration_in_seconds":4250}]},{"id":"9b314d44-c01f-4d27-af5d-eb207487afef","title":"Pulling the Grim Trigger | Kevin Zollman","url":"https://theartistsofdatascience.fireside.fm/kevin-zollman","content_text":"Kevin is a game theorist, philosopher, and author who studies the evolution of language and the mathematics of social behavior.\n\nIn this episode, we have an entertaining and engaging conversation about game theory.\n\nCONNECT WITH KEVIN ONLINE\n\nLinkedIn: https://www.linkedin.com/in/kevinzollman/\n\nTwitter: https://twitter.com/KevinZollman\n\nQUOTES\n\n[00:14:56] \"The idea here is that when we have situations where we can cooperate for mutual benefit, that is if we all wear masks, we're cooperating, we're helping one another. \"\n\n[00:17:23] \"For a game to be a zero sum game, it needs to be that if one person wins, someone else loses.\"\n\n[00:26:47] \"One of the things game theorists know is: it's important to make it smaller rather than bigger. Because the bigger it is, the more the easier it is to sneak under the radar and cheat. And you want to create opportunity for accountability\n\n[00:32:40] \"Talking to one another is another example of a game. Especially when we're worried about circumstances where we might have an incentive to be dishonest or at least not fully honest.\"\n\n[00:49:36] \"Game theory always says you have to think about what it is the other person doing, and what are their incentives, and what are they thinking about. Because if you misunderstand what they're doing because you think that they have a different set of incentives, then you can make mistakes.\"\n\n[00:59:27] \"What game theory helps you to do is: it helps to ask what are the things you need to think about?\"\n\n[01:02:43] \"A game theorist would say the first thing you've got to do, is figure out what they value. Because you can't make any predictions about what they're going to do until you know what they want.\"\n\nHIGHLIGHTS FROM THE SHOW\n\n[00:02:50] We learn about Kevin’s background\n\n[00:05:05] What Kevin thought his future would be like\n\n[00:06:22] We talk about Kevin’s background in programming\n\n[00:07:18] How programming makes its way into the research and work he currently does\n\n[00:09:14] What is a game and why do we need theories about them anyways?\n\n[00:11:13] Is game theory evil?\n\n[00:12:42] Game theory in poker\n\n[00:14:00] The game theory of wearing masks in a pandemic \n\n[00:17:13] Is wearing a mask a zero sum game?\n\n[00:18:31] What is the prisoner’s dilemma\n\n[00:23:37] Repeated prisoner’s dilemma\n\n[00:25:01] Prisoner’s dilemma and engagement pods on social media\n\n[00:27:51] Pulling the grim trigger\n\n[00:29:23] Is game theory unique to humans?\n\n[00:32:15] How is game theory applied to language?\n\n[00:34:45] Do you have to know you’re in a game to be playing a game?\n\n[00:36:48] What is a Nash equilibrium?\n\n[00:40:44] What does fairness mean to a game theorist?\n\n[00:44:30] What can a game theory teach us about our own sense of fairness and morality and equality?\n\n[00:45:10] The ultimatum game\n\n[00:47:50] “You don’t play the hand, you play the man across from you.”\n\n[00:51:11] Is losing ever a winning strategy?\n\n[00:52:55] How can we use everything we've just been talking about to negotiate better job offers?\n\n[00:55:55] A hot topic of discussion in both of our households\n\n[01:01:56] Can multiple people be playing the exact same game but have different rewards? \n\n[01:03:28] A Game Theorist Guide for Parenting\n\n[01:05:36] It’s 100 years in the future, what do you want to be remembered for?\n\n[01:07:05] The Random RoundSpecial Guest: Kevin Zollman.","content_html":"Kevin is a game theorist, philosopher, and author who studies the evolution of language and the mathematics of social behavior.
\n\nIn this episode, we have an entertaining and engaging conversation about game theory.
\n\nCONNECT WITH KEVIN ONLINE
\n\nLinkedIn: https://www.linkedin.com/in/kevinzollman/
\n\nTwitter: https://twitter.com/KevinZollman
\n\nQUOTES
\n\n[00:14:56] "The idea here is that when we have situations where we can cooperate for mutual benefit, that is if we all wear masks, we're cooperating, we're helping one another. "
\n\n[00:17:23] "For a game to be a zero sum game, it needs to be that if one person wins, someone else loses."
\n\n[00:26:47] "One of the things game theorists know is: it's important to make it smaller rather than bigger. Because the bigger it is, the more the easier it is to sneak under the radar and cheat. And you want to create opportunity for accountability
\n\n[00:32:40] "Talking to one another is another example of a game. Especially when we're worried about circumstances where we might have an incentive to be dishonest or at least not fully honest."
\n\n[00:49:36] "Game theory always says you have to think about what it is the other person doing, and what are their incentives, and what are they thinking about. Because if you misunderstand what they're doing because you think that they have a different set of incentives, then you can make mistakes."
\n\n[00:59:27] "What game theory helps you to do is: it helps to ask what are the things you need to think about?"
\n\n[01:02:43] "A game theorist would say the first thing you've got to do, is figure out what they value. Because you can't make any predictions about what they're going to do until you know what they want."
\n\nHIGHLIGHTS FROM THE SHOW
\n\n[00:02:50] We learn about Kevin’s background
\n\n[00:05:05] What Kevin thought his future would be like
\n\n[00:06:22] We talk about Kevin’s background in programming
\n\n[00:07:18] How programming makes its way into the research and work he currently does
\n\n[00:09:14] What is a game and why do we need theories about them anyways?
\n\n[00:11:13] Is game theory evil?
\n\n[00:12:42] Game theory in poker
\n\n[00:14:00] The game theory of wearing masks in a pandemic
\n\n[00:17:13] Is wearing a mask a zero sum game?
\n\n[00:18:31] What is the prisoner’s dilemma
\n\n[00:23:37] Repeated prisoner’s dilemma
\n\n[00:25:01] Prisoner’s dilemma and engagement pods on social media
\n\n[00:27:51] Pulling the grim trigger
\n\n[00:29:23] Is game theory unique to humans?
\n\n[00:32:15] How is game theory applied to language?
\n\n[00:34:45] Do you have to know you’re in a game to be playing a game?
\n\n[00:36:48] What is a Nash equilibrium?
\n\n[00:40:44] What does fairness mean to a game theorist?
\n\n[00:44:30] What can a game theory teach us about our own sense of fairness and morality and equality?
\n\n[00:45:10] The ultimatum game
\n\n[00:47:50] “You don’t play the hand, you play the man across from you.”
\n\n[00:51:11] Is losing ever a winning strategy?
\n\n[00:52:55] How can we use everything we've just been talking about to negotiate better job offers?
\n\n[00:55:55] A hot topic of discussion in both of our households
\n\n[01:01:56] Can multiple people be playing the exact same game but have different rewards?
\n\n[01:03:28] A Game Theorist Guide for Parenting
\n\n[01:05:36] It’s 100 years in the future, what do you want to be remembered for?
\n\n[01:07:05] The Random Round
Special Guest: Kevin Zollman.
","summary":"","date_published":"2021-04-23T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/9b314d44-c01f-4d27-af5d-eb207487afef.mp3","mime_type":"audio/mpeg","size_in_bytes":91719123,"duration_in_seconds":4585}]},{"id":"2955c54d-4501-4afa-a17d-b8d025d2d8b9","title":"Comet ML Office Hours 10 - 17APRIL2021","url":"https://theartistsofdatascience.fireside.fm/comet-ml-10","content_text":"Comet provides a self-hosted and cloud-based meta machine learning platform allowing data scientists and teams to track, compare, explain and optimize experiments and models. Backed by thousands of users and multiple Fortune 100 companies, Comet provides insights and data to build better, more accurate AI models while improving productivity, collaboration and visibility across teams.\n\nRegister for future sessions here: http://bit.ly/comet-ml-oh\n\nCheckout Comet ML by visiting: https://www.comet.ml/\n\nCheckout the latest FREE e-book from Comet - Building Effective Machine Learning Teams: https://bit.ly/3bWrJ0O\n\nOr on Twitter: https://twitter.com/CometML\n\nOn YouTube: https://www.youtube.com/channel/UCmN63HKvfXSCS-UwVwmK8Hw\n\nConnect with Ayodele \n\nLinkedIn: https://www.linkedin.com/in/ayodeleodubela/ \n\nTwitter: https://twitter.com/DataSciBae\n\nCheck out her course on LinkedIn Learning: https://www.linkedin.com/learning/supervised-learning-essential-training/supervised-machine-learning-and-the-technology-boom","content_html":"Comet provides a self-hosted and cloud-based meta machine learning platform allowing data scientists and teams to track, compare, explain and optimize experiments and models. Backed by thousands of users and multiple Fortune 100 companies, Comet provides insights and data to build better, more accurate AI models while improving productivity, collaboration and visibility across teams.
\n\nRegister for future sessions here: http://bit.ly/comet-ml-oh
\n\nCheckout Comet ML by visiting: https://www.comet.ml/
\n\nCheckout the latest FREE e-book from Comet - Building Effective Machine Learning Teams: https://bit.ly/3bWrJ0O
\n\nOr on Twitter: https://twitter.com/CometML
\n\nOn YouTube: https://www.youtube.com/channel/UCmN63HKvfXSCS-UwVwmK8Hw
\n\nConnect with Ayodele
\n\nLinkedIn: https://www.linkedin.com/in/ayodeleodubela/
\n\nTwitter: https://twitter.com/DataSciBae
\n\nCheck out her course on LinkedIn Learning: https://www.linkedin.com/learning/supervised-learning-essential-training/supervised-machine-learning-and-the-technology-boom
","summary":"Comet provides a self-hosted and cloud-based meta machine learning platform allowing data scientists and teams to track, compare, explain and optimize experiments and models. Backed by thousands of users and multiple Fortune 100 companies, Comet provides insights and data to build better, more accurate AI models while improving productivity, collaboration and visibility across teams.","date_published":"2021-04-22T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/2955c54d-4501-4afa-a17d-b8d025d2d8b9.mp3","mime_type":"audio/mpeg","size_in_bytes":41120075,"duration_in_seconds":4289}]},{"id":"27c50072-9bef-433c-be46-6558b0c14cff","title":"Data Science Happy Hour 28 | 16APR2021","url":"https://theartistsofdatascience.fireside.fm/oh-28","content_text":"This happy hour session was a heavy discussion. It's sad that we need to talk about this in this day in age, but it's only gonna get better if we raise awareness and have the courage to participate and share these discussions.\n\nVote in the data community content creators awards! http://bit.ly/data-creators-awards\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh\n\nWatch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm\n\nCheckout the interview I had with the Super Data Science Podcast: https://www.superdatascience.com/podcast/landing-your-data-science-dream-jobSpecial Guests: Greg Coquillo, Mikiko Bazeley, and Vin Vashishta.","content_html":"This happy hour session was a heavy discussion. It's sad that we need to talk about this in this day in age, but it's only gonna get better if we raise awareness and have the courage to participate and share these discussions.
\n\nVote in the data community content creators awards! http://bit.ly/data-creators-awards
\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh
\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\n\nWatch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm
\n\nCheckout the interview I had with the Super Data Science Podcast: https://www.superdatascience.com/podcast/landing-your-data-science-dream-job
Special Guests: Greg Coquillo, Mikiko Bazeley, and Vin Vashishta.
","summary":"","date_published":"2021-04-18T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/27c50072-9bef-433c-be46-6558b0c14cff.mp3","mime_type":"audio/mpeg","size_in_bytes":39865621,"duration_in_seconds":4237}]},{"id":"d520f9d5-db67-49ad-8b35-fb951810f0a5","title":"How to Build a Data Science Culture | John K Thompson","url":"https://theartistsofdatascience.fireside.fm/john-k-thompson","content_text":"John is an international technology executive with over 30 years of experience in the business intelligence and advanced analytics fields. \n\nHe has experience building start-up organizations from the ground up and has reengineered business units of Fortune 500 firms to enable them to reach their full potential.\n\nFIND JOHN ONLINE\n\nLinkedIn: https://www.linkedin.com/in/johnkthompson/\n\nTwitter: https://twitter.com/johnkthompson60\n\nWebsite: https://mktng-sciences.com/\n\nBook Website: http://www.winningwithintelligence.com/\n\nQUOTES\n\n[00:07:27] \"We, as data scientists, understand data drift and model drift very well. But most people outside the field don't. So they say is: 'I hired these people. They built a model. That should be the end of it. It should work into the future and forever. And as we know, they don't\"\n\n[00:15:16] \"Oh, it's definitely an art. There's no doubt about it...data science is a creative endeavor. It is an artistic endeavor.\"\n\n[00:26:22] \"And I've said it many times. You probably heard me say it before. We are Data scientists. We are not magicians. We can't just make stuff up. We have to have something to work with..\"\n\n[00:30:02] \"An open mindset is really, in my opinion, is often characterized and exhibited by a voracious curiosity.\"\n\n[00:31:34] \" I've often seen people through my career - and they generally don't do very well in data science - that they think they know everything. And they think they know how to approach every problem...\" \n\nHIGHLIGHTS FROM THE SHOW\n\n[00:01:27] Guest introduction\n\n[00:02:51] A general data science problem solving framework\n\n[00:08:48] Artisanal vs factory data science teams\n\n[00:13:23] How does the artisanal or factory culture happen on data science teams?\n\n[00:15:13] Is Data science and art or science? How do you view it?\n\n[00:17:47] The creative process in data science\n\n[00:20:21] Phase separation in data science\n\n[00:22:10] How to manage executive expectation when a fair chunk of data science is research\n\n[00:24:47] Help us understand what is a good idea? What is a bad idea? What separates the two?\n\n[00:28:02] What to do when you’re working on a problem but nothing seems to work\n\n[00:29:35] Open mindset vs fixed mindset\n\n[00:32:50] Ditch the map, use a compass\n\nthe story? Yeah, we do think there's a there's a future in those computers.\n\n[00:35:39] Where do you see the field of Data things headed in the next two to five years?\n\n[00:38:47] What was your journey like going from an individual contributor to executive level?\n\n[00:41:17] What makes analytics so unique?\n\n[00:42:33] How would you handle that situation where you have somebody who thinks they know your job and is trying to tell you what to do and not to do?\n\n[00:45:57] Learn to build, learn to sell.\n\n[00:48:01] How to become a better communicator\n\n[00:51:06] As the first data scientist in an organization, how can we ensure that we're building or at least cultivating a culture for analytics to thrive?\n\n[00:53:31] How can we balance and this creative, iterative, unpredictable process of analytic discovery with those environments that have these operational or production process-oriented characteristics?\n\n[00:55:59] Does agile development practices work on data science teams\n\n[00:58:20] Linear and non-linear thinking\n\n[01:01:23] Where on the org chart do data science teams belong?\n\n[01:04:23] What are some unreasonable expectations that executives and management have of startup data science teams?\n\n[01:08:15] It is one hundred years in the future, what do you want to be remembered for?\n\n[01:08:46] The Random RoundSpecial Guest: John K Thompson.","content_html":"John is an international technology executive with over 30 years of experience in the business intelligence and advanced analytics fields.
\n\nHe has experience building start-up organizations from the ground up and has reengineered business units of Fortune 500 firms to enable them to reach their full potential.
\n\nFIND JOHN ONLINE
\n\nLinkedIn: https://www.linkedin.com/in/johnkthompson/
\n\nTwitter: https://twitter.com/johnkthompson60
\n\nWebsite: https://mktng-sciences.com/
\n\nBook Website: http://www.winningwithintelligence.com/
\n\nQUOTES
\n\n[00:07:27] "We, as data scientists, understand data drift and model drift very well. But most people outside the field don't. So they say is: 'I hired these people. They built a model. That should be the end of it. It should work into the future and forever. And as we know, they don't"
\n\n[00:15:16] "Oh, it's definitely an art. There's no doubt about it...data science is a creative endeavor. It is an artistic endeavor."
\n\n[00:26:22] "And I've said it many times. You probably heard me say it before. We are Data scientists. We are not magicians. We can't just make stuff up. We have to have something to work with.."
\n\n[00:30:02] "An open mindset is really, in my opinion, is often characterized and exhibited by a voracious curiosity."
\n\n[00:31:34] " I've often seen people through my career - and they generally don't do very well in data science - that they think they know everything. And they think they know how to approach every problem..."
\n\nHIGHLIGHTS FROM THE SHOW
\n\n[00:01:27] Guest introduction
\n\n[00:02:51] A general data science problem solving framework
\n\n[00:08:48] Artisanal vs factory data science teams
\n\n[00:13:23] How does the artisanal or factory culture happen on data science teams?
\n\n[00:15:13] Is Data science and art or science? How do you view it?
\n\n[00:17:47] The creative process in data science
\n\n[00:20:21] Phase separation in data science
\n\n[00:22:10] How to manage executive expectation when a fair chunk of data science is research
\n\n[00:24:47] Help us understand what is a good idea? What is a bad idea? What separates the two?
\n\n[00:28:02] What to do when you’re working on a problem but nothing seems to work
\n\n[00:29:35] Open mindset vs fixed mindset
\n\n[00:32:50] Ditch the map, use a compass
\n\nthe story? Yeah, we do think there's a there's a future in those computers.
\n\n[00:35:39] Where do you see the field of Data things headed in the next two to five years?
\n\n[00:38:47] What was your journey like going from an individual contributor to executive level?
\n\n[00:41:17] What makes analytics so unique?
\n\n[00:42:33] How would you handle that situation where you have somebody who thinks they know your job and is trying to tell you what to do and not to do?
\n\n[00:45:57] Learn to build, learn to sell.
\n\n[00:48:01] How to become a better communicator
\n\n[00:51:06] As the first data scientist in an organization, how can we ensure that we're building or at least cultivating a culture for analytics to thrive?
\n\n[00:53:31] How can we balance and this creative, iterative, unpredictable process of analytic discovery with those environments that have these operational or production process-oriented characteristics?
\n\n[00:55:59] Does agile development practices work on data science teams
\n\n[00:58:20] Linear and non-linear thinking
\n\n[01:01:23] Where on the org chart do data science teams belong?
\n\n[01:04:23] What are some unreasonable expectations that executives and management have of startup data science teams?
\n\n[01:08:15] It is one hundred years in the future, what do you want to be remembered for?
\n\n[01:08:46] The Random Round
Special Guest: John K Thompson.
","summary":"","date_published":"2021-04-16T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/d520f9d5-db67-49ad-8b35-fb951810f0a5.mp3","mime_type":"audio/mpeg","size_in_bytes":44810990,"duration_in_seconds":4480}]},{"id":"6b82be34-3ff9-4df0-a43b-72333bdefb96","title":"ONE YEAR ANNIVERSARY EXTRAVAGANZA! ","url":"https://theartistsofdatascience.fireside.fm/one-year-special","content_text":"TRANSCRIPT, SHOW NOTES, AND COUPON CODES WILL BE ADDED SOON! I'M HELLA BUSY THIS WEEKEND!\n\nAndrew Jones, https://data-science-infinity.teachable.com/, CODE: HARPREET_IS_THE_MAN\n\n50% OFF OF COURSES FROM KATE: https://datacated.com, CODE: HARPREET_IS_THE_MAN\n\n50% OFF OF COURSES FROM GEORGE FIRICAN: https://www.lightsondata.com/, CODE: HARPREET_IS_THE_MAN\n\nFREE COURSE FORM KEN JEE: https://www.udemy.com/course/how-to-start-a-career-in-data-science/?couponCode=HAPPY-1YEAR\nPromo code: HAPPY-1YEAR\n\nDAVE'S HOOKING US UP WITH 50% OFF HIS COURSE! https://bit.ly/FromExcelToMLBundle, 50% Code: HARPREET_IS_THE_MAN\n\nVote in the data community content creators awards! http://bit.ly/data-creators-awards\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh\n\nWatch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm\n\nCheckout the interview I had with the Super Data Science Podcast: https://www.superdatascience.com/podcast/landing-your-data-science-dream-jobSpecial Guests: Brandon Quach, PhD, Carlos Mercado, Greg Coquillo, and Kate Strachnyi.","content_html":"TRANSCRIPT, SHOW NOTES, AND COUPON CODES WILL BE ADDED SOON! I'M HELLA BUSY THIS WEEKEND!
\n\nAndrew Jones, https://data-science-infinity.teachable.com/, CODE: HARPREET_IS_THE_MAN
\n\n50% OFF OF COURSES FROM KATE: https://datacated.com, CODE: HARPREET_IS_THE_MAN
\n\n50% OFF OF COURSES FROM GEORGE FIRICAN: https://www.lightsondata.com/, CODE: HARPREET_IS_THE_MAN
\n\nFREE COURSE FORM KEN JEE: https://www.udemy.com/course/how-to-start-a-career-in-data-science/?couponCode=HAPPY-1YEAR
\nPromo code: HAPPY-1YEAR
DAVE'S HOOKING US UP WITH 50% OFF HIS COURSE! https://bit.ly/FromExcelToMLBundle, 50% Code: HARPREET_IS_THE_MAN
\n\nVote in the data community content creators awards! http://bit.ly/data-creators-awards
\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh
\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\n\nWatch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm
\n\nCheckout the interview I had with the Super Data Science Podcast: https://www.superdatascience.com/podcast/landing-your-data-science-dream-job
Special Guests: Brandon Quach, PhD, Carlos Mercado, Greg Coquillo, and Kate Strachnyi.
","summary":"","date_published":"2021-04-11T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/6b82be34-3ff9-4df0-a43b-72333bdefb96.mp3","mime_type":"audio/mpeg","size_in_bytes":54927630,"duration_in_seconds":5328}]},{"id":"010e505d-c745-410e-b9b4-d947e1f0de3d","title":"Meditations on Power and Mastery | Robert Greene","url":"https://theartistsofdatascience.fireside.fm/robert-greene","content_text":"Robert is the legendary number one New York Times best-selling author of six books exploring topics ranging from the nature of power, to strategies for war, to seduction, and the development of mastery.\n\nFIND ROBERT ONLINE\n\nTWITTER: https://twitter.com/robertgreene\n\nINSTAGRAM: https://www.instagram.com/robertgreeneofficial\n\nQUOTES\n\n[00:11:36] \"The world that we all take for granted was built by people with ambition. And so there's nothing selfish about it. It's the desire and the want to create things to contribute to society.\"\n\n[00:16:03] \"We are seeing an epidemic of irrationality, of people who have completely loosened themselves from science, from reality, from truth, you know, and it's frightening. \"\n\n[00:24:28] \"That's what all great thinkers and entrepreneurs innovators do. They simply imagine something else. They imagine other possibilities in the world.\"\n\n[00:42:06] \"Don't be afraid of that. Don't be afraid to fail. Don't be afraid to get a little bit lost, as long as you have an overall sense of direction.\"\n\n[00:50:16] \"It takes so much more minutia. It's a more arduous process to get to that stage of creativity.\"\n\n[00:52:15] \"And second of all, that reading my books can actually change who you are, can change how you think and how you look at the world.\"\n\nHIGHLIGHTS FROM THE SHOW\n\n[00:01:21] Guest introduction\n\n[00:02:31] We learn about where Robert is from\n\n[00:04:07] What kind of kid were you in high school?\n\n[00:05:12] Robert talks to us about some interesting experiences he had while traveling across the country at age 17\n\n[00:07:45] What Robert thought his future would look like\n\n[00:10:20] Do you ever get jealous or feel envious of people who don't have ambition?\n\n[00:14:39] How do you see history repeating itself in 2021 and beyond?\n\n[00:19:00] Which aspect of human nature is going to kind has been sending us off in this direction of irrationality and into this revolutionary type of age?\n\n[00:23:27] What would be the keys of power or the keys to power in this situation?\n\n[00:32:38] Robert suffered a stroke in 2018, he shares his progress on the road to recovery\n\n[00:34:12] How 50 Cent help inspire Robert to write Mastery – and he share stories about 50 Cent’s work ethic\n\n[00:37:26] Robert talks about the six stages of mastery\n\n[00:42:36] Robert shares some tips on how to go about finding a mentor\n\n[00:46:46] What would you say is the difference between art and science?\n\n[00:50:57] It’s 100 years in the future, what do you want to be remembered for?\n\n[00:52:56] The Random RoundSpecial Guest: Robert Greene.","content_html":"Robert is the legendary number one New York Times best-selling author of six books exploring topics ranging from the nature of power, to strategies for war, to seduction, and the development of mastery.
\n\nFIND ROBERT ONLINE
\n\nTWITTER: https://twitter.com/robertgreene
\n\nINSTAGRAM: https://www.instagram.com/robertgreeneofficial
\n\nQUOTES
\n\n[00:11:36] "The world that we all take for granted was built by people with ambition. And so there's nothing selfish about it. It's the desire and the want to create things to contribute to society."
\n\n[00:16:03] "We are seeing an epidemic of irrationality, of people who have completely loosened themselves from science, from reality, from truth, you know, and it's frightening. "
\n\n[00:24:28] "That's what all great thinkers and entrepreneurs innovators do. They simply imagine something else. They imagine other possibilities in the world."
\n\n[00:42:06] "Don't be afraid of that. Don't be afraid to fail. Don't be afraid to get a little bit lost, as long as you have an overall sense of direction."
\n\n[00:50:16] "It takes so much more minutia. It's a more arduous process to get to that stage of creativity."
\n\n[00:52:15] "And second of all, that reading my books can actually change who you are, can change how you think and how you look at the world."
\n\nHIGHLIGHTS FROM THE SHOW
\n\n[00:01:21] Guest introduction
\n\n[00:02:31] We learn about where Robert is from
\n\n[00:04:07] What kind of kid were you in high school?
\n\n[00:05:12] Robert talks to us about some interesting experiences he had while traveling across the country at age 17
\n\n[00:07:45] What Robert thought his future would look like
\n\n[00:10:20] Do you ever get jealous or feel envious of people who don't have ambition?
\n\n[00:14:39] How do you see history repeating itself in 2021 and beyond?
\n\n[00:19:00] Which aspect of human nature is going to kind has been sending us off in this direction of irrationality and into this revolutionary type of age?
\n\n[00:23:27] What would be the keys of power or the keys to power in this situation?
\n\n[00:32:38] Robert suffered a stroke in 2018, he shares his progress on the road to recovery
\n\n[00:34:12] How 50 Cent help inspire Robert to write Mastery – and he share stories about 50 Cent’s work ethic
\n\n[00:37:26] Robert talks about the six stages of mastery
\n\n[00:42:36] Robert shares some tips on how to go about finding a mentor
\n\n[00:46:46] What would you say is the difference between art and science?
\n\n[00:50:57] It’s 100 years in the future, what do you want to be remembered for?
\n\n[00:52:56] The Random Round
Special Guest: Robert Greene.
","summary":"","date_published":"2021-04-09T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/010e505d-c745-410e-b9b4-d947e1f0de3d.mp3","mime_type":"audio/mpeg","size_in_bytes":33279442,"duration_in_seconds":3390}]},{"id":"224f45cd-5f5a-4afe-8c45-e543ec31703c","title":"Comet ML Office Hours 9 - 04APR2021","url":"https://theartistsofdatascience.fireside.fm/comet-ml-9","content_text":"Comet provides a self-hosted and cloud-based meta machine learning platform allowing data scientists and teams to track, compare, explain and optimize experiments and models. Backed by thousands of users and multiple Fortune 100 companies, Comet provides insights and data to build better, more accurate AI models while improving productivity, collaboration and visibility across teams.\n\nRegister for future sessions here: http://bit.ly/comet-ml-oh\n\nCheckout Comet ML by visiting: https://www.comet.ml/\n\nCheckout the latest FREE e-book from Comet - Building Effective Machine Learning Teams: https://bit.ly/3bWrJ0O\n\nOr on Twitter: https://twitter.com/CometML\n\nOn YouTube: https://www.youtube.com/channel/UCmN63HKvfXSCS-UwVwmK8Hw\n\nConnect with Ayodele \n\nLinkedIn: https://www.linkedin.com/in/ayodeleodubela/ \n\nTwitter: https://twitter.com/DataSciBae\n\nCheck out her course on LinkedIn Learning: https://www.linkedin.com/learning/supervised-learning-essential-training/supervised-machine-learning-and-the-technology-boom\n\n[00:00:55] Do you think you need to have creativity be creative as a data scientist?\n\n[00:06:58] Some tips on how to answer technical interview questions (coding challenges) for machine learning engineer positions\n\n[00:11:29] Resume review and tips for resumes\n\n[00:20:44] Resume advice for freshers\n\n[00:28:29] Tips on how to present projects on your resume\n\n[00:31:27] Connecting with people on LinkedIn\n\n[00:35:21] The progression from individual contributor data scientists to a leadership role by way of product management\n\n[00:40:18] Ayodele talks about her experience at startups\n\n[00:42:31] Data Scientists are not like servers at a restaurant. You don't exist just to take orders and then go find things in the data to match these orders. You should be more of a trusted advisor. \n\n[00:45:00] Books to build your business acumen\n\n[00:47:36] Adopt a “founders mindset”","content_html":"Comet provides a self-hosted and cloud-based meta machine learning platform allowing data scientists and teams to track, compare, explain and optimize experiments and models. Backed by thousands of users and multiple Fortune 100 companies, Comet provides insights and data to build better, more accurate AI models while improving productivity, collaboration and visibility across teams.
\n\nRegister for future sessions here: http://bit.ly/comet-ml-oh
\n\nCheckout Comet ML by visiting: https://www.comet.ml/
\n\nCheckout the latest FREE e-book from Comet - Building Effective Machine Learning Teams: https://bit.ly/3bWrJ0O
\n\nOr on Twitter: https://twitter.com/CometML
\n\nOn YouTube: https://www.youtube.com/channel/UCmN63HKvfXSCS-UwVwmK8Hw
\n\nConnect with Ayodele
\n\nLinkedIn: https://www.linkedin.com/in/ayodeleodubela/
\n\nTwitter: https://twitter.com/DataSciBae
\n\nCheck out her course on LinkedIn Learning: https://www.linkedin.com/learning/supervised-learning-essential-training/supervised-machine-learning-and-the-technology-boom
\n\n[00:00:55] Do you think you need to have creativity be creative as a data scientist?
\n\n[00:06:58] Some tips on how to answer technical interview questions (coding challenges) for machine learning engineer positions
\n\n[00:11:29] Resume review and tips for resumes
\n\n[00:20:44] Resume advice for freshers
\n\n[00:28:29] Tips on how to present projects on your resume
\n\n[00:31:27] Connecting with people on LinkedIn
\n\n[00:35:21] The progression from individual contributor data scientists to a leadership role by way of product management
\n\n[00:40:18] Ayodele talks about her experience at startups
\n\n[00:42:31] Data Scientists are not like servers at a restaurant. You don't exist just to take orders and then go find things in the data to match these orders. You should be more of a trusted advisor.
\n\n[00:45:00] Books to build your business acumen
\n\n[00:47:36] Adopt a “founders mindset”
","summary":"Comet provides a self-hosted and cloud-based meta machine learning platform allowing data scientists and teams to track, compare, explain and optimize experiments and models. Backed by thousands of users and multiple Fortune 100 companies, Comet provides insights and data to build better, more accurate AI models while improving productivity, collaboration and visibility across teams.","date_published":"2021-04-08T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/224f45cd-5f5a-4afe-8c45-e543ec31703c.mp3","mime_type":"audio/mpeg","size_in_bytes":33670104,"duration_in_seconds":3493}]},{"id":"dc32a718-c487-4cc4-89ab-6d154d020d0b","title":"Data Science Happy Hour 26 | 02APR2021","url":"https://theartistsofdatascience.fireside.fm/oh26","content_text":"Vote in the data community content creators awards! http://bit.ly/data-creators-awards\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh\n\nWatch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm\n\nCheckout the interview I had with the Super Data Science Podcast: https://www.superdatascience.com/podcast/landing-your-data-science-dream-job\n\n[00:05:07] The difference between Docker and Virtual Environments \n\n[00:13:46] What is the best way to serve a model \n\n[00:17:02] What does Docker have to do with serving models?\n\n[00:19:34] A brief history of container technology\n\n[00:21:32] How do you best integrate Data scientist in a company? \n\n[00:28:04] Integrated data science teams\n\n[00:37:20] What is the best strategy for data scientist for navigating larger companies?\n\n[00:42:59] A question on feature selection and dimensionality reduction\n\n[01:05:43] When does it make sense for a company to hire a CDO?Special Guest: Brandon Quach, PhD.","content_html":"Vote in the data community content creators awards! http://bit.ly/data-creators-awards
\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh
\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\n\nWatch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm
\n\nCheckout the interview I had with the Super Data Science Podcast: https://www.superdatascience.com/podcast/landing-your-data-science-dream-job
\n\n[00:05:07] The difference between Docker and Virtual Environments
\n\n[00:13:46] What is the best way to serve a model
\n\n[00:17:02] What does Docker have to do with serving models?
\n\n[00:19:34] A brief history of container technology
\n\n[00:21:32] How do you best integrate Data scientist in a company?
\n\n[00:28:04] Integrated data science teams
\n\n[00:37:20] What is the best strategy for data scientist for navigating larger companies?
\n\n[00:42:59] A question on feature selection and dimensionality reduction
\n\n[01:05:43] When does it make sense for a company to hire a CDO?
Special Guest: Brandon Quach, PhD.
","summary":"","date_published":"2021-04-04T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/dc32a718-c487-4cc4-89ab-6d154d020d0b.mp3","mime_type":"audio/mpeg","size_in_bytes":47489980,"duration_in_seconds":5056}]},{"id":"cfd98835-f428-4686-9381-636777d31e5d","title":"The Future is No Degrees | Jonaed Iqbal","url":"https://theartistsofdatascience.fireside.fm/jonaed-iqbal","content_text":"Jonaed is a fierce advocate for extraordinary individuals who have chosen to bypass the traditional college route and go straight into impacting the world.\n\nHe's also the CEO of NoDegree and host of the NoDegree podcast.\n\nCONNECT WITH JONAED ONLINE\n\nLinkedIn: https://www.linkedin.com/in/jonaed/\n\nPodcast: https://www.nodegree.fm/\n\nHIGHLIGHTS FROM THE SHOW\n\n[00:02:00] We learn about where Jonaed is from\n\n[00:04:15] It's like we're entering a completely new, different economy, right? \n\n[00:04:59] What Jonaed thought his future would look like\n\n[00:06:53] Why Jonaed is all about no degrees\n\n[00:09:14] How do you think education is going to change going forward? \n\n[00:11:57] Education does not always mean school. \n\n[00:13:58] Careers that are not requiring degrees\n\n[00:17:09] What's wrong with the way that people are networking on LinkedIn?\n\n[00:20:55] How to make it easy for people to help you\n\n[00:23:24] Tips on engaging with content and messaging people\n\n[00:25:44] When it comes to LinkedIn, what are people doing wrong to get attention.\n\n[00:28:02] How to up your engagement\n\n[00:28:53] Jonaed’s process for creating LinkedIn posts\n\n[00:29:55] Become a better writer\n\n[00:32:09] We get into Jonaed’s podcast\n\n[00:34:50] Soft skills and the new economy\n\n[00:36:45] We talk about introversion and chronobiology\n\n[00:40:31] We talk smack about those viral LinkedIn posts\n\n[00:42:04] We talk about Jonaed’s business: NoDegree and common resume mistakes we see over and over again\n\n[00:46:19] Imposter syndrome\n\n[00:48:23] It’s 100 years in the future, what do you want to be remembered for?\n\n[00:49:17] The Random RoundSpecial Guest: Jonaed Iqbal.","content_html":"Jonaed is a fierce advocate for extraordinary individuals who have chosen to bypass the traditional college route and go straight into impacting the world.
\n\nHe's also the CEO of NoDegree and host of the NoDegree podcast.
\n\nCONNECT WITH JONAED ONLINE
\n\nLinkedIn: https://www.linkedin.com/in/jonaed/
\n\nPodcast: https://www.nodegree.fm/
\n\nHIGHLIGHTS FROM THE SHOW
\n\n[00:02:00] We learn about where Jonaed is from
\n\n[00:04:15] It's like we're entering a completely new, different economy, right?
\n\n[00:04:59] What Jonaed thought his future would look like
\n\n[00:06:53] Why Jonaed is all about no degrees
\n\n[00:09:14] How do you think education is going to change going forward?
\n\n[00:11:57] Education does not always mean school.
\n\n[00:13:58] Careers that are not requiring degrees
\n\n[00:17:09] What's wrong with the way that people are networking on LinkedIn?
\n\n[00:20:55] How to make it easy for people to help you
\n\n[00:23:24] Tips on engaging with content and messaging people
\n\n[00:25:44] When it comes to LinkedIn, what are people doing wrong to get attention.
\n\n[00:28:02] How to up your engagement
\n\n[00:28:53] Jonaed’s process for creating LinkedIn posts
\n\n[00:29:55] Become a better writer
\n\n[00:32:09] We get into Jonaed’s podcast
\n\n[00:34:50] Soft skills and the new economy
\n\n[00:36:45] We talk about introversion and chronobiology
\n\n[00:40:31] We talk smack about those viral LinkedIn posts
\n\n[00:42:04] We talk about Jonaed’s business: NoDegree and common resume mistakes we see over and over again
\n\n[00:46:19] Imposter syndrome
\n\n[00:48:23] It’s 100 years in the future, what do you want to be remembered for?
\n\n[00:49:17] The Random Round
Special Guest: Jonaed Iqbal.
","summary":"","date_published":"2021-04-02T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/cfd98835-f428-4686-9381-636777d31e5d.mp3","mime_type":"audio/mpeg","size_in_bytes":32551532,"duration_in_seconds":3515}]},{"id":"603e8b89-5237-42ed-926a-3253df8d328a","title":"Comet ML Office Hours 8 - 27MARCH2021","url":"https://theartistsofdatascience.fireside.fm/comet-ml-8","content_text":"Comet provides a self-hosted and cloud-based meta machine learning platform allowing data scientists and teams to track, compare, explain and optimize experiments and models. Backed by thousands of users and multiple Fortune 100 companies, Comet provides insights and data to build better, more accurate AI models while improving productivity, collaboration and visibility across teams.\n\nRegister for future sessions here: http://bit.ly/comet-ml-oh\n\nCheckout Comet ML by visiting: https://www.comet.ml/\n\nCheckout the latest FREE e-book from Comet - Building Effective Machine Learning Teams: https://bit.ly/3bWrJ0O\n\nOr on Twitter: https://twitter.com/CometML\n\nOn YouTube: https://www.youtube.com/channel/UCmN63HKvfXSCS-UwVwmK8Hw\n\nConnect with Ayodele \n\nLinkedIn: https://www.linkedin.com/in/ayodeleodubela/ \n\nTwitter: https://twitter.com/DataSciBae\n\nCheck out her course on LinkedIn Learning: https://www.linkedin.com/learning/supervised-learning-essential-training/supervised-machine-learning-and-the-technology-boom","content_html":"Comet provides a self-hosted and cloud-based meta machine learning platform allowing data scientists and teams to track, compare, explain and optimize experiments and models. Backed by thousands of users and multiple Fortune 100 companies, Comet provides insights and data to build better, more accurate AI models while improving productivity, collaboration and visibility across teams.
\n\nRegister for future sessions here: http://bit.ly/comet-ml-oh
\n\nCheckout Comet ML by visiting: https://www.comet.ml/
\n\nCheckout the latest FREE e-book from Comet - Building Effective Machine Learning Teams: https://bit.ly/3bWrJ0O
\n\nOr on Twitter: https://twitter.com/CometML
\n\nOn YouTube: https://www.youtube.com/channel/UCmN63HKvfXSCS-UwVwmK8Hw
\n\nConnect with Ayodele
\n\nLinkedIn: https://www.linkedin.com/in/ayodeleodubela/
\n\nTwitter: https://twitter.com/DataSciBae
\n\nCheck out her course on LinkedIn Learning: https://www.linkedin.com/learning/supervised-learning-essential-training/supervised-machine-learning-and-the-technology-boom
","summary":"","date_published":"2021-03-31T18:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/603e8b89-5237-42ed-926a-3253df8d328a.mp3","mime_type":"audio/mpeg","size_in_bytes":38627660,"duration_in_seconds":4034}]},{"id":"dc649284-49ab-4e2f-bfce-01e959421926","title":"Data Science Happy Hour 25 | 26MAR2021","url":"https://theartistsofdatascience.fireside.fm/oh25","content_text":"Vote in the data community content creators awards! http://bit.ly/data-creators-awards\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh\n\nWatch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjmSpecial Guests: Greg Coquillo, Kate Strachnyi, Mikiko Bazeley, and Vin Vashishta.","content_html":"Vote in the data community content creators awards! http://bit.ly/data-creators-awards
\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh
\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\n\nWatch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm
Special Guests: Greg Coquillo, Kate Strachnyi, Mikiko Bazeley, and Vin Vashishta.
","summary":"","date_published":"2021-03-28T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/dc649284-49ab-4e2f-bfce-01e959421926.mp3","mime_type":"audio/mpeg","size_in_bytes":51383167,"duration_in_seconds":5381}]},{"id":"c271e4b8-4b02-4e78-87f1-005dba7f36bd","title":"AI Ethics and the Meaning of Life | Paul Thagard","url":"https://theartistsofdatascience.fireside.fm/paul-thagard","content_text":"Paul Thagard,is a philosopher, cognitive scientist, and author of many interdisciplinary books. He is Distinguished Professor Emeritus of Philosophy at the University of Waterloo and a Fellow of the Royal Society of Canada, the Cognitive Science Society, and the Association for Psychological Science. \n\nCONNECT WITH PAUL ONLINE\n\nWebsite: https://paulthagard.com/\n\nQUOTES\n\n[00:13:55] \"The thing about the brain is it's not like a normal computer. We just make one inference at a time. We do this, this, this, this. What the brain does is make these inferences in parallel. \"\n\n[00:16:40] \"That's one of the problems. But there's problems about the past, too. We can always remember how we acted before. We don't always remember what was successful or what wasn't. We often don't learn from our mistakes. So, the past is problematic as well.\"\n\n[00:22:56] \"I thought it would be fun to keep track of ways in which people screw up, but it is just a way of putting it in a more amusing form.\"\n\nHIGHLIGHTS FROM THE SHOW\n\n[00:01:35] Guest introduction\n\n[00:02:34] We learn about Paul’s younger years\n\n[00:03:01] When you were in high school, what did you think your future would look like?\n\n[00:03:50] What kind of philosophy did you start getting into as a 15 year old?\n\n[00:04:36] What is the philosophy of science?\n\n[00:05:21] How Paul got into cognitive science\n\n[00:06:18] What is the difference between a mind a brain?\n\n[00:07:09] What’s the difference between perception and inference?\n\n[00:08:32] How is it possible for the brain to discern that what it's looking at is the one objectively true reality if all we have is our perception of it?\n\n[00:09:47] A rundown of the systems in our brain that make emotions possible\n\n[00:11:26] How does the brain perceive the body?\n\n[00:12:52] How does the brain make decisions?\n\n[00:14:58] Why do we get stuck in this analysis paralysis?\n\n[00:17:24] Why is it that we don't learn from our past as well as we should?\n\n[00:19:44] Why does the brain have trouble conceptualizing probabilities?\n\n[00:21:26] Can we “tame” our emotional reactions?\n\n[00:24:22] Why is life worth living and what is the meaning of it all?\n\n[00:28:14] A question about problem solving\n\n[00:30:05] The three aspects of the concept of intelligence. \n\n[00:33:59] The kinds of intelligence that contribute to human intelligence.\n\n[00:35:17] Growing and developing emotional intelligence\n\n[00:37:47] Marvin Minsky and 1978\n\n[00:40:07] The intelligence of recommender systems\n\n[00:41:47] How AI falls short of human intelligence\n\n[00:44:17] Intelligent animals\n\n[00:46:29] The ethics of artificial intelligence\n\n[00:51:06] Which AI ethics principle do you think is going to be of most concern to society?\n\n[00:53:25] How can we instill human values into AI systems?\n\n[00:56:17] It’s 100 years in the future, what do you want to be remembered for?\n\n[00:57:26] The Random RoundSpecial Guest: Paul Thagard.","content_html":"Paul Thagard,is a philosopher, cognitive scientist, and author of many interdisciplinary books. He is Distinguished Professor Emeritus of Philosophy at the University of Waterloo and a Fellow of the Royal Society of Canada, the Cognitive Science Society, and the Association for Psychological Science.
\n\nCONNECT WITH PAUL ONLINE
\n\nWebsite: https://paulthagard.com/
\n\nQUOTES
\n\n[00:13:55] "The thing about the brain is it's not like a normal computer. We just make one inference at a time. We do this, this, this, this. What the brain does is make these inferences in parallel. "
\n\n[00:16:40] "That's one of the problems. But there's problems about the past, too. We can always remember how we acted before. We don't always remember what was successful or what wasn't. We often don't learn from our mistakes. So, the past is problematic as well."
\n\n[00:22:56] "I thought it would be fun to keep track of ways in which people screw up, but it is just a way of putting it in a more amusing form."
\n\nHIGHLIGHTS FROM THE SHOW
\n\n[00:01:35] Guest introduction
\n\n[00:02:34] We learn about Paul’s younger years
\n\n[00:03:01] When you were in high school, what did you think your future would look like?
\n\n[00:03:50] What kind of philosophy did you start getting into as a 15 year old?
\n\n[00:04:36] What is the philosophy of science?
\n\n[00:05:21] How Paul got into cognitive science
\n\n[00:06:18] What is the difference between a mind a brain?
\n\n[00:07:09] What’s the difference between perception and inference?
\n\n[00:08:32] How is it possible for the brain to discern that what it's looking at is the one objectively true reality if all we have is our perception of it?
\n\n[00:09:47] A rundown of the systems in our brain that make emotions possible
\n\n[00:11:26] How does the brain perceive the body?
\n\n[00:12:52] How does the brain make decisions?
\n\n[00:14:58] Why do we get stuck in this analysis paralysis?
\n\n[00:17:24] Why is it that we don't learn from our past as well as we should?
\n\n[00:19:44] Why does the brain have trouble conceptualizing probabilities?
\n\n[00:21:26] Can we “tame” our emotional reactions?
\n\n[00:24:22] Why is life worth living and what is the meaning of it all?
\n\n[00:28:14] A question about problem solving
\n\n[00:30:05] The three aspects of the concept of intelligence.
\n\n[00:33:59] The kinds of intelligence that contribute to human intelligence.
\n\n[00:35:17] Growing and developing emotional intelligence
\n\n[00:37:47] Marvin Minsky and 1978
\n\n[00:40:07] The intelligence of recommender systems
\n\n[00:41:47] How AI falls short of human intelligence
\n\n[00:44:17] Intelligent animals
\n\n[00:46:29] The ethics of artificial intelligence
\n\n[00:51:06] Which AI ethics principle do you think is going to be of most concern to society?
\n\n[00:53:25] How can we instill human values into AI systems?
\n\n[00:56:17] It’s 100 years in the future, what do you want to be remembered for?
\n\n[00:57:26] The Random Round
Special Guest: Paul Thagard.
","summary":"","date_published":"2021-03-26T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/c271e4b8-4b02-4e78-87f1-005dba7f36bd.mp3","mime_type":"audio/mpeg","size_in_bytes":77743613,"duration_in_seconds":3887}]},{"id":"1ec897e9-5015-4c44-8230-e8fb47d2085e","title":"Comet ML Office Hours 7 - 21MAR2021","url":"https://theartistsofdatascience.fireside.fm/comet-ml-7","content_text":"Comet provides a self-hosted and cloud-based meta machine learning platform allowing data scientists and teams to track, compare, explain and optimize experiments and models. Backed by thousands of users and multiple Fortune 100 companies, Comet provides insights and data to build better, more accurate AI models while improving productivity, collaboration and visibility across teams.\n\nRegister for future sessions here: http://bit.ly/comet-ml-oh\n\nCheckout Comet ML by visiting: https://www.comet.ml/\n\nCheckout the latest FREE e-book from Comet - Building Effective Machine Learning Teams: https://bit.ly/3bWrJ0O\n\nOr on Twitter: https://twitter.com/CometML\n\nOn YouTube: https://www.youtube.com/channel/UCmN63HKvfXSCS-UwVwmK8Hw\n\nConnect with Ayodele \n\nLinkedIn: https://www.linkedin.com/in/ayodeleodubela/ \n\nTwitter: https://twitter.com/DataSciBae\n\nCheck out her course on LinkedIn Learning: https://www.linkedin.com/learning/supervised-learning-essential-training/supervised-machine-learning-and-the-technology-boom\n\n[00:00:52] To specialize, or to generalize?\n\n[00:05:04] What is the specific skill that you decided double down on?\n\n[00:07:12] Talking about combining skills\n\n[00:09:56] At the end of the day most data scientists are end users of Data\n\n[00:10:32] The challenges of implementing a data strategy\n\n[00:12:29] What does Data storytelling mean? \n\n[00:13:46] The pains of data strategy\n\n[00:17:24] The different flavors of data scientists\n\n[00:22:28] What does a research data scientist do?\n\n[00:24:45] Is MLOps a good direction to go if you’re not the best coder out there?\n\n[00:29:13] Wait, hold on a second what is MLOps anyway?\n\n[00:32:29] What's the biggest challenge you've come across during your whole career in data? What are the little hacks you've learned along the way?\n\n[00:42:26] What were the hang ups that that you came across, where you felt like if you tackle this early on would have made your life easier?\n\n[00:52:00] How do you deal with proving yourself once you're in a new company?\n\n[01:01:46] From insights to implementation\n\n[01:06:10] Conveying business from your projects","content_html":"Comet provides a self-hosted and cloud-based meta machine learning platform allowing data scientists and teams to track, compare, explain and optimize experiments and models. Backed by thousands of users and multiple Fortune 100 companies, Comet provides insights and data to build better, more accurate AI models while improving productivity, collaboration and visibility across teams.
\n\nRegister for future sessions here: http://bit.ly/comet-ml-oh
\n\nCheckout Comet ML by visiting: https://www.comet.ml/
\n\nCheckout the latest FREE e-book from Comet - Building Effective Machine Learning Teams: https://bit.ly/3bWrJ0O
\n\nOr on Twitter: https://twitter.com/CometML
\n\nOn YouTube: https://www.youtube.com/channel/UCmN63HKvfXSCS-UwVwmK8Hw
\n\nConnect with Ayodele
\n\nLinkedIn: https://www.linkedin.com/in/ayodeleodubela/
\n\nTwitter: https://twitter.com/DataSciBae
\n\nCheck out her course on LinkedIn Learning: https://www.linkedin.com/learning/supervised-learning-essential-training/supervised-machine-learning-and-the-technology-boom
\n\n[00:00:52] To specialize, or to generalize?
\n\n[00:05:04] What is the specific skill that you decided double down on?
\n\n[00:07:12] Talking about combining skills
\n\n[00:09:56] At the end of the day most data scientists are end users of Data
\n\n[00:10:32] The challenges of implementing a data strategy
\n\n[00:12:29] What does Data storytelling mean?
\n\n[00:13:46] The pains of data strategy
\n\n[00:17:24] The different flavors of data scientists
\n\n[00:22:28] What does a research data scientist do?
\n\n[00:24:45] Is MLOps a good direction to go if you’re not the best coder out there?
\n\n[00:29:13] Wait, hold on a second what is MLOps anyway?
\n\n[00:32:29] What's the biggest challenge you've come across during your whole career in data? What are the little hacks you've learned along the way?
\n\n[00:42:26] What were the hang ups that that you came across, where you felt like if you tackle this early on would have made your life easier?
\n\n[00:52:00] How do you deal with proving yourself once you're in a new company?
\n\n[01:01:46] From insights to implementation
\n\n[01:06:10] Conveying business from your projects
","summary":"","date_published":"2021-03-25T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/1ec897e9-5015-4c44-8230-e8fb47d2085e.mp3","mime_type":"audio/mpeg","size_in_bytes":41314171,"duration_in_seconds":4258}]},{"id":"11ee4e4a-722c-4810-869c-d1c20d94ad3d","title":"Data Science Happy Hour 24 | 19MAR2021","url":"https://theartistsofdatascience.fireside.fm/oh24","content_text":"Vote in the data community content creators awards! http://bit.ly/data-creators-awards\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh\n\nIf you want to interact with me multiple times a week, join Data Science Dream Job for 70% off: http://dsdj.co/artists70\n\nWatch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm\n\n[00:03:27] What are examples of times when you think that Data is not an appropriate solution to the problem?\n\n[00:05:08] If one is trying to force Data to solve a business problem where it doesn't belong, then that is not a great approach for any problem solving. \n\n[00:08:53] Data itself shouldn't lead to anything. \n\n[00:16:35] A question around end-to-end data projects, specifically how to approach planning one. \n\n[00:19:28] Thom Ives shares how to think through a project pipeline\n\n[00:23:39] Ben Taylor talks about how to approach a project\n\n[00:24:47] A corporate perspective for planning a project\n\n[00:28:36] Antonio with a mic drop\n\n[00:30:57] Which LinkedIn post would become an NFT in the future?\n\n[00:37:03] What is the biggest pain points in your process, that if you alleviate with a magic wand, would make your life soooo much easier?\n\n[00:38:37] Ben Taylor chimes in with some insight to this question\n\n[00:41:59] Antonio: I think this is all communication. Honestly, ninety five percent of the problems I see are related to the communication rather than technology\n\n[00:47:41] The importance of MLOps and documentation\n\n[00:51:38] As a manager for one silo, you are not understanding the other silo and therefore you're not able to communicate through language barriers. \n\n[00:53:07] Even if I know the answer, I'll still ask those questions so that I can benefit, or maybe somebody else can benefit from it. \n\n[00:56:18] Thom has a conspiracy theory about David Langer\n\n[00:56:51] What do you think is the next wave in data science?\n\n[01:04:52] Data Science is such a broad field, and I don’t know If I am I technical enough\n\n[01:06:13] Santona: I think technical ability is so vague and broad and very context specific \n\n[01:09:01] Mikiko shares some awesome advice\n\n[01:12:22] Greg: And what I can tell you is if you focus on gaining industry knowledge, you will be so comfortable with tackling what needs to be done from the technical side to solve these business problems. So the more business savvy you are, the better you can communicate with business folks, identify their problems, then you can work backwards to figure out you need technical skills that you need to solve them. \n\n[01:13:25] Specialize or generalize?\n\n[01:15:05] What are the things that school can't teach you? Special Guests: Kate Strachnyi, Kurtis Pykes, Mikiko Bazeley, and Santona Tuli, PhD.","content_html":"Vote in the data community content creators awards! http://bit.ly/data-creators-awards
\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh
\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\n\nIf you want to interact with me multiple times a week, join Data Science Dream Job for 70% off: http://dsdj.co/artists70
\n\nWatch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm
\n\n[00:03:27] What are examples of times when you think that Data is not an appropriate solution to the problem?
\n\n[00:05:08] If one is trying to force Data to solve a business problem where it doesn't belong, then that is not a great approach for any problem solving.
\n\n[00:08:53] Data itself shouldn't lead to anything.
\n\n[00:16:35] A question around end-to-end data projects, specifically how to approach planning one.
\n\n[00:19:28] Thom Ives shares how to think through a project pipeline
\n\n[00:23:39] Ben Taylor talks about how to approach a project
\n\n[00:24:47] A corporate perspective for planning a project
\n\n[00:28:36] Antonio with a mic drop
\n\n[00:30:57] Which LinkedIn post would become an NFT in the future?
\n\n[00:37:03] What is the biggest pain points in your process, that if you alleviate with a magic wand, would make your life soooo much easier?
\n\n[00:38:37] Ben Taylor chimes in with some insight to this question
\n\n[00:41:59] Antonio: I think this is all communication. Honestly, ninety five percent of the problems I see are related to the communication rather than technology
\n\n[00:47:41] The importance of MLOps and documentation
\n\n[00:51:38] As a manager for one silo, you are not understanding the other silo and therefore you're not able to communicate through language barriers.
\n\n[00:53:07] Even if I know the answer, I'll still ask those questions so that I can benefit, or maybe somebody else can benefit from it.
\n\n[00:56:18] Thom has a conspiracy theory about David Langer
\n\n[00:56:51] What do you think is the next wave in data science?
\n\n[01:04:52] Data Science is such a broad field, and I don’t know If I am I technical enough
\n\n[01:06:13] Santona: I think technical ability is so vague and broad and very context specific
\n\n[01:09:01] Mikiko shares some awesome advice
\n\n[01:12:22] Greg: And what I can tell you is if you focus on gaining industry knowledge, you will be so comfortable with tackling what needs to be done from the technical side to solve these business problems. So the more business savvy you are, the better you can communicate with business folks, identify their problems, then you can work backwards to figure out you need technical skills that you need to solve them.
\n\n[01:13:25] Specialize or generalize?
\n\n[01:15:05] What are the things that school can't teach you?
Special Guests: Kate Strachnyi, Kurtis Pykes, Mikiko Bazeley, and Santona Tuli, PhD.
","summary":"","date_published":"2021-03-21T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/11ee4e4a-722c-4810-869c-d1c20d94ad3d.mp3","mime_type":"audio/mpeg","size_in_bytes":47793226,"duration_in_seconds":5037}]},{"id":"d70ae382-8cc9-44b2-aca2-75c55a935632","title":"Comet ML Office Hours 6 - 13MARCH2021","url":"https://theartistsofdatascience.fireside.fm/comet-ml-6","content_text":"Comet provides a self-hosted and cloud-based meta machine learning platform allowing data scientists and teams to track, compare, explain and optimize experiments and models. Backed by thousands of users and multiple Fortune 100 companies, Comet provides insights and data to build better, more accurate AI models while improving productivity, collaboration and visibility across teams.\n\nRegister for future sessions here: http://bit.ly/comet-ml-oh\n\nCheckout Comet ML by visiting: https://www.comet.ml/\n\nCheckout the latest FREE e-book from Comet - Building Effective Machine Learning Teams: https://bit.ly/3bWrJ0O\n\nOr on Twitter: https://twitter.com/CometML\n\nOn YouTube: https://www.youtube.com/channel/UCmN63HKvfXSCS-UwVwmK8Hw\n\nConnect with Ayodele \n\nLinkedIn: https://www.linkedin.com/in/ayodeleodubela/ \n\nTwitter: https://twitter.com/DataSciBae\n\nCheck out her course on LinkedIn Learning: https://www.linkedin.com/learning/supervised-learning-essential-training/supervised-machine-learning-and-the-technology-boom","content_html":"Comet provides a self-hosted and cloud-based meta machine learning platform allowing data scientists and teams to track, compare, explain and optimize experiments and models. Backed by thousands of users and multiple Fortune 100 companies, Comet provides insights and data to build better, more accurate AI models while improving productivity, collaboration and visibility across teams.
\n\nRegister for future sessions here: http://bit.ly/comet-ml-oh
\n\nCheckout Comet ML by visiting: https://www.comet.ml/
\n\nCheckout the latest FREE e-book from Comet - Building Effective Machine Learning Teams: https://bit.ly/3bWrJ0O
\n\nOr on Twitter: https://twitter.com/CometML
\n\nOn YouTube: https://www.youtube.com/channel/UCmN63HKvfXSCS-UwVwmK8Hw
\n\nConnect with Ayodele
\n\nLinkedIn: https://www.linkedin.com/in/ayodeleodubela/
\n\nTwitter: https://twitter.com/DataSciBae
\n\nCheck out her course on LinkedIn Learning: https://www.linkedin.com/learning/supervised-learning-essential-training/supervised-machine-learning-and-the-technology-boom
","summary":"Comet provides a self-hosted and cloud-based meta machine learning platform allowing data scientists and teams to track, compare, explain and optimize experiments and models. Backed by thousands of users and multiple Fortune 100 companies, Comet provides insights and data to build better, more accurate AI models while improving productivity, collaboration and visibility across teams.","date_published":"2021-03-20T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/d70ae382-8cc9-44b2-aca2-75c55a935632.mp3","mime_type":"audio/mpeg","size_in_bytes":32717987,"duration_in_seconds":3401}]},{"id":"442a0151-d4c2-413e-8516-510111579e0e","title":"The Science of Successful Interviewing | Evan Pellett","url":"https://theartistsofdatascience.fireside.fm/evan-pellett","content_text":"Evan is a #1-ranked recruiter and former C level talent executive with numerous awards.\n\nHe’s taken his years of experience and distilled it into a groundbreaking hands-on, proven, scientific approach to cracking job interviews. \n\nHis book reveals a science that, when learned and practiced aggressively will allow you to go on the offensive. Controlling and creating the interview the way you want it, while answering all of the hiring manager’s questions - often before they’re asked.\n\nAnd today he’s here to share some tips with us so that we’re ready to crush our next interview!\n\nFIND EVAN ONLINE\n\nLINKEDIN: https://www.linkedin.com/in/evan-pellett-008a108/\n\nTWITTER: https://twitter.com/HowToInterview1\n\nApologies on the audio quality here - I was having microphone issues\n\nHIGHLIGHTS FROM THE SHOW\n\n[00:01:42] Guest Introduction\n\n[00:03:15] Evan talks about his early years\n\n[00:03:54] How is life different from what you thought it would be?\n\n[00:04:49] What is industrial psychology?\n\n[00:05:55] How Evan’s journey inspired his writing of his book\n\n[00:09:21] The eight-step process for a successful interview: REAP RICH\n\n[00:16:11] How do you make sure that we're demonstrating that we produced strong results in the past?\n\n[00:20:10] What can we do to make sure our energy is on point during an interview. How do we take that nervousness and make into something positive?\n\n[00:22:55] Handle your inner states\n\n[00:29:19] Tend to your inner landscape\n\n[00:32:39] How do we demonstrate our growth mindset in the interview process?\n\n[00:38:33] How can extracurricular activities help your profile in the job search? \n\n[00:39:17] Yes, definitely. And and that's a very strong piece of it, because what they want to know, what makes you work harder, be more creative.\n\n[00:40:57] A framework for telling stories and communicating results in an interview\n\n[00:47:24] The question behind the question \n\n[00:50:08] What type of questions should you ask during an interview?\n\n[00:55:13] How to ask for the job\n\n[01:00:13] The biggest thing you’re failing to do throughout your career\n\n[01:05:06] How to control the flow of an interview\n\n[01:09:03] Create connection in the interview\n\n[01:10:03] What is subtle defiance and how do we prevent ourselves from being that?\n\n[01:16:56] There are four types of hiring managers, here are their profiles?\n\n[01:22:25] It’s 100 years in the future, what do you want to be remembered for?\n\n[01:23:35] The Random RoundSpecial Guest: Evan Pellett.","content_html":"Evan is a #1-ranked recruiter and former C level talent executive with numerous awards.
\n\nHe’s taken his years of experience and distilled it into a groundbreaking hands-on, proven, scientific approach to cracking job interviews.
\n\nHis book reveals a science that, when learned and practiced aggressively will allow you to go on the offensive. Controlling and creating the interview the way you want it, while answering all of the hiring manager’s questions - often before they’re asked.
\n\nAnd today he’s here to share some tips with us so that we’re ready to crush our next interview!
\n\nFIND EVAN ONLINE
\n\nLINKEDIN: https://www.linkedin.com/in/evan-pellett-008a108/
\n\nTWITTER: https://twitter.com/HowToInterview1
\n\nApologies on the audio quality here - I was having microphone issues
\n\nHIGHLIGHTS FROM THE SHOW
\n\n[00:01:42] Guest Introduction
\n\n[00:03:15] Evan talks about his early years
\n\n[00:03:54] How is life different from what you thought it would be?
\n\n[00:04:49] What is industrial psychology?
\n\n[00:05:55] How Evan’s journey inspired his writing of his book
\n\n[00:09:21] The eight-step process for a successful interview: REAP RICH
\n\n[00:16:11] How do you make sure that we're demonstrating that we produced strong results in the past?
\n\n[00:20:10] What can we do to make sure our energy is on point during an interview. How do we take that nervousness and make into something positive?
\n\n[00:22:55] Handle your inner states
\n\n[00:29:19] Tend to your inner landscape
\n\n[00:32:39] How do we demonstrate our growth mindset in the interview process?
\n\n[00:38:33] How can extracurricular activities help your profile in the job search?
\n\n[00:39:17] Yes, definitely. And and that's a very strong piece of it, because what they want to know, what makes you work harder, be more creative.
\n\n[00:40:57] A framework for telling stories and communicating results in an interview
\n\n[00:47:24] The question behind the question
\n\n[00:50:08] What type of questions should you ask during an interview?
\n\n[00:55:13] How to ask for the job
\n\n[01:00:13] The biggest thing you’re failing to do throughout your career
\n\n[01:05:06] How to control the flow of an interview
\n\n[01:09:03] Create connection in the interview
\n\n[01:10:03] What is subtle defiance and how do we prevent ourselves from being that?
\n\n[01:16:56] There are four types of hiring managers, here are their profiles?
\n\n[01:22:25] It’s 100 years in the future, what do you want to be remembered for?
\n\n[01:23:35] The Random Round
Special Guest: Evan Pellett.
","summary":"","date_published":"2021-03-19T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/442a0151-d4c2-413e-8516-510111579e0e.mp3","mime_type":"audio/mpeg","size_in_bytes":89887207,"duration_in_seconds":5617}]},{"id":"07a16e99-b86d-4cbe-9d58-eb36e246fdf9","title":"Data Science Happy Hour 23 | 12MAR2021","url":"https://theartistsofdatascience.fireside.fm/oh23","content_text":"The Data Science Happy Hours keep getting happier! \n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh\n\nIf you want to interact with me multiple times a week, join Data Science Dream Job for 70% off: http://dsdj.co/artists70\n\nWatch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjmSpecial Guests: Greg Coquillo and Nicole Janeway Bills.","content_html":"The Data Science Happy Hours keep getting happier!
\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh
\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\n\nIf you want to interact with me multiple times a week, join Data Science Dream Job for 70% off: http://dsdj.co/artists70
\n\nWatch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm
Special Guests: Greg Coquillo and Nicole Janeway Bills.
","summary":"","date_published":"2021-03-14T00:00:00.000-05:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/07a16e99-b86d-4cbe-9d58-eb36e246fdf9.mp3","mime_type":"audio/mpeg","size_in_bytes":42749759,"duration_in_seconds":4470}]},{"id":"3752c132-9a3d-4502-b12b-fee2a41e1db7","title":"Comet ML Office Hours 5 - 07MAR2021","url":"https://theartistsofdatascience.fireside.fm/comet-ml-5","content_text":"Comet provides a self-hosted and cloud-based meta machine learning platform allowing data scientists and teams to track, compare, explain and optimize experiments and models. Backed by thousands of users and multiple Fortune 100 companies, Comet provides insights and data to build better, more accurate AI models while improving productivity, collaboration and visibility across teams.\n\nRegister for future sessions here: http://bit.ly/comet-ml-oh\n\nCheckout Comet ML by visiting: https://www.comet.ml/\n\nOr on Twitter: https://twitter.com/CometML\n\nOn YouTube: https://www.youtube.com/channel/UCmN63HKvfXSCS-UwVwmK8Hw\n\nConnect with Ayodele \n\nLinkedIn: https://www.linkedin.com/in/ayodeleodubela/ \n\nTwitter: https://twitter.com/DataSciBae\n\nCheck out her course on LinkedIn Learning: https://www.linkedin.com/learning/supervised-learning-essential-training/supervised-machine-learning-and-the-technology-boom\n\nHIGHLIGHTS FROM THE SESSION\n\n[00:02:21] What does it mean to be an expert in Data science? What's that spectrum look like to go from novice beginner all the way up to expert?\n\n[00:04:07] At what point in your career do you feel like you started to deviate from falling recipes? \n\n[00:07:41] What does it really mean to be a mentor or a mentee and anybody can become a mentor? \n\n[00:18:28] If you're going to approach somebody to be your mentor, then try to make it a positive sum game somehow. \n\n[00:21:30] How are you supposed to work with a mentor?\n\n[00:23:37] Tor has a dilemma related to cohort analysis and attribution modeling\n\n[00:32:20] Helping each other build up confidence in this crazy job search environment\n\n[00:36:06] What are some roles that that can help in getting your foot in the door before becoming a data scientist?\n\n[00:38:26] What are some interesting job titles you've seen in your search?\n\n[00:46:39] How do I ask the correct question?\n\n[00:50:38] When you meet people that are very skilled, they go directly into the complexity of the problems and they basically create so many problems. Nobody wants to listen anymore. \n\n[00:55:04] I'm consistently taking new classes and courses that I consistently keep meeting new things I don't know. How do you go about it? Is there no end there? We all go around sharing ideas for how to learn more efficiently.\n\n[01:04:52] The quest for a golden resource\n\n[01:11:24] Tips for coding round interviews\n\n[01:16:54] Do we need OOP in data science?\n\n[01:18:47] Should I go to grad school?","content_html":"Comet provides a self-hosted and cloud-based meta machine learning platform allowing data scientists and teams to track, compare, explain and optimize experiments and models. Backed by thousands of users and multiple Fortune 100 companies, Comet provides insights and data to build better, more accurate AI models while improving productivity, collaboration and visibility across teams.
\n\nRegister for future sessions here: http://bit.ly/comet-ml-oh
\n\nCheckout Comet ML by visiting: https://www.comet.ml/
\n\nOr on Twitter: https://twitter.com/CometML
\n\nOn YouTube: https://www.youtube.com/channel/UCmN63HKvfXSCS-UwVwmK8Hw
\n\nConnect with Ayodele
\n\nLinkedIn: https://www.linkedin.com/in/ayodeleodubela/
\n\nTwitter: https://twitter.com/DataSciBae
\n\nCheck out her course on LinkedIn Learning: https://www.linkedin.com/learning/supervised-learning-essential-training/supervised-machine-learning-and-the-technology-boom
\n\nHIGHLIGHTS FROM THE SESSION
\n\n[00:02:21] What does it mean to be an expert in Data science? What's that spectrum look like to go from novice beginner all the way up to expert?
\n\n[00:04:07] At what point in your career do you feel like you started to deviate from falling recipes?
\n\n[00:07:41] What does it really mean to be a mentor or a mentee and anybody can become a mentor?
\n\n[00:18:28] If you're going to approach somebody to be your mentor, then try to make it a positive sum game somehow.
\n\n[00:21:30] How are you supposed to work with a mentor?
\n\n[00:23:37] Tor has a dilemma related to cohort analysis and attribution modeling
\n\n[00:32:20] Helping each other build up confidence in this crazy job search environment
\n\n[00:36:06] What are some roles that that can help in getting your foot in the door before becoming a data scientist?
\n\n[00:38:26] What are some interesting job titles you've seen in your search?
\n\n[00:46:39] How do I ask the correct question?
\n\n[00:50:38] When you meet people that are very skilled, they go directly into the complexity of the problems and they basically create so many problems. Nobody wants to listen anymore.
\n\n[00:55:04] I'm consistently taking new classes and courses that I consistently keep meeting new things I don't know. How do you go about it? Is there no end there? We all go around sharing ideas for how to learn more efficiently.
\n\n[01:04:52] The quest for a golden resource
\n\n[01:11:24] Tips for coding round interviews
\n\n[01:16:54] Do we need OOP in data science?
\n\n[01:18:47] Should I go to grad school?
","summary":"","date_published":"2021-03-12T00:00:00.000-05:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/3752c132-9a3d-4502-b12b-fee2a41e1db7.mp3","mime_type":"audio/mpeg","size_in_bytes":48357672,"duration_in_seconds":5074}]},{"id":"cae0aaef-dfba-41ee-a2ee-53dda7847c40","title":"Improve Your Analytics with Predictive Data | Dave Kelly","url":"https://theartistsofdatascience.fireside.fm/dave-kelly-analyticsiq","content_text":"Dave started his career in 1995 at Equifax, and has since gone on to start two successful companies: Sigma Analytics in 1997 (which was acquired by Merkle) and AnalyticsIQ in 2006.\n\nAnalyticsIQ is a dynamic, fast-growing Marketing Data and Predictive Analytics company that is focused on providing innovative consumer data and analytics solutions. \n\nAnalyticsIQ is actively hiring, checkout the open positions here: https://analytics-iq.com/who-we-are/#careers\n\nGet your hands on a sample dataset which has 500 rows and 50 features, perfect for doing an EDA project. Or some unsupervised learning. Check it out here: http://bit.ly/3dJSG96\n\nConnect with Dave:\n\nTwitter: https://twitter.com/analyticsiq\n\nLinkedIn: https://www.linkedin.com/in/davekellyaiq/\n\nWebsite: https://analytics-iq.com/\n\nShoutout to Dave and the team for sponsoring this episode, it means a lot to be supported by a company with such an awesome culture and leader.\n\nIf you're interested in having your organization highlighed in an episode, reach out to me at: theartistsofdatascience@gmail.com\n\nHIGHLIGHTS FROM THE SHOW\n\n[00:01:32] Guest introduction\n\n[00:03:07] We learn about where Dave’s from \n\n[00:04:58] What Dave thought his future would look like\n\n[00:06:08] We geek out over our mutual love of astronomy\n\n[00:07:03] Astronomy books that Dave recommends\n\n[00:07:42] What is database marketing and how did you become interested in it?\n\n[00:09:03] What is compiled data?\n\n[00:09:44] How did you start AnalyticsIQ? What was the opportunity you saw in the market and how did that spark the idea for starting this company?\n\n[00:10:51] What’s the mission of AnalyticsIQ?\n\n[00:12:13] How do you create these data sets?\n\n[00:14:56] What’s the change you want to see through your work at AnalyticsIQ?\n\n[00:16:58] How do you see data scientists working inside of larger companies benefit from using external data sources?\n\n[00:18:23] How AnalyticsIQ is using cognitive behavioral science to create their data sets\n\n[00:20:10] Working with survey data\n\n[00:21:44] How to structure questions so that people give valuable answers\n\n[00:23:09] What’s so unique about the PeopleCore dataset?\n\n[00:24:52] How big is this data set?\n\n[00:25:51] How having external and survey data can benefit businesses in a pandemic\n\n[00:26:52] Pandemic personas?\n\n[00:29:05] How do we make sure that Data is managed in a safe way so that we're protecting privacy?\n\n[00:30:57] How can we use data for good?\n\n[00:32:43] How can somebody who's armed with nothing but a laptop use data and analytics for good?\n\n[00:36:37] Some examples for how you can help your local community using your data skills\n\n[00:43:39] Do you have any advice or tips for anyone who's toying with the idea of entrepreneurship?\n\n[00:46:07] What do you see as some problems worth tackling that maybe an enterprising analytics professional can seize?\n\n[00:48:00] Dave’s philanthropic work\n\n[00:49:51] It is one hundred years in the future. What do you want to be remembered for?\n\n[00:50:50] The Random RoundSpecial Guest: Dave Kelly.","content_html":"Dave started his career in 1995 at Equifax, and has since gone on to start two successful companies: Sigma Analytics in 1997 (which was acquired by Merkle) and AnalyticsIQ in 2006.
\n\nAnalyticsIQ is a dynamic, fast-growing Marketing Data and Predictive Analytics company that is focused on providing innovative consumer data and analytics solutions.
\n\nAnalyticsIQ is actively hiring, checkout the open positions here: https://analytics-iq.com/who-we-are/#careers
\n\nGet your hands on a sample dataset which has 500 rows and 50 features, perfect for doing an EDA project. Or some unsupervised learning. Check it out here: http://bit.ly/3dJSG96
\n\nConnect with Dave:
\n\nTwitter: https://twitter.com/analyticsiq
\n\nLinkedIn: https://www.linkedin.com/in/davekellyaiq/
\n\nWebsite: https://analytics-iq.com/
\n\nShoutout to Dave and the team for sponsoring this episode, it means a lot to be supported by a company with such an awesome culture and leader.
\n\nIf you're interested in having your organization highlighed in an episode, reach out to me at: theartistsofdatascience@gmail.com
\n\nHIGHLIGHTS FROM THE SHOW
\n\n[00:01:32] Guest introduction
\n\n[00:03:07] We learn about where Dave’s from
\n\n[00:04:58] What Dave thought his future would look like
\n\n[00:06:08] We geek out over our mutual love of astronomy
\n\n[00:07:03] Astronomy books that Dave recommends
\n\n[00:07:42] What is database marketing and how did you become interested in it?
\n\n[00:09:03] What is compiled data?
\n\n[00:09:44] How did you start AnalyticsIQ? What was the opportunity you saw in the market and how did that spark the idea for starting this company?
\n\n[00:10:51] What’s the mission of AnalyticsIQ?
\n\n[00:12:13] How do you create these data sets?
\n\n[00:14:56] What’s the change you want to see through your work at AnalyticsIQ?
\n\n[00:16:58] How do you see data scientists working inside of larger companies benefit from using external data sources?
\n\n[00:18:23] How AnalyticsIQ is using cognitive behavioral science to create their data sets
\n\n[00:20:10] Working with survey data
\n\n[00:21:44] How to structure questions so that people give valuable answers
\n\n[00:23:09] What’s so unique about the PeopleCore dataset?
\n\n[00:24:52] How big is this data set?
\n\n[00:25:51] How having external and survey data can benefit businesses in a pandemic
\n\n[00:26:52] Pandemic personas?
\n\n[00:29:05] How do we make sure that Data is managed in a safe way so that we're protecting privacy?
\n\n[00:30:57] How can we use data for good?
\n\n[00:32:43] How can somebody who's armed with nothing but a laptop use data and analytics for good?
\n\n[00:36:37] Some examples for how you can help your local community using your data skills
\n\n[00:43:39] Do you have any advice or tips for anyone who's toying with the idea of entrepreneurship?
\n\n[00:46:07] What do you see as some problems worth tackling that maybe an enterprising analytics professional can seize?
\n\n[00:48:00] Dave’s philanthropic work
\n\n[00:49:51] It is one hundred years in the future. What do you want to be remembered for?
\n\n[00:50:50] The Random Round
Special Guest: Dave Kelly.
","summary":"","date_published":"2021-03-11T00:00:00.000-05:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/cae0aaef-dfba-41ee-a2ee-53dda7847c40.mp3","mime_type":"audio/mpeg","size_in_bytes":35455217,"duration_in_seconds":3864}]},{"id":"c7776261-4756-481a-8dab-3183751baff9","title":"Data Science Happy Hour 22 | 05MAR2021","url":"https://theartistsofdatascience.fireside.fm/oh22","content_text":"The Data Science Happy Hours keep getting happier! \n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh\n\nIf you want to interact with me multiple times a week, join Data Science Dream Job for 70% off: http://dsdj.co/artists70\n\nWatch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm\n\nChat transcript from the session: http://theartistsofdatascience.fireside.fm/articles/oh22-chat-transcript\n\nHIGHLIGHTS FROM THE SESSION\n\n[00:02:37] What probability distributions do you need to know as a data scientist?\n\n[00:06:07] What type of course should I take as a beginner in data science?\n\n[00:12:05] How much of software development and data science is bullshit\n\n[00:15:49] The myth of job security\n\n[00:20:34] How to build an intuition for data science\n\n[00:23:17] Thom Ives with some great wisdom\n\n[00:27:52] Book recommendation on thinking and learning\n\n[00:37:24] Build scrappy solutions\n\n[00:40:23] Use statistics to move KPIs\n\n[00:43:50] How to parse the text from an HTML blob\n\n[00:49:12] Thom changes his name to “What Mikiko said”\n\n[00:50:16] We talk about our biggest fails as data and analytics professionals\n\n[01:07:39] Juico’s webscraping question \n\n[01:11:43] The Data Community Content Creator’s AwardsSpecial Guests: Carlos Mercado, Greg Coquillo, Mikiko Bazeley, and Vin Vashishta.","content_html":"The Data Science Happy Hours keep getting happier!
\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh
\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\n\nIf you want to interact with me multiple times a week, join Data Science Dream Job for 70% off: http://dsdj.co/artists70
\n\nWatch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm
\n\nChat transcript from the session: http://theartistsofdatascience.fireside.fm/articles/oh22-chat-transcript
\n\nHIGHLIGHTS FROM THE SESSION
\n\n[00:02:37] What probability distributions do you need to know as a data scientist?
\n\n[00:06:07] What type of course should I take as a beginner in data science?
\n\n[00:12:05] How much of software development and data science is bullshit
\n\n[00:15:49] The myth of job security
\n\n[00:20:34] How to build an intuition for data science
\n\n[00:23:17] Thom Ives with some great wisdom
\n\n[00:27:52] Book recommendation on thinking and learning
\n\n[00:37:24] Build scrappy solutions
\n\n[00:40:23] Use statistics to move KPIs
\n\n[00:43:50] How to parse the text from an HTML blob
\n\n[00:49:12] Thom changes his name to “What Mikiko said”
\n\n[00:50:16] We talk about our biggest fails as data and analytics professionals
\n\n[01:07:39] Juico’s webscraping question
\n\n[01:11:43] The Data Community Content Creator’s Awards
Special Guests: Carlos Mercado, Greg Coquillo, Mikiko Bazeley, and Vin Vashishta.
","summary":"","date_published":"2021-03-07T00:00:00.000-05:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/c7776261-4756-481a-8dab-3183751baff9.mp3","mime_type":"audio/mpeg","size_in_bytes":50578296,"duration_in_seconds":5341}]},{"id":"2fce7495-04d9-45c9-ae9d-e99c7fe20eea","title":"Laughter and Leadership | Sadhna Bokhiria","url":"https://theartistsofdatascience.fireside.fm/sadhna-bokhiria","content_text":"Sadhna is the Vice President of Client Services at Darwin Research Group where she gets to work with a team of superhuman data scientists to provide advanced intelligence and in-depth strategic insights to health care executives.\n\nMore interestingly though, she’s a Gelotologist - who specializes in studying the correlation between humor and intelligence. \n\nConnect with Sadhna Online:\n\nWebsite: http://sbokhiria.com/\n\nLinkedIn: https://www.linkedin.com/in/sadhna-bokhiria-ed-d-52684553/\n\nQUOTES\n\n[00:16:27] \"Genuine laughter is definitely the best in terms of your wellness because it feels great, but your body really doesn't know the difference. So, even fake laughter has positive benefits for your body.\"\n\n[00:17:54] \"I think people admire that. And I think people find something remarkable in that person who's not afraid to kind of take a chance and bet on themselves.\"\n\n[00:20:10] \"If you live in the box, you don't know any better. Ignorance is bliss. But once you're outside of the box and you can see everything, it's like you never really want to go back inside the box because that's like a lesser state of existence.\"\n\n[00:29:23] \"Failure is an inevitable part of their success. It's necessary for their growth. So, if we can take more of that into our lives and understand that you're either going to learn from it and grow and get back on stage and do better the next time; or you're just going to sink and stay in that space and and not really move forward.\"\n\n[00:36:24] \"I think that as human beings, especially artists, especially people who demand perfection, such as myself, it's debilitating. You don't want to do it unless it's perfect. And that stops you from doing it because nothing is perfect. So, recognizing that sometimes progress is better than not doing anything right. So just breaking it down into those steps and saying, look, I'm going to do this and if it works out, it works out. And if it doesn't, I did it. It's that not having that regret.\"\n\n[00:42:53] \"Data scientists are intimidating in their brilliance. Because, you know, if you're a Data scientist like you are like up there like. You're extraordinary. And it's a rock star kind of role when you read about the positions, and the companies, and the sheer power of data in itself in this day and age. So, I think maybe some advice for your listeners is: You guys are rock stars. There's hundreds of millions of people who would kill to be a data scientist, and you don't need to know everything.\" \n\nHIGHLIGHTS FROM THE SHOW\n\n[00:01:34] Guest introduction\n\n[00:02:53] We learn about where Sadhna grew up\n\n[00:06:10] We talk about identity\n\n[00:07:57] What Sadhna was like back in high school\n\n[00:09:11] The path that led Sadhna to where she is today\n\n[00:11:20] How Sadhna got so interested in studying comedians\n\n[00:16:03] The physiological benefits of laughter\n\n[00:16:45] The relationship between being humorous and intelligence\n\n[00:19:40] Can people “suffer from intelligence”?\n\n[00:22:49] How to develop your “ humor skills”\n\n[00:24:13] Are funny people more emotionally intelligent? And if so, why is that?\n\n[00:25:52] Does laughter help us improve our relationships?\n\n[00:26:52] The impact that always communicating through screens has on emotional intelligence, especially in the workplace \n\n[00:28:37] What can stand-up comedians teach us about problem solving?\n\n[00:32:15] What do you think makes a good leader?\n\n[00:35:31] Why you shouldn’t be afraid to share more of yourself and your story\n\n[00:37:49] Do we have to be in official leadership role to be a good leader or to show leadership?\n\n[00:40:18] How can we start allowing people to be the best version of themselves? \n\n[00:44:02] Do leaders have a better sense of humor?\n\n[00:45:17] What does it mean to “be authentic”?\n\n[00:47:51] Why is it more important that we keep it real in the first place now more than more than ever?\n\n[00:49:42] Can we cultivate authenticity as a trait? \n\n[00:51:47] Can we use authenticity to help us combat imposter syndrome?\n\n[00:54:06] It's one hundred years in the future, what do you want to be remembered for?\n\n[00:55:05] The Random RoundSpecial Guest: Sadhna Bokhiria.","content_html":"Sadhna is the Vice President of Client Services at Darwin Research Group where she gets to work with a team of superhuman data scientists to provide advanced intelligence and in-depth strategic insights to health care executives.
\n\nMore interestingly though, she’s a Gelotologist - who specializes in studying the correlation between humor and intelligence.
\n\nConnect with Sadhna Online:
\n\nWebsite: http://sbokhiria.com/
\n\nLinkedIn: https://www.linkedin.com/in/sadhna-bokhiria-ed-d-52684553/
\n\nQUOTES
\n\n[00:16:27] "Genuine laughter is definitely the best in terms of your wellness because it feels great, but your body really doesn't know the difference. So, even fake laughter has positive benefits for your body."
\n\n[00:17:54] "I think people admire that. And I think people find something remarkable in that person who's not afraid to kind of take a chance and bet on themselves."
\n\n[00:20:10] "If you live in the box, you don't know any better. Ignorance is bliss. But once you're outside of the box and you can see everything, it's like you never really want to go back inside the box because that's like a lesser state of existence."
\n\n[00:29:23] "Failure is an inevitable part of their success. It's necessary for their growth. So, if we can take more of that into our lives and understand that you're either going to learn from it and grow and get back on stage and do better the next time; or you're just going to sink and stay in that space and and not really move forward."
\n\n[00:36:24] "I think that as human beings, especially artists, especially people who demand perfection, such as myself, it's debilitating. You don't want to do it unless it's perfect. And that stops you from doing it because nothing is perfect. So, recognizing that sometimes progress is better than not doing anything right. So just breaking it down into those steps and saying, look, I'm going to do this and if it works out, it works out. And if it doesn't, I did it. It's that not having that regret."
\n\n[00:42:53] "Data scientists are intimidating in their brilliance. Because, you know, if you're a Data scientist like you are like up there like. You're extraordinary. And it's a rock star kind of role when you read about the positions, and the companies, and the sheer power of data in itself in this day and age. So, I think maybe some advice for your listeners is: You guys are rock stars. There's hundreds of millions of people who would kill to be a data scientist, and you don't need to know everything."
\n\nHIGHLIGHTS FROM THE SHOW
\n\n[00:01:34] Guest introduction
\n\n[00:02:53] We learn about where Sadhna grew up
\n\n[00:06:10] We talk about identity
\n\n[00:07:57] What Sadhna was like back in high school
\n\n[00:09:11] The path that led Sadhna to where she is today
\n\n[00:11:20] How Sadhna got so interested in studying comedians
\n\n[00:16:03] The physiological benefits of laughter
\n\n[00:16:45] The relationship between being humorous and intelligence
\n\n[00:19:40] Can people “suffer from intelligence”?
\n\n[00:22:49] How to develop your “ humor skills”
\n\n[00:24:13] Are funny people more emotionally intelligent? And if so, why is that?
\n\n[00:25:52] Does laughter help us improve our relationships?
\n\n[00:26:52] The impact that always communicating through screens has on emotional intelligence, especially in the workplace
\n\n[00:28:37] What can stand-up comedians teach us about problem solving?
\n\n[00:32:15] What do you think makes a good leader?
\n\n[00:35:31] Why you shouldn’t be afraid to share more of yourself and your story
\n\n[00:37:49] Do we have to be in official leadership role to be a good leader or to show leadership?
\n\n[00:40:18] How can we start allowing people to be the best version of themselves?
\n\n[00:44:02] Do leaders have a better sense of humor?
\n\n[00:45:17] What does it mean to “be authentic”?
\n\n[00:47:51] Why is it more important that we keep it real in the first place now more than more than ever?
\n\n[00:49:42] Can we cultivate authenticity as a trait?
\n\n[00:51:47] Can we use authenticity to help us combat imposter syndrome?
\n\n[00:54:06] It's one hundred years in the future, what do you want to be remembered for?
\n\n[00:55:05] The Random Round
Special Guest: Sadhna Bokhiria.
","summary":"","date_published":"2021-03-05T00:00:00.000-05:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/2fce7495-04d9-45c9-ae9d-e99c7fe20eea.mp3","mime_type":"audio/mpeg","size_in_bytes":59551312,"duration_in_seconds":3721}]},{"id":"6cf39550-d520-4ed2-b5e0-2d80519ad5e2","title":"Comet ML Office Hours 4 - 28FEB2021","url":"https://theartistsofdatascience.fireside.fm/comet-ml-4","content_text":"Comet provides a self-hosted and cloud-based meta machine learning platform allowing data scientists and teams to track, compare, explain and optimize experiments and models. Backed by thousands of users and multiple Fortune 100 companies, Comet provides insights and data to build better, more accurate AI models while improving productivity, collaboration and visibility across teams.\n\nRegister for future sessions here: http://bit.ly/comet-ml-oh\n\nCheckout Comet ML by visiting: https://www.comet.ml/\n\nOr on Twitter: https://twitter.com/CometML\n\nOn YouTube: https://www.youtube.com/channel/UCmN63HKvfXSCS-UwVwmK8Hw\n\nConnect with Ayodele \n\nLinkedIn: https://www.linkedin.com/in/ayodeleodubela/ \n\nTwitter: https://twitter.com/DataSciBae\n\nCheck out her course on LinkedIn Learning: https://www.linkedin.com/learning/supervised-learning-essential-training/supervised-machine-learning-and-the-technology-boom\n\n[00:01:17] We talk about the madness of the data science interview process\n\n[00:02:29] Why are why are we making it so difficult for people to even get considered for an interview? \n\n[00:05:41] Could data science benefit from a series of exams and accreditations, much like actuaries have to do?\n\n[00:07:53] How does one prepare for these technical questions? \n\n[00:11:35] How long should you spend preparing for an interview?\n\n[00:12:58] What are considered medium/hard type of questions?\n\n[00:16:02] Am I expected to have an answer for everything?\n\n[00:20:46] How many projects should I have, and what should they be like?\n\n[00:24:25] How should I be allocating my time in the job search process?\n\n[00:25:17] Book recommendations\n\n[00:28:30] Notebooks or scripts?\n\n[00:30:53] How to explain your projects?\n\n[00:35:07] Mark talks about some stuff he’s doing at work around creating and defining metrics and KPIs\n\n[00:41:16] Tor jumps in with some sage advice\n\n[00:44:46] Measuring the monetary value of your efforts\n\n[00:50:10] How do you get managers buying into creating a data project? \n\n[00:54:21] Explaining the value of your passion projects in an interview\n\n[00:57:03] NLP question time","content_html":"Comet provides a self-hosted and cloud-based meta machine learning platform allowing data scientists and teams to track, compare, explain and optimize experiments and models. Backed by thousands of users and multiple Fortune 100 companies, Comet provides insights and data to build better, more accurate AI models while improving productivity, collaboration and visibility across teams.
\n\nRegister for future sessions here: http://bit.ly/comet-ml-oh
\n\nCheckout Comet ML by visiting: https://www.comet.ml/
\n\nOr on Twitter: https://twitter.com/CometML
\n\nOn YouTube: https://www.youtube.com/channel/UCmN63HKvfXSCS-UwVwmK8Hw
\n\nConnect with Ayodele
\n\nLinkedIn: https://www.linkedin.com/in/ayodeleodubela/
\n\nTwitter: https://twitter.com/DataSciBae
\n\nCheck out her course on LinkedIn Learning: https://www.linkedin.com/learning/supervised-learning-essential-training/supervised-machine-learning-and-the-technology-boom
\n\n[00:01:17] We talk about the madness of the data science interview process
\n\n[00:02:29] Why are why are we making it so difficult for people to even get considered for an interview?
\n\n[00:05:41] Could data science benefit from a series of exams and accreditations, much like actuaries have to do?
\n\n[00:07:53] How does one prepare for these technical questions?
\n\n[00:11:35] How long should you spend preparing for an interview?
\n\n[00:12:58] What are considered medium/hard type of questions?
\n\n[00:16:02] Am I expected to have an answer for everything?
\n\n[00:20:46] How many projects should I have, and what should they be like?
\n\n[00:24:25] How should I be allocating my time in the job search process?
\n\n[00:25:17] Book recommendations
\n\n[00:28:30] Notebooks or scripts?
\n\n[00:30:53] How to explain your projects?
\n\n[00:35:07] Mark talks about some stuff he’s doing at work around creating and defining metrics and KPIs
\n\n[00:41:16] Tor jumps in with some sage advice
\n\n[00:44:46] Measuring the monetary value of your efforts
\n\n[00:50:10] How do you get managers buying into creating a data project?
\n\n[00:54:21] Explaining the value of your passion projects in an interview
\n\n[00:57:03] NLP question time
","summary":"","date_published":"2021-03-04T00:00:00.000-05:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/6cf39550-d520-4ed2-b5e0-2d80519ad5e2.mp3","mime_type":"audio/mpeg","size_in_bytes":37907045,"duration_in_seconds":3914}]},{"id":"6f1f6d7c-b15d-4ebd-ad42-b42265e45463","title":"Data Science Happy Hour 21 | 26FEB2021","url":"https://theartistsofdatascience.fireside.fm/oh21","content_text":"The Data Science Happy Hours keep getting happier! \n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh\n\nIf you want to interact with me multiple times a week, join Data Science Dream Job for 70% off: http://dsdj.co/artists70\n\nWatch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm\n\n[00:00:09] We kick it off with a practice presentation and then questions from the audience. This is an excellent learning experience for everyone!\n\n[00:13:30] Audience questions start here.\n\n[00:26:04] Tribe member Eric Sims shares some awesome news with us\n\n[00:27:15] We learn a lot about cloud technologies through the lens of a web scraping project \n\n[00:38:50] Can a business person manage a fully developed Data science team? And what are the skills required for that?\n\n[00:41:30] In data science, there are two types of leadership\n\n[00:43:38] What’s the difference between strategic leadership and technical leadership?\n\n[00:49:13] Data science leadership at the executive level vs team lead level\n\n[00:58:55] Question about an NLP project\n\n[01:04:35] Product management, metrics, and KPIs\n\n[01:12:28] Now what foundation does it take to break into engineering from Data science besides technical skills, what are their skills are needed to survive in engineering.\n\n[01:23:13] How to “cold call” and network on LinkedInSpecial Guests: Greg Coquillo and Vin Vashishta.","content_html":"The Data Science Happy Hours keep getting happier!
\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh
\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\n\nIf you want to interact with me multiple times a week, join Data Science Dream Job for 70% off: http://dsdj.co/artists70
\n\nWatch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm
\n\n[00:00:09] We kick it off with a practice presentation and then questions from the audience. This is an excellent learning experience for everyone!
\n\n[00:13:30] Audience questions start here.
\n\n[00:26:04] Tribe member Eric Sims shares some awesome news with us
\n\n[00:27:15] We learn a lot about cloud technologies through the lens of a web scraping project
\n\n[00:38:50] Can a business person manage a fully developed Data science team? And what are the skills required for that?
\n\n[00:41:30] In data science, there are two types of leadership
\n\n[00:43:38] What’s the difference between strategic leadership and technical leadership?
\n\n[00:49:13] Data science leadership at the executive level vs team lead level
\n\n[00:58:55] Question about an NLP project
\n\n[01:04:35] Product management, metrics, and KPIs
\n\n[01:12:28] Now what foundation does it take to break into engineering from Data science besides technical skills, what are their skills are needed to survive in engineering.
\n\n[01:23:13] How to “cold call” and network on LinkedIn
Special Guests: Greg Coquillo and Vin Vashishta.
","summary":"","date_published":"2021-02-28T00:00:00.000-05:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/6f1f6d7c-b15d-4ebd-ad42-b42265e45463.mp3","mime_type":"audio/mpeg","size_in_bytes":52646586,"duration_in_seconds":5522}]},{"id":"a5b9b645-6099-4708-9f86-006665b1a0c9","title":"Frameworks for Strategy and Innovation | Tim Enalls","url":"https://theartistsofdatascience.fireside.fm/tim-enalls","content_text":"We're continuing down this path of understanding the interplay between product management and data science, it's an important topic and something we should all be knowledgeable about. \n\nOn this episode we speak to a data scientist who wears many hats: Tim Enalls. \n\nHe’s an MBA, CAP, PMP, YouTuber, blogger, and thought leader - and though he has many identities, he has one passion: Data Science.\n\n[00:01:33] Guest introductiuon\n\n[00:02:38] We learn about where Tim is from\n\n[00:03:50] What Tim thought his future would look like when he was younger\n\n[00:05:22] What was the journey like coming from high school to where you are now?\n\n[00:06:56] What would you say would be a experience that contributed to shaping who you are today?\n\n[00:07:50] Some frameworks for innovations\n\n[00:12:27] The innovation framework Tim has used most in his career\n\n[00:13:21] The importance of self-teaching\n\n[00:14:08] Business strategy frameworks that every data scientist should know\n\n[00:17:49] What can a data scientist do to build and develop their product sense or their business acumen?\n\n[00:19:47] What do you think are some reasons that Data science projects fail? And how can we as data scientist prevent that from happening?\n\n[00:23:19] Best practices for helping your company grow its analytic maturity\n\n[00:24:51] Strategies for problem solving\n\n[00:28:29] The difference between a product manager and a data science manager\n\n[00:30:24] What can Data scientists learn from product managers, product managers?\n\n[00:31:14] In what ways has your experience taking the PMP exams made you a better data scientist?\n\n[00:31:45] How can data scientists be more out of the box thinkers?\n\n[00:33:14] We geek out over our mutual appreciation of Seth Godin\n\n[00:34:31] Do you subscribe to things like business newsletters or anything like that? \n\n[00:36:09] The importance of emotional intelligence\n\n[00:38:47] It’s 100 years in the future, what do you want to be remembered for?\n\n[00:40:53] The creative practice\n\n[00:43:24] The random roundSpecial Guest: Tim Enalls.","content_html":"We're continuing down this path of understanding the interplay between product management and data science, it's an important topic and something we should all be knowledgeable about.
\n\nOn this episode we speak to a data scientist who wears many hats: Tim Enalls.
\n\nHe’s an MBA, CAP, PMP, YouTuber, blogger, and thought leader - and though he has many identities, he has one passion: Data Science.
\n\n[00:01:33] Guest introductiuon
\n\n[00:02:38] We learn about where Tim is from
\n\n[00:03:50] What Tim thought his future would look like when he was younger
\n\n[00:05:22] What was the journey like coming from high school to where you are now?
\n\n[00:06:56] What would you say would be a experience that contributed to shaping who you are today?
\n\n[00:07:50] Some frameworks for innovations
\n\n[00:12:27] The innovation framework Tim has used most in his career
\n\n[00:13:21] The importance of self-teaching
\n\n[00:14:08] Business strategy frameworks that every data scientist should know
\n\n[00:17:49] What can a data scientist do to build and develop their product sense or their business acumen?
\n\n[00:19:47] What do you think are some reasons that Data science projects fail? And how can we as data scientist prevent that from happening?
\n\n[00:23:19] Best practices for helping your company grow its analytic maturity
\n\n[00:24:51] Strategies for problem solving
\n\n[00:28:29] The difference between a product manager and a data science manager
\n\n[00:30:24] What can Data scientists learn from product managers, product managers?
\n\n[00:31:14] In what ways has your experience taking the PMP exams made you a better data scientist?
\n\n[00:31:45] How can data scientists be more out of the box thinkers?
\n\n[00:33:14] We geek out over our mutual appreciation of Seth Godin
\n\n[00:34:31] Do you subscribe to things like business newsletters or anything like that?
\n\n[00:36:09] The importance of emotional intelligence
\n\n[00:38:47] It’s 100 years in the future, what do you want to be remembered for?
\n\n[00:40:53] The creative practice
\n\n[00:43:24] The random round
Special Guest: Tim Enalls.
","summary":"We're continuing down this path of understanding the interplay between product management and data science, it's an important topic and something we should all be knowledgeable about. ","date_published":"2021-02-26T00:00:00.000-05:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/a5b9b645-6099-4708-9f86-006665b1a0c9.mp3","mime_type":"audio/mpeg","size_in_bytes":49738046,"duration_in_seconds":3108}]},{"id":"bc462a53-f678-48b2-a46d-9bf124132e66","title":"Comet ML Office Hours 3 - 21FEB2021","url":"https://theartistsofdatascience.fireside.fm/comet-ml-3","content_text":"Comet provides a self-hosted and cloud-based meta machine learning platform allowing data scientists and teams to track, compare, explain and optimize experiments and models. Backed by thousands of users and multiple Fortune 100 companies, Comet provides insights and data to build better, more accurate AI models while improving productivity, collaboration and visibility across teams.\n\nRegister for future sessions here: http://bit.ly/comet-ml-oh\n\nCheckout Comet ML by visiting: https://www.comet.ml/\n\nOr on Twitter: https://twitter.com/CometML\n\nConnect with Ayodele \n\nLinkedIn: https://www.linkedin.com/in/ayodeleodubela/ \n\nTwitter: https://twitter.com/DataSciBae\n\nCheck out her course on LinkedIn Learning: https://www.linkedin.com/learning/supervised-learning-essential-training/supervised-machine-learning-and-the-technology-boom\n\n[00:01:05] When we talk about data validation, what is it that we mean?\n\n[00:03:19] A data sheet? What is that?\n\n[00:05:30] What’s a good approach to asking questions for a data science project?\n\n[00:10:41] Sampling is important\n\n[00:17:54] Where does data validation fall in the data science lifecycle?\n\n[00:18:31] Where in the pipeline do I perform cross-validation?\n\n[00:21:00] How do algorithms know which content to push to you and how can I affect the content being pushed my way?\n\n[00:26:26] There is a lack of transparency when it comes to these algorithms\n\n[00:30:50] Some more excellent discussion around ethics in machine learning\n\n[00:32:52] Ayodele drops some sage insight on how machine learning algorithms are used and the ethics of it all\n\n[00:36:20] How to go from data to decisions\n\n[00:43:10] What exactly is an insight?\n\n[00:47:09] What comes first: the question or the data?\n\n[00:53:22] How do you create a narrative around your analysis?\n\n[01:02:09] How do you talk about the narrative in your project?\n\n[01:05:14] Eliminating data feeds that are wasting money, why are you collecting data that you don’t use?\n\n[01:11:23] What’s your favorite data science book?","content_html":"Comet provides a self-hosted and cloud-based meta machine learning platform allowing data scientists and teams to track, compare, explain and optimize experiments and models. Backed by thousands of users and multiple Fortune 100 companies, Comet provides insights and data to build better, more accurate AI models while improving productivity, collaboration and visibility across teams.
\n\nRegister for future sessions here: http://bit.ly/comet-ml-oh
\n\nCheckout Comet ML by visiting: https://www.comet.ml/
\n\nOr on Twitter: https://twitter.com/CometML
\n\nConnect with Ayodele
\n\nLinkedIn: https://www.linkedin.com/in/ayodeleodubela/
\n\nTwitter: https://twitter.com/DataSciBae
\n\nCheck out her course on LinkedIn Learning: https://www.linkedin.com/learning/supervised-learning-essential-training/supervised-machine-learning-and-the-technology-boom
\n\n[00:01:05] When we talk about data validation, what is it that we mean?
\n\n[00:03:19] A data sheet? What is that?
\n\n[00:05:30] What’s a good approach to asking questions for a data science project?
\n\n[00:10:41] Sampling is important
\n\n[00:17:54] Where does data validation fall in the data science lifecycle?
\n\n[00:18:31] Where in the pipeline do I perform cross-validation?
\n\n[00:21:00] How do algorithms know which content to push to you and how can I affect the content being pushed my way?
\n\n[00:26:26] There is a lack of transparency when it comes to these algorithms
\n\n[00:30:50] Some more excellent discussion around ethics in machine learning
\n\n[00:32:52] Ayodele drops some sage insight on how machine learning algorithms are used and the ethics of it all
\n\n[00:36:20] How to go from data to decisions
\n\n[00:43:10] What exactly is an insight?
\n\n[00:47:09] What comes first: the question or the data?
\n\n[00:53:22] How do you create a narrative around your analysis?
\n\n[01:02:09] How do you talk about the narrative in your project?
\n\n[01:05:14] Eliminating data feeds that are wasting money, why are you collecting data that you don’t use?
\n\n[01:11:23] What’s your favorite data science book?
","summary":"Comet provides a self-hosted and cloud-based meta machine learning platform allowing data scientists and teams to track, compare, explain and optimize experiments and models. Backed by thousands of users and multiple Fortune 100 companies, Comet provides insights and data to build better, more accurate AI models while improving productivity, collaboration and visibility across teams.","date_published":"2021-02-25T00:00:00.000-05:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/bc462a53-f678-48b2-a46d-9bf124132e66.mp3","mime_type":"audio/mpeg","size_in_bytes":42872907,"duration_in_seconds":4448}]},{"id":"c2647746-a94b-4dbf-b09a-904db0250dd0","title":"Data Science Happy Hour 20 | 19FEB2021","url":"https://theartistsofdatascience.fireside.fm/oh20","content_text":"The Data Science Happy Hours keep getting happier! \n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh\n\nIf you want to interact with me multiple times a week, join Data Science Dream Job for 70% off: http://dsdj.co/artists70\n\nWatch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm\n\n[00:03:19] The rise of new roles in data science\n\n[00:04:10] What is it going to take, going forward, to start making money with machine learning and help companies on that road to maturity?\n\n[00:06:54] What is an ML architect?\n\n[00:09:13] Should a research oriented data scientist learn about architecture?\n\n[00:12:41] Do you have to be a great software engineer to think like one?\n\n[00:18:48] What is a feature store?\n\n[00:20:57] The more I get into this data science/machine learning space…it's like the more I realized I don't know shit.\n\n[00:23:22] Mikiko comes in with some awesome insight about feature sores\n\n[00:28:48] When do I use a partition for a database?\n\n[00:36:46] What are some other types of correlation?\n\n[00:42:04] Thom with some wisdom.\n\n[00:44:23] A question on web scraping (not people information, but product prices)\n\n[00:55:17] The legality of web scraping\n\n[00:58:15] How to understand how to help someone in the most effective way\n\n[01:11:26] Figure out what the “ground truth” really is\n\n[01:14:09] Why you need an emphasis on customer focus and how you can cultivate that mindset\n\n\n\nSome useful links from our discussion\n\n00:23:14 Greg Coquillo\nhttps://www.linkedin.com/posts/greg-coquillo_datascience-machinelearning-artificialintelligence-activity-6760977800963985408-ys5y\n\n00:28:41 Joe Reis\nhttps://www.youtube.com/watch?v=o4q_ljRkXqw\n\n00:36:24 Mikiko Bazeley\nhttps://learning.oreilly.com/library/view/designing-data-intensive-applications/9781491903063/ch06.html\n\n00:37:55 Mikiko Bazeley\nhttps://docs.snowflake.com/en/user-guide/tables-clustering-micropartitions.html)\n\n00:42:50 Mark Freeman\nhttps://drive.google.com/file/d/1qkURyDrEa4IkQRm0in26CNIpP84ws0Tf/view\n\n00:43:51 Harpreet Sahota\nhttps://realpython.com/numpy-scipy-pandas-correlation-python/\n\n00:44:02 Mitul Patel\nhttps://easystats.github.io/correlation/articles/types.html in R\n\n00:47:07 Mark Freeman\nhttps://towardsdatascience.com/rip-correlation-introducing-the-predictive-power-score-3d90808b9598\n\n00:54:25 Mikiko Bazeley\nhttps://realpython.com/courses/python-lambda-functions/\n\n00:54:35 Mikiko Bazeley\nhttps://learn.datacamp.com/courses/streaming-data-with-aws-kinesis-and-lambda\n\n00:55:43 Mark Freeman\nhttps://docs.aws.amazon.com/toolkit-for-eclipse/v1/user-guide/lambda-tutorial.html\n\n00:56:54 Mark Freeman\nhttps://docs.aws.amazon.com/lambda/latest/dg/welcome.html\n\n00:58:14 Mikiko Bazeley\nhttps://www.forbes.com/sites/emmawoollacott/2019/09/10/linkedin-data-scraping-ruled-legal/?sh=a0f2baa1b54b\n\n00:58:20 Joe Reis\nhttps://www.eff.org/deeplinks/2019/09/victory-ruling-hiq-v-linkedin-protects-scraping-public-data\n\n01:06:32 Mark Freeman\nhttps://www.datascience-pm.com/crisp-dm-2/\n\n01:19:05 Mikiko Bazeley\nhttps://www.linkedin.com/posts/crmercado_datascience-deeplearning-artificialintelligence-activity-6767800296333811712-Jf8r\n\n01:24:20 Mark Freeman\nhttps://voltagecontrol.com/blog/5-steps-of-the-design-thinking-process-a-step-by-step-guide/\n\n01:24:45 Mark Freeman\nhttps://steveblank.com/category/lean-launchpad/\n\n01:27:27 Vikram Krishna Kotturu\nhttps://join.slack.com/t/artofdatascienceloft/shared_invite/zt-dgzn8abm-ge_dKGxrc9Dsuhnly90WTw\n\n01:27:45 Harpreet Sahota\nhttps://join.slack.com/t/artofdatascienceloft/shared_invite/zt-dgzn8abm-ge_dKGxrc9Dsuhnly90WTwSpecial Guests: Brandon Quach, PhD, Greg Coquillo, Mikiko Bazeley, and Vin Vashishta.","content_html":"The Data Science Happy Hours keep getting happier!
\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh
\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\n\nIf you want to interact with me multiple times a week, join Data Science Dream Job for 70% off: http://dsdj.co/artists70
\n\nWatch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm
\n\n[00:03:19] The rise of new roles in data science
\n\n[00:04:10] What is it going to take, going forward, to start making money with machine learning and help companies on that road to maturity?
\n\n[00:06:54] What is an ML architect?
\n\n[00:09:13] Should a research oriented data scientist learn about architecture?
\n\n[00:12:41] Do you have to be a great software engineer to think like one?
\n\n[00:18:48] What is a feature store?
\n\n[00:20:57] The more I get into this data science/machine learning space…it's like the more I realized I don't know shit.
\n\n[00:23:22] Mikiko comes in with some awesome insight about feature sores
\n\n[00:28:48] When do I use a partition for a database?
\n\n[00:36:46] What are some other types of correlation?
\n\n[00:42:04] Thom with some wisdom.
\n\n[00:44:23] A question on web scraping (not people information, but product prices)
\n\n[00:55:17] The legality of web scraping
\n\n[00:58:15] How to understand how to help someone in the most effective way
\n\n[01:11:26] Figure out what the “ground truth” really is
\n\n[01:14:09] Why you need an emphasis on customer focus and how you can cultivate that mindset
\n\nSome useful links from our discussion
\n\n00:23:14 Greg Coquillo
\nhttps://www.linkedin.com/posts/greg-coquillo_datascience-machinelearning-artificialintelligence-activity-6760977800963985408-ys5y
00:28:41 Joe Reis
\nhttps://www.youtube.com/watch?v=o4q_ljRkXqw
00:36:24 Mikiko Bazeley
\nhttps://learning.oreilly.com/library/view/designing-data-intensive-applications/9781491903063/ch06.html
00:37:55 Mikiko Bazeley
\nhttps://docs.snowflake.com/en/user-guide/tables-clustering-micropartitions.html)
00:42:50 Mark Freeman
\nhttps://drive.google.com/file/d/1qkURyDrEa4IkQRm0in26CNIpP84ws0Tf/view
00:43:51 Harpreet Sahota
\nhttps://realpython.com/numpy-scipy-pandas-correlation-python/
00:44:02 Mitul Patel
\nhttps://easystats.github.io/correlation/articles/types.html in R
00:47:07 Mark Freeman
\nhttps://towardsdatascience.com/rip-correlation-introducing-the-predictive-power-score-3d90808b9598
00:54:25 Mikiko Bazeley
\nhttps://realpython.com/courses/python-lambda-functions/
00:54:35 Mikiko Bazeley
\nhttps://learn.datacamp.com/courses/streaming-data-with-aws-kinesis-and-lambda
00:55:43 Mark Freeman
\nhttps://docs.aws.amazon.com/toolkit-for-eclipse/v1/user-guide/lambda-tutorial.html
00:56:54 Mark Freeman
\nhttps://docs.aws.amazon.com/lambda/latest/dg/welcome.html
00:58:14 Mikiko Bazeley
\nhttps://www.forbes.com/sites/emmawoollacott/2019/09/10/linkedin-data-scraping-ruled-legal/?sh=a0f2baa1b54b
00:58:20 Joe Reis
\nhttps://www.eff.org/deeplinks/2019/09/victory-ruling-hiq-v-linkedin-protects-scraping-public-data
01:06:32 Mark Freeman
\nhttps://www.datascience-pm.com/crisp-dm-2/
01:19:05 Mikiko Bazeley
\nhttps://www.linkedin.com/posts/crmercado_datascience-deeplearning-artificialintelligence-activity-6767800296333811712-Jf8r
01:24:20 Mark Freeman
\nhttps://voltagecontrol.com/blog/5-steps-of-the-design-thinking-process-a-step-by-step-guide/
01:24:45 Mark Freeman
\nhttps://steveblank.com/category/lean-launchpad/
01:27:27 Vikram Krishna Kotturu
\nhttps://join.slack.com/t/artofdatascienceloft/shared_invite/zt-dgzn8abm-ge_dKGxrc9Dsuhnly90WTw
01:27:45 Harpreet Sahota
\nhttps://join.slack.com/t/artofdatascienceloft/shared_invite/zt-dgzn8abm-ge_dKGxrc9Dsuhnly90WTw
Special Guests: Brandon Quach, PhD, Greg Coquillo, Mikiko Bazeley, and Vin Vashishta.
","summary":"","date_published":"2021-02-21T00:00:00.000-05:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/c2647746-a94b-4dbf-b09a-904db0250dd0.mp3","mime_type":"audio/mpeg","size_in_bytes":48048557,"duration_in_seconds":5049}]},{"id":"ba80b5b9-64d6-4f1c-8115-7eefe90c14d9","title":"Product Management for Data Scientists | Greg Coquillo","url":"https://theartistsofdatascience.fireside.fm/greg-coquillo","content_text":"Greg is an Amazon Private Brands Program Manager and content creator. He was recently named one of LinkedIn's Top Voices in Data and Analytics for 2020\n\nFIND GREG ONLINE\n\nLinkedIn: https://www.linkedin.com/in/greg-coquillo/\n\nQUOTES\n\n[00:10:06] \"Let your curiosity be your driver.\"\n\n[00:13:48] \"The product manager is there to take a look at the product vision...a manager is there to guide through the vision.\"\n\n[00:20:52] \"Communication skill helps me translate that technical solution into a solution that your stakeholder relate to. One of the best ways to learn from stakeholders is to invite them into the technical solution building session.\"\n\n[00:28:47] \"When you don't invite the business stakeholders into your model building sessions, you will miss out on capturing the level of risk that those business stakeholders are willing to take.\" \n\nHIGHLIGHTS FROM THE SHOW\n\n[00:02:04] Guest introduction\n\n[00:02:50] An experience that shaped Greg\n\n[00:04:25] What Greg thought he was going to be when he grew up\n\n[00:06:51] The path that led Greg to where he is today\n\n[00:09:59] How Greg taught himself data science skills\n\n[00:12:43] What role does the product manager play on a Data science team?\n\n[00:15:21] What part of the Data science lifecycle does the product manager own? \n\n[00:16:47] How is a product manager different from a manager of a Data science team?\n\n[00:18:44] What can the data scientist learn from the product manager?\n\n[00:21:40] What can the Data scientists do to help make their product manager more effective?\n\n[00:22:44] How can a data scientist learn product management skills?\n\n[00:24:40] The ten dysfunctions of product management\n\n[00:26:53] How do we measure what really matters and how do we determine what matters?\n\n[00:30:40] The difference between AI and BI\n\n[00:32:42] What qualities make for a good BI leader?\n\n[00:33:16] What qualities make for a good AI leader?\n\n[00:37:40] What do you think will be the biggest positive impact that AI will have in the next two to five years on society?\n\n[00:39:30] The scariest application of AI?\n\n[00:40:46] An AI code of ethics\n\n[00:43:11] Auditing algorithms \n\n[00:45:19] Compliance as a service\n\n[00:47:06] What data scientists need to know about compliance and how they can learn about it\n\n[00:48:29] Should you be afraid of job descriptions?\n\n[00:52:22] First order and second order thinking\n\n[00:56:09] The importance of communication skills\n\n[01:00:30] It’s 100 years in the future, what do you want to be remembered for?\n\n[01:03:01] The random roundSpecial Guest: Greg Coquillo.","content_html":"Greg is an Amazon Private Brands Program Manager and content creator. He was recently named one of LinkedIn's Top Voices in Data and Analytics for 2020
\n\nFIND GREG ONLINE
\n\nLinkedIn: https://www.linkedin.com/in/greg-coquillo/
\n\nQUOTES
\n\n[00:10:06] "Let your curiosity be your driver."
\n\n[00:13:48] "The product manager is there to take a look at the product vision...a manager is there to guide through the vision."
\n\n[00:20:52] "Communication skill helps me translate that technical solution into a solution that your stakeholder relate to. One of the best ways to learn from stakeholders is to invite them into the technical solution building session."
\n\n[00:28:47] "When you don't invite the business stakeholders into your model building sessions, you will miss out on capturing the level of risk that those business stakeholders are willing to take."
\n\nHIGHLIGHTS FROM THE SHOW
\n\n[00:02:04] Guest introduction
\n\n[00:02:50] An experience that shaped Greg
\n\n[00:04:25] What Greg thought he was going to be when he grew up
\n\n[00:06:51] The path that led Greg to where he is today
\n\n[00:09:59] How Greg taught himself data science skills
\n\n[00:12:43] What role does the product manager play on a Data science team?
\n\n[00:15:21] What part of the Data science lifecycle does the product manager own?
\n\n[00:16:47] How is a product manager different from a manager of a Data science team?
\n\n[00:18:44] What can the data scientist learn from the product manager?
\n\n[00:21:40] What can the Data scientists do to help make their product manager more effective?
\n\n[00:22:44] How can a data scientist learn product management skills?
\n\n[00:24:40] The ten dysfunctions of product management
\n\n[00:26:53] How do we measure what really matters and how do we determine what matters?
\n\n[00:30:40] The difference between AI and BI
\n\n[00:32:42] What qualities make for a good BI leader?
\n\n[00:33:16] What qualities make for a good AI leader?
\n\n[00:37:40] What do you think will be the biggest positive impact that AI will have in the next two to five years on society?
\n\n[00:39:30] The scariest application of AI?
\n\n[00:40:46] An AI code of ethics
\n\n[00:43:11] Auditing algorithms
\n\n[00:45:19] Compliance as a service
\n\n[00:47:06] What data scientists need to know about compliance and how they can learn about it
\n\n[00:48:29] Should you be afraid of job descriptions?
\n\n[00:52:22] First order and second order thinking
\n\n[00:56:09] The importance of communication skills
\n\n[01:00:30] It’s 100 years in the future, what do you want to be remembered for?
\n\n[01:03:01] The random round
Special Guest: Greg Coquillo.
","summary":"","date_published":"2021-02-19T00:00:00.000-05:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/ba80b5b9-64d6-4f1c-8115-7eefe90c14d9.mp3","mime_type":"audio/mpeg","size_in_bytes":40987647,"duration_in_seconds":4596}]},{"id":"47a3a031-62e7-48ea-9bd0-1406e0bc15da","title":"Comet ML Office Hours 2 - 14FEB2021","url":"https://theartistsofdatascience.fireside.fm/comet-ml-2","content_text":"Comet provides a self-hosted and cloud-based meta machine learning platform allowing data scientists and teams to track, compare, explain and optimize experiments and models. Backed by thousands of users and multiple Fortune 100 companies, Comet provides insights and data to build better, more accurate AI models while improving productivity, collaboration and visibility across teams.\n\nRegister for future sessions here: http://bit.ly/comet-ml-oh\n\nCheckout Comet ML by visiting: https://www.comet.ml/\n\nOr on Twitter: https://twitter.com/CometML\n\nConnect with Ayodele \n\nLinkedIn: https://www.linkedin.com/in/ayodeleodubela/ \n\nTwitter: https://twitter.com/DataSciBae\n\nCheck out her course on LinkedIn Learning: https://www.linkedin.com/learning/supervised-learning-essential-training/supervised-machine-learning-and-the-technology-boom\n\n[00:00:09] Friendly banter between the hosts\n\n[00:02:17] Did you learn anything new this week?\n\n[00:02:56] What is MLOps?\n\n[00:09:33] How MLOps is used in the finance industry\n\n[00:11:04] Topics to brush up on if you’re looking to get into the finance space as a data scientist\n\n[00:12:06] Resources for learning about GLMs\n\n[00:15:00] The struggle of being a data scientist\n\n[00:17:19] What to do when you feel like there is so much to learn and not enough time\n\n[00:18:56] A few key things you should focus on when you’re breaking into the field\n\n[00:22:01] The hardest part about SQL\n\n[00:25:59] What skills do I try to showcase in my portfolio project?\n\n[00:30:42] How am I supposed to gain business acumen when I don’t have a job?\n\n[00:37:30] How do I get my profile noticed?\n\n[00:38:52] Understand how to develop KPIs and how your model impacts them\n\n[00:44:05] How would you split your time amongst different activities when doing a project?\n\n[00:51:17] There are multiple algorithms to use, how do I choose?\n\n[00:53:33] How to deal with these crazy job descriptions?\n\n[01:01:09] How do I position myself as a valuable candidate for a job?\n\n[01:07:19] Get people to do mock interviews with you\n\n[01:07:48] Convincing business stakeholders of your results when they want to follow their gut\n\n[01:15:17] Resources for picking up some nonobvious skills you need as a data scientist\n\n[01:16:13] Template methodology for problem framing: https://www.comet.ml/reports-templates/project-scope-template/reports/template/project-scope\n\n[01:18:14] Why you need to get as much hands on practice as you can","content_html":"Comet provides a self-hosted and cloud-based meta machine learning platform allowing data scientists and teams to track, compare, explain and optimize experiments and models. Backed by thousands of users and multiple Fortune 100 companies, Comet provides insights and data to build better, more accurate AI models while improving productivity, collaboration and visibility across teams.
\n\nRegister for future sessions here: http://bit.ly/comet-ml-oh
\n\nCheckout Comet ML by visiting: https://www.comet.ml/
\n\nOr on Twitter: https://twitter.com/CometML
\n\nConnect with Ayodele
\n\nLinkedIn: https://www.linkedin.com/in/ayodeleodubela/
\n\nTwitter: https://twitter.com/DataSciBae
\n\nCheck out her course on LinkedIn Learning: https://www.linkedin.com/learning/supervised-learning-essential-training/supervised-machine-learning-and-the-technology-boom
\n\n[00:00:09] Friendly banter between the hosts
\n\n[00:02:17] Did you learn anything new this week?
\n\n[00:02:56] What is MLOps?
\n\n[00:09:33] How MLOps is used in the finance industry
\n\n[00:11:04] Topics to brush up on if you’re looking to get into the finance space as a data scientist
\n\n[00:12:06] Resources for learning about GLMs
\n\n[00:15:00] The struggle of being a data scientist
\n\n[00:17:19] What to do when you feel like there is so much to learn and not enough time
\n\n[00:18:56] A few key things you should focus on when you’re breaking into the field
\n\n[00:22:01] The hardest part about SQL
\n\n[00:25:59] What skills do I try to showcase in my portfolio project?
\n\n[00:30:42] How am I supposed to gain business acumen when I don’t have a job?
\n\n[00:37:30] How do I get my profile noticed?
\n\n[00:38:52] Understand how to develop KPIs and how your model impacts them
\n\n[00:44:05] How would you split your time amongst different activities when doing a project?
\n\n[00:51:17] There are multiple algorithms to use, how do I choose?
\n\n[00:53:33] How to deal with these crazy job descriptions?
\n\n[01:01:09] How do I position myself as a valuable candidate for a job?
\n\n[01:07:19] Get people to do mock interviews with you
\n\n[01:07:48] Convincing business stakeholders of your results when they want to follow their gut
\n\n[01:15:17] Resources for picking up some nonobvious skills you need as a data scientist
\n\n[01:16:13] Template methodology for problem framing: https://www.comet.ml/reports-templates/project-scope-template/reports/template/project-scope
\n\n[01:18:14] Why you need to get as much hands on practice as you can
","summary":"","date_published":"2021-02-18T00:00:00.000-05:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/47a3a031-62e7-48ea-9bd0-1406e0bc15da.mp3","mime_type":"audio/mpeg","size_in_bytes":47261111,"duration_in_seconds":4895}]},{"id":"5989e1c6-69b8-4f3c-9fc1-74cf3b876fae","title":"Data Science Happy Hour 19 | 12FEB2021","url":"https://theartistsofdatascience.fireside.fm/oh19","content_text":"The Data Science Happy Hours keep getting happier! \n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh\n\nIf you want to interact with me multiple times a week, join Data Science Dream Job for 70% off: http://dsdj.co/artists70\n\nWatch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm","content_html":"The Data Science Happy Hours keep getting happier!
\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh
\n\nRegister for Sunday Sessions here: http://bit.ly/comet-ml-oh
\n\nIf you want to interact with me multiple times a week, join Data Science Dream Job for 70% off: http://dsdj.co/artists70
\n\nWatch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm
","summary":"","date_published":"2021-02-13T18:00:00.000-05:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/5989e1c6-69b8-4f3c-9fc1-74cf3b876fae.mp3","mime_type":"audio/mpeg","size_in_bytes":50791156,"duration_in_seconds":4915}]},{"id":"13cc86e3-701d-473a-9eb0-e385b9fca0cf","title":"Algorithmic Fairness | Sian Lewis","url":"https://theartistsofdatascience.fireside.fm/sian-lewis","content_text":"Siann is a lead data scientist and analytics manager at Booz Allen Hamilton where she helps her stakeholders cut through the clutter to make better decisions, and leads a team that transforms complex problems into simple solutions.\n\nFor her contributions to data science and social good, she’s been awarded the 2020 Women of Color in STEM All Star award, the 2019 DCFemTech award, and the 2017 Prince George’s County, MD 40 Under 40 honoree. \n\nFIND SIAN ONLINE\n\nLinkedIn: https://www.linkedin.com/in/allsian/\n\nWebsite: https://www.sianlewis.org/\n\nCorporate Profile: https://www.boozallen.com/e/insight/people-profiles/sian-lewis.html\n\nQUOTES\n\n[00:04:17] \"When you're an immigrant, you find any form of these social enclaves. Wherever you are with people who are similar to you, who are from similar countries as you, you form tight knit communities.\"\n\n[00:08:29] \"You know, I went to grad school. I was terrible at it. I didn't want to be there. And I actually learned that I had no interest in actually anything health care related. So I quit after great anguish, great terror. And I was like, oh, my God, what am I going to do with my life?\"\n\n[00:14:08] \"I love that people think that we are magical wizards that control the world. And then I get to burst people's bubble...\" \n\n[00:14:31] \"I also love Data science because I blink and something new has come out that has fundamentally changed the way I did things. Literally every single day there's something new, there's a new package, there's a new technique, there's a new finding. There's a new paper that comes out. And I get to rethink what I learned in school. I get to rethink what I've done practically over the years. And I love that.\"\n\n[00:18:42] \"You're hired to solve a very specific problem. And the problems are usually in three categories: How are you going to increase usage of something? How are you going to increase revenue? Or how are you going to increase engagement on something?\"\n\nHIGHLIGHTS FROM THE SHOW\n\n[00:03:14] Where Siann grew up and what it was like there\n\n[00:05:30] The immigrant experience\n\n[00:06:18] What Siann was like in high school\n\n[00:08:13] The journey into data science\n\n[00:10:50] How data science is used in political science\n\n[00:14:03] What do you love most about being a data scientist?\n\n[00:15:21] Do you consider Data science machine learning to be an art or purely a hard science? \n\n[00:17:58] What role do you think being creative and curious plays in being successful as a Data scientist?\n\n[00:20:57] What is a model and why is it that we even build them in the first place?\n\n[00:22:39] How can we use algorithms to build models with equality and equality?\n\n[00:28:25] How to make sure you’re building a fair model\n\n[00:29:20] Some tips for feature engineering\n\n[00:35:53] Project idea for survey data\n\n[00:36:19] The importance of MLOps\n\n[00:37:51] Communicating model results with business stakeholders\n\n[00:40:17] The non-obvious skills you need for success\n\n[00:46:22] Communicating with executives\n\n[00:48:39] Don’t be afraid to apply for a job just because the description looks crazy\n\n[00:52:10] Advice for women in STEM\n\n[00:55:23] How to foster diversity in data science\n\n[00:58:59] It’s 100 years in the future, what do you want to be remembered for?\n\n[00:59:46] The random roundSpecial Guest: Sian Lewis.","content_html":"Siann is a lead data scientist and analytics manager at Booz Allen Hamilton where she helps her stakeholders cut through the clutter to make better decisions, and leads a team that transforms complex problems into simple solutions.
\n\nFor her contributions to data science and social good, she’s been awarded the 2020 Women of Color in STEM All Star award, the 2019 DCFemTech award, and the 2017 Prince George’s County, MD 40 Under 40 honoree.
\n\nFIND SIAN ONLINE
\n\nLinkedIn: https://www.linkedin.com/in/allsian/
\n\nWebsite: https://www.sianlewis.org/
\n\nCorporate Profile: https://www.boozallen.com/e/insight/people-profiles/sian-lewis.html
\n\nQUOTES
\n\n[00:04:17] "When you're an immigrant, you find any form of these social enclaves. Wherever you are with people who are similar to you, who are from similar countries as you, you form tight knit communities."
\n\n[00:08:29] "You know, I went to grad school. I was terrible at it. I didn't want to be there. And I actually learned that I had no interest in actually anything health care related. So I quit after great anguish, great terror. And I was like, oh, my God, what am I going to do with my life?"
\n\n[00:14:08] "I love that people think that we are magical wizards that control the world. And then I get to burst people's bubble..."
\n\n[00:14:31] "I also love Data science because I blink and something new has come out that has fundamentally changed the way I did things. Literally every single day there's something new, there's a new package, there's a new technique, there's a new finding. There's a new paper that comes out. And I get to rethink what I learned in school. I get to rethink what I've done practically over the years. And I love that."
\n\n[00:18:42] "You're hired to solve a very specific problem. And the problems are usually in three categories: How are you going to increase usage of something? How are you going to increase revenue? Or how are you going to increase engagement on something?"
\n\nHIGHLIGHTS FROM THE SHOW
\n\n[00:03:14] Where Siann grew up and what it was like there
\n\n[00:05:30] The immigrant experience
\n\n[00:06:18] What Siann was like in high school
\n\n[00:08:13] The journey into data science
\n\n[00:10:50] How data science is used in political science
\n\n[00:14:03] What do you love most about being a data scientist?
\n\n[00:15:21] Do you consider Data science machine learning to be an art or purely a hard science?
\n\n[00:17:58] What role do you think being creative and curious plays in being successful as a Data scientist?
\n\n[00:20:57] What is a model and why is it that we even build them in the first place?
\n\n[00:22:39] How can we use algorithms to build models with equality and equality?
\n\n[00:28:25] How to make sure you’re building a fair model
\n\n[00:29:20] Some tips for feature engineering
\n\n[00:35:53] Project idea for survey data
\n\n[00:36:19] The importance of MLOps
\n\n[00:37:51] Communicating model results with business stakeholders
\n\n[00:40:17] The non-obvious skills you need for success
\n\n[00:46:22] Communicating with executives
\n\n[00:48:39] Don’t be afraid to apply for a job just because the description looks crazy
\n\n[00:52:10] Advice for women in STEM
\n\n[00:55:23] How to foster diversity in data science
\n\n[00:58:59] It’s 100 years in the future, what do you want to be remembered for?
\n\n[00:59:46] The random round
Special Guest: Sian Lewis.
","summary":"","date_published":"2021-02-12T00:00:00.000-05:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/13cc86e3-701d-473a-9eb0-e385b9fca0cf.mp3","mime_type":"audio/mpeg","size_in_bytes":62564380,"duration_in_seconds":3910}]},{"id":"87a0fc65-3f29-45aa-8f65-3e3e3435ea2f","title":"Comet ML Office Hours 1 - 07FEB2021","url":"https://theartistsofdatascience.fireside.fm/comet-ml-1","content_text":"Comet provides a self-hosted and cloud-based meta machine learning platform allowing data scientists and teams to track, compare, explain and optimize experiments and models. Backed by thousands of users and multiple Fortune 100 companies, Comet provides insights and data to build better, more accurate AI models while improving productivity, collaboration and visibility across teams.\n\nRegister for future sessions here: http://bit.ly/comet-ml-oh\n\nCheckout Comet ML by visiting: https://www.comet.ml/\n\nOr on Twitter: https://twitter.com/CometML\n\nConnect with Ayodele \n\nLinkedIn: https://www.linkedin.com/in/ayodeleodubela/ \n\nTwitter: https://twitter.com/DataSciBae\n\nCheck out her course on LinkedIn Learning: https://www.linkedin.com/learning/supervised-learning-essential-training/supervised-machine-learning-and-the-technology-boom\n\n[00:00:24] Getting to know my co-host, Ayodele\n\n[00:04:01] Ayodele talks about her new course on LinkedIn Learning\n\n[00:04:57] What is a data evangelist?\n\n[00:06:04] Why Comet ML decided to pair up with The Artists of Data Science\n\n[00:07:20] Where Comet ML fits into the machine learning lifecycle\n\n[00:12:58] What are the do’s and don'ts for anyone who wants to get into machine learning?\n\n[00:21:23] How to find labels for data in an image recognition project?\n\n[00:25:55] MLOps Engineer vs Machine Learning Engineer?\n\n[00:30:04] How do we convince senior leaders that we need an ML solution?\n\n[00:40:50] The trials and tribulations of being the only data scientist in an organization: dealing with what to learn and imposter syndrome.\n\n[00:53:37] How to learn more about a company\n\n[00:57:39] Struggles with linear algebra\n\n[01:02:56] Building data pipelines\n\n[01:04:25] Paying for internships","content_html":"Comet provides a self-hosted and cloud-based meta machine learning platform allowing data scientists and teams to track, compare, explain and optimize experiments and models. Backed by thousands of users and multiple Fortune 100 companies, Comet provides insights and data to build better, more accurate AI models while improving productivity, collaboration and visibility across teams.
\n\nRegister for future sessions here: http://bit.ly/comet-ml-oh
\n\nCheckout Comet ML by visiting: https://www.comet.ml/
\n\nOr on Twitter: https://twitter.com/CometML
\n\nConnect with Ayodele
\n\nLinkedIn: https://www.linkedin.com/in/ayodeleodubela/
\n\nTwitter: https://twitter.com/DataSciBae
\n\nCheck out her course on LinkedIn Learning: https://www.linkedin.com/learning/supervised-learning-essential-training/supervised-machine-learning-and-the-technology-boom
\n\n[00:00:24] Getting to know my co-host, Ayodele
\n\n[00:04:01] Ayodele talks about her new course on LinkedIn Learning
\n\n[00:04:57] What is a data evangelist?
\n\n[00:06:04] Why Comet ML decided to pair up with The Artists of Data Science
\n\n[00:07:20] Where Comet ML fits into the machine learning lifecycle
\n\n[00:12:58] What are the do’s and don'ts for anyone who wants to get into machine learning?
\n\n[00:21:23] How to find labels for data in an image recognition project?
\n\n[00:25:55] MLOps Engineer vs Machine Learning Engineer?
\n\n[00:30:04] How do we convince senior leaders that we need an ML solution?
\n\n[00:40:50] The trials and tribulations of being the only data scientist in an organization: dealing with what to learn and imposter syndrome.
\n\n[00:53:37] How to learn more about a company
\n\n[00:57:39] Struggles with linear algebra
\n\n[01:02:56] Building data pipelines
\n\n[01:04:25] Paying for internships
","summary":"","date_published":"2021-02-11T00:00:00.000-05:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/87a0fc65-3f29-45aa-8f65-3e3e3435ea2f.mp3","mime_type":"audio/mpeg","size_in_bytes":40514326,"duration_in_seconds":4286}]},{"id":"c5449a63-85bf-4cef-825f-0fecb225843c","title":"Data Science Happy Hour 18 | 05FEB2021","url":"https://theartistsofdatascience.fireside.fm/oh18","content_text":"The Data Science Happy Hours keep getting happier! \n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh\n\nIf you want to interact with me multiple times a week, join Data Science Dream Job for 70% off: http://dsdj.co/artists70\n\nWatch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm\n\nWe were voted one of the top ten data science podcasts by FeedSpot - check it out here: https://blog.feedspot.com/data_science_podcasts/","content_html":"The Data Science Happy Hours keep getting happier!
\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh
\n\nIf you want to interact with me multiple times a week, join Data Science Dream Job for 70% off: http://dsdj.co/artists70
\n\nWatch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm
\n\nWe were voted one of the top ten data science podcasts by FeedSpot - check it out here: https://blog.feedspot.com/data_science_podcasts/
","summary":"","date_published":"2021-02-07T10:00:00.000-05:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/c5449a63-85bf-4cef-825f-0fecb225843c.mp3","mime_type":"audio/mpeg","size_in_bytes":40063165,"duration_in_seconds":4319}]},{"id":"b978336a-a719-4cab-aaa9-b99ea38365d9","title":"From Cult Leader to Data Scientist | Kurtis Pykes","url":"https://theartistsofdatascience.fireside.fm/kurtis-pykes","content_text":"Kurtis Pykes comes by the show to talk about how he got into data science. He's got an unique back story which includes being a cult leader. We touch on a wide range of topics from how to get experience in data science without a data science job to how writing blogs can help you become a better data scientist.\n\nIt's an awesome episode that you won't want to miss!\n\nFIND KURTIS ONLINE\n\nMedium: https://kurtispykes.medium.com/\n\nLinkedIn: https://www.linkedin.com/in/kurtispykes/Special Guest: Kurtis Pykes.","content_html":"Kurtis Pykes comes by the show to talk about how he got into data science. He's got an unique back story which includes being a cult leader. We touch on a wide range of topics from how to get experience in data science without a data science job to how writing blogs can help you become a better data scientist.
\n\nIt's an awesome episode that you won't want to miss!
\n\nFIND KURTIS ONLINE
\n\nMedium: https://kurtispykes.medium.com/
\n\nLinkedIn: https://www.linkedin.com/in/kurtispykes/
Special Guest: Kurtis Pykes.
","summary":"","date_published":"2021-02-05T00:00:00.000-05:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/b978336a-a719-4cab-aaa9-b99ea38365d9.mp3","mime_type":"audio/mpeg","size_in_bytes":43748890,"duration_in_seconds":4570}]},{"id":"4b94d8a8-ddaf-4b87-834b-c01ec09cb48f","title":"Strategic Problem Solving for Data Scientists | Fred Pelard","url":"https://theartistsofdatascience.fireside.fm/fred-pelard","content_text":"For the last 20 years, Fred been lecturing on strategic thinking and complex problem solving; with an audience that has included the CEOs and management teams of major corporations and consulting firms around the world. Today he’s here to talk to us about his book. And share some tips with us on how we can be better problem solvers and more strategic.\n\nFIND FRED ONLINE\n\nWebsite: https://www.fredpelard.com/\n\nLinkedIn: https://www.linkedin.com/in/fredpelard/\n\nTwitter: https://twitter.com/fredpelard\n\nQUOTES\n\n[00:11:10] \"When you're solving problems, you're starting point pretty much every time is going to be complexity. If it's not complex, it doesn't need solving\"\n\n[00:14:05] \"When your chest is puffed out, you know the answer. You're in expert mode.\"\n\n[00:17:58] \"A lot of what these profession share is one similarity, which is a lot of the essence of their work is in the past. Lawyers solve past problems. Investigative journalists reveal past crimes. Engineersing actions build things in the present. But you see a theme emerging. None of these people really focus on the future. And so when you focus on the future, the data runs out of road and you have to use a different method.\"\n\n[00:20:29] \"You don't need real Data to have real options. You need real Data to have real solutions. And that's one of the slight drawbacks of a lot of the people I work with who tend to be analytical in their mindset.\"\n\n[00:23:57] \"I want to know which of my ideas is wrong early so I can reallocate my scarce time and resources towards the ones that work. And then once you've done that, now you feel very confident in your ideas.\"\n\n[00:25:17] \"It's creative first and analytical second. So first, have lots of ideas about a problem in the future and then bring the cavalry of the data to sort of whittled them down to one.\"\n\nSHOW NOTES\n\n[00:01:35] Guest introduction\n\n[00:02:10] Where Fred grew up and what it was like there\n\n[00:03:15] What Fred was like in high school\n\n[00:03:45] The transition from rocket science to the business world\n\n[00:05:09] The inspiration for the book: How to Be Strategic\n\n[00:07:59] Being strategic is a mindset\n\n[00:10:47] Complexity, completion, clarity, certainty, and conviction. \n\n[00:13:43] The expert mode of problem solving\n\n[00:15:33] The analytical mode of problem solving\n\n[00:17:34] The creative mode of problem solving\n\n[00:20:29] Solutions versus options\n\n[00:21:29] And moving on now to the strategic method. Talk to us about that and maybe give us the example and some benefits like you're just doing.\n\n[00:27:51] The difference between qualitative and quantitative problems\n\n[00:30:28] Qualitative problems have a unique set of challenges\n\n[00:37:26] The power of positive framing\n\n[00:40:59] A twist on The Lean Startup Philosophy\n\n[00:49:10] Sell your ideas with impactful words\n\n[00:53:42] Memorable metrics \n\n[00:58:02] Road test your metrics\n\n[00:58:59] How to create a compelling story\n\n[01:02:30] It’s 100 years in the future, what do you want to be remembered for?\n\n[01:04:17] The Random RoundSpecial Guest: Fred Pelard.","content_html":"For the last 20 years, Fred been lecturing on strategic thinking and complex problem solving; with an audience that has included the CEOs and management teams of major corporations and consulting firms around the world. Today he’s here to talk to us about his book. And share some tips with us on how we can be better problem solvers and more strategic.
\n\nFIND FRED ONLINE
\n\nWebsite: https://www.fredpelard.com/
\n\nLinkedIn: https://www.linkedin.com/in/fredpelard/
\n\nTwitter: https://twitter.com/fredpelard
\n\nQUOTES
\n\n[00:11:10] "When you're solving problems, you're starting point pretty much every time is going to be complexity. If it's not complex, it doesn't need solving"
\n\n[00:14:05] "When your chest is puffed out, you know the answer. You're in expert mode."
\n\n[00:17:58] "A lot of what these profession share is one similarity, which is a lot of the essence of their work is in the past. Lawyers solve past problems. Investigative journalists reveal past crimes. Engineersing actions build things in the present. But you see a theme emerging. None of these people really focus on the future. And so when you focus on the future, the data runs out of road and you have to use a different method."
\n\n[00:20:29] "You don't need real Data to have real options. You need real Data to have real solutions. And that's one of the slight drawbacks of a lot of the people I work with who tend to be analytical in their mindset."
\n\n[00:23:57] "I want to know which of my ideas is wrong early so I can reallocate my scarce time and resources towards the ones that work. And then once you've done that, now you feel very confident in your ideas."
\n\n[00:25:17] "It's creative first and analytical second. So first, have lots of ideas about a problem in the future and then bring the cavalry of the data to sort of whittled them down to one."
\n\nSHOW NOTES
\n\n[00:01:35] Guest introduction
\n\n[00:02:10] Where Fred grew up and what it was like there
\n\n[00:03:15] What Fred was like in high school
\n\n[00:03:45] The transition from rocket science to the business world
\n\n[00:05:09] The inspiration for the book: How to Be Strategic
\n\n[00:07:59] Being strategic is a mindset
\n\n[00:10:47] Complexity, completion, clarity, certainty, and conviction.
\n\n[00:13:43] The expert mode of problem solving
\n\n[00:15:33] The analytical mode of problem solving
\n\n[00:17:34] The creative mode of problem solving
\n\n[00:20:29] Solutions versus options
\n\n[00:21:29] And moving on now to the strategic method. Talk to us about that and maybe give us the example and some benefits like you're just doing.
\n\n[00:27:51] The difference between qualitative and quantitative problems
\n\n[00:30:28] Qualitative problems have a unique set of challenges
\n\n[00:37:26] The power of positive framing
\n\n[00:40:59] A twist on The Lean Startup Philosophy
\n\n[00:49:10] Sell your ideas with impactful words
\n\n[00:53:42] Memorable metrics
\n\n[00:58:02] Road test your metrics
\n\n[00:58:59] How to create a compelling story
\n\n[01:02:30] It’s 100 years in the future, what do you want to be remembered for?
\n\n[01:04:17] The Random Round
Special Guest: Fred Pelard.
","summary":"","date_published":"2021-01-29T00:00:00.000-05:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/4b94d8a8-ddaf-4b87-834b-c01ec09cb48f.mp3","mime_type":"audio/mpeg","size_in_bytes":86993050,"duration_in_seconds":4349}]},{"id":"d48307de-2c22-4ed3-a5c6-473b392147d6","title":"Data Science Happy Hour 17, 22JAN2021","url":"https://theartistsofdatascience.fireside.fm/oh17","content_text":"The Data Science Happy Hours keep getting happier! \n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh\n\nIf you want to interact with me multiple times a week, join Data Science Dream Job for 70% off: http://dsdj.co/artists70\n\nWatch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm\n\nWe were voted one of the top ten data science podcasts by FeedSpot - check it out here: https://blog.feedspot.com/data_science_podcasts/","content_html":"The Data Science Happy Hours keep getting happier!
\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh
\n\nIf you want to interact with me multiple times a week, join Data Science Dream Job for 70% off: http://dsdj.co/artists70
\n\nWatch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm
\n\nWe were voted one of the top ten data science podcasts by FeedSpot - check it out here: https://blog.feedspot.com/data_science_podcasts/
","summary":"","date_published":"2021-01-24T17:45:00.000-05:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/d48307de-2c22-4ed3-a5c6-473b392147d6.mp3","mime_type":"audio/mpeg","size_in_bytes":49453516,"duration_in_seconds":5240}]},{"id":"218b3273-97c5-4e6d-a2f1-dfd0651d5810","title":"The Science of Creating Your Own Luck | Christian Busch","url":"https://theartistsofdatascience.fireside.fm/christian-busch","content_text":"Dr. Busch is the Director of the Global Economy Program at New York University’s Center for Global Affairs where he teaches on purpose-driven leadership, impact entrepreneurship, social innovation, and emerging markets. Today he’s here to talk about his latest book “The Serendipity Mindset” – which develops a science-based framework for individuals and companies to help prompt and leverage positive accidents.\n\nFIND CHRISTIAN ONLINE\n\nWEBSITE: https://theserendipitymindset.com/\n\nLINKEDIN: https://www.linkedin.com/in/christianwbusch/\n\nTWITTER: https://twitter.com/ChrisSerendip\n\nQUOTES\n\n[00:05:09] \"Serendipity is really this kind of smart luck. So, it's very different from the blind luck.\"\n\n[00:11:00] \"In the end, it's all about connecting dots, right? It's all about saying, what is that kind of moment where, like you bring two potentially previously disparate things together.\" \n\n[00:14:03] \"I think then essentially, once we overcome this bias to not underestimate the unexpected, we can create this muscle. That's the muscle that allows us to essentially make the best out of it.\"\n\n[00:15:45] \"We all feel like impostors at times. We all feel like, you know, that we haven't figured it all out. And that's fine.\"\n\n[00:23:36] \"I will still everyday talk with at least one other person to make them feel better about themselves. And by doing that, that gives me meaning. And I think that kind of reminded me, right, that in every situation you can always reframe something.\"\n\n[00:30:06] \"The core idea is really that it's not only about knowledge that's power, but it's really kind of informational things that get seated. Then you can create knowledge out of it, unexpected.\"\n\n[00:33:58] \"We create the most serendipity when we think constantly about how does this relate now to something I've read, to something I talked with a friend, and how can I introduce them to someone and something? \"\n\n[00:41:50] \"Serendipity is all about, somehow, sagacity. And it is all about kind of seeing something in the moment and making sense out of it. But, also then again, having the tenacity to to go through with it.\"\n\n[00:44:18] \"I think the courage that comes from being vulnerable, and the courage that comes from admitting that you haven't had it all figured out then empowers other people as well. Because it gives them the license to come up with new ideas.\"\n\nSHOW NOTES\n\n[00:01:38] Guest introduction\n\n[00:02:51] What kind of kid were you in high school?\n\n[00:03:25] How different is life now than what you had imagined it would be?\n\n[00:04:07] what was the journey like from rebellious teenager to now Dr. Christian Busch? \n\n[00:05:00] How do you define serendipity? What is this thing?\n\n[00:06:32] The three types of serendipity\n\n[00:08:46] does serendipity have an anatomy? And if so, what does that anatomy look like?\n\n[00:10:50] The concept of bisociation\n\n[00:12:29] The biggest barriers to serendipity\n\n[00:14:03] The serendipity muscle\n\n[00:14:50] Alertness and serendipity\n\n[00:19:27] The importance of self-talk for serendipity\n\n[00:21:53] The reticular activating system and serendipity\n\n[00:24:43] How we can do a better job at defining problem statements\n\n[00:27:20] The two basic questions you should be asking\n\n[00:29:26] Information is at the core of life's opportunities\n\n[00:31:52] Networking in the COVID era\n\n[00:36:35] Remixing, rebooting and deconstruction\n\n[00:40:31] What can Seneca teach us about serendipity?\n\n[00:45:24] Why you shouldn’t be afraid to admit you don’t know something\n\n[00:50:44] How can we change the way that we speak to ensure that we're opening ourselves up for serendipity?\n\n[00:53:19] It's one hundred years in the future, what do you want to be remembered for?\n\n[00:55:38] The random roundSpecial Guest: Christian Busch.","content_html":"Dr. Busch is the Director of the Global Economy Program at New York University’s Center for Global Affairs where he teaches on purpose-driven leadership, impact entrepreneurship, social innovation, and emerging markets. Today he’s here to talk about his latest book “The Serendipity Mindset” – which develops a science-based framework for individuals and companies to help prompt and leverage positive accidents.
\n\nFIND CHRISTIAN ONLINE
\n\nWEBSITE: https://theserendipitymindset.com/
\n\nLINKEDIN: https://www.linkedin.com/in/christianwbusch/
\n\nTWITTER: https://twitter.com/ChrisSerendip
\n\nQUOTES
\n\n[00:05:09] "Serendipity is really this kind of smart luck. So, it's very different from the blind luck."
\n\n[00:11:00] "In the end, it's all about connecting dots, right? It's all about saying, what is that kind of moment where, like you bring two potentially previously disparate things together."
\n\n[00:14:03] "I think then essentially, once we overcome this bias to not underestimate the unexpected, we can create this muscle. That's the muscle that allows us to essentially make the best out of it."
\n\n[00:15:45] "We all feel like impostors at times. We all feel like, you know, that we haven't figured it all out. And that's fine."
\n\n[00:23:36] "I will still everyday talk with at least one other person to make them feel better about themselves. And by doing that, that gives me meaning. And I think that kind of reminded me, right, that in every situation you can always reframe something."
\n\n[00:30:06] "The core idea is really that it's not only about knowledge that's power, but it's really kind of informational things that get seated. Then you can create knowledge out of it, unexpected."
\n\n[00:33:58] "We create the most serendipity when we think constantly about how does this relate now to something I've read, to something I talked with a friend, and how can I introduce them to someone and something? "
\n\n[00:41:50] "Serendipity is all about, somehow, sagacity. And it is all about kind of seeing something in the moment and making sense out of it. But, also then again, having the tenacity to to go through with it."
\n\n[00:44:18] "I think the courage that comes from being vulnerable, and the courage that comes from admitting that you haven't had it all figured out then empowers other people as well. Because it gives them the license to come up with new ideas."
\n\nSHOW NOTES
\n\n[00:01:38] Guest introduction
\n\n[00:02:51] What kind of kid were you in high school?
\n\n[00:03:25] How different is life now than what you had imagined it would be?
\n\n[00:04:07] what was the journey like from rebellious teenager to now Dr. Christian Busch?
\n\n[00:05:00] How do you define serendipity? What is this thing?
\n\n[00:06:32] The three types of serendipity
\n\n[00:08:46] does serendipity have an anatomy? And if so, what does that anatomy look like?
\n\n[00:10:50] The concept of bisociation
\n\n[00:12:29] The biggest barriers to serendipity
\n\n[00:14:03] The serendipity muscle
\n\n[00:14:50] Alertness and serendipity
\n\n[00:19:27] The importance of self-talk for serendipity
\n\n[00:21:53] The reticular activating system and serendipity
\n\n[00:24:43] How we can do a better job at defining problem statements
\n\n[00:27:20] The two basic questions you should be asking
\n\n[00:29:26] Information is at the core of life's opportunities
\n\n[00:31:52] Networking in the COVID era
\n\n[00:36:35] Remixing, rebooting and deconstruction
\n\n[00:40:31] What can Seneca teach us about serendipity?
\n\n[00:45:24] Why you shouldn’t be afraid to admit you don’t know something
\n\n[00:50:44] How can we change the way that we speak to ensure that we're opening ourselves up for serendipity?
\n\n[00:53:19] It's one hundred years in the future, what do you want to be remembered for?
\n\n[00:55:38] The random round
Special Guest: Christian Busch.
","summary":"","date_published":"2021-01-22T00:00:00.000-05:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/218b3273-97c5-4e6d-a2f1-dfd0651d5810.mp3","mime_type":"audio/mpeg","size_in_bytes":36295142,"duration_in_seconds":3511}]},{"id":"ae3027a1-26d8-417f-a6db-c1a04b10d5fd","title":"Data Science Happy Hours 16, 15JAN2021","url":"https://theartistsofdatascience.fireside.fm/oh16","content_text":"The Data Science Happy Hours keep getting happier! \n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh\n\nIf you want to interact with me multiple times a week, join Data Science Dream Job for 70% off: http://dsdj.co/artists70\n\nWatch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm\n\nWe were voted one of the top ten data science podcasts by FeedSpot - check it out here: https://blog.feedspot.com/data_science_podcasts/Special Guests: Carlos Mercado and Santona Tuli, PhD.","content_html":"The Data Science Happy Hours keep getting happier!
\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh
\n\nIf you want to interact with me multiple times a week, join Data Science Dream Job for 70% off: http://dsdj.co/artists70
\n\nWatch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm
\n\nWe were voted one of the top ten data science podcasts by FeedSpot - check it out here: https://blog.feedspot.com/data_science_podcasts/
Special Guests: Carlos Mercado and Santona Tuli, PhD.
","summary":"","date_published":"2021-01-17T00:00:00.000-05:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/ae3027a1-26d8-417f-a6db-c1a04b10d5fd.mp3","mime_type":"audio/mpeg","size_in_bytes":50545227,"duration_in_seconds":4970}]},{"id":"ef7c540a-267c-4f7b-b68a-69ccea08391b","title":"Be Remarkable | Sundas Khalid","url":"https://theartistsofdatascience.fireside.fm/sundas-khalid","content_text":"Sundas is the first woman in her family to graduate university, earning her Bachelors from the University of Washington where she graduated as the class valedictorian. She’s currently a Senior Analytical Lead at Google. And before that she was a Data Scientist at Amazon, where she was awarded for her work driving large-scale experimentations and data science initiatives.\n\nFIND SUNDAS ONLINE\n\nWebsite: https://sundaskhalid.com/\n\nLinkedIn: https://www.linkedin.com/in/sundaskhalid/\n\nMedium: https://sundaskhalid.medium.com/\n\nTwitter: https://twitter.com/sundaskhalid6\n\nQUOTES\n\n[00:17:33] \"A difference between a good and a great data scientist is communication. We know that Data science can be a complicated field. The purpose of doing all the data science work is so we can impact the bottom line of the business. And in order to do that, a lot of the times we have to pitch our own work to non tech individuals.\"\n\n[00:22:17] \"In my life I have failed quite a bit of times. And every time I failed, I would get up again and try a different method. And try something else. And I will achieve it the second time, or the third time.\"\n\n[00:23:27] \"Do not change your goal, change your method. And that's what this whole growth mindset is about. Keep learning and try different things. If it doesn't work one way, try a different way.\"\n\n[00:42:00] \"You need to have a really good elevator pitch ready and you need to make sure that you have done the research on the person that you are reaching out to when you're cold messaging somebody.\" \n\nSHOW NOTES\n\n[00:01:35] Guest introduction\n\n[00:02:47] How Sundas got into data science\n\n[00:06:47] Sundas shares some of her struggles \n\n[00:10:25] Where is data science headed in the next two to five years?\n\n[00:12:49] What do you think will be the scariest application of data science in the near future?\n\n[00:14:16] The things you should be concerned with while working with data\n\n[00:17:22] What separates the good data scientists from the great data scientists?\n\n[00:18:31] The tutorial trap\n\n[00:21:31] The importance of embracing failure\n\n[00:23:27] The growth mindset \n\n[00:24:26] Sundas was rejected from a U of W and then got accepted and graduated as class valedictorian\n\n[00:26:07] How to deal with imposter syndrome\n\n[00:31:57] How can we foster the inclusion of women in data science?\n\n[00:34:53] What are some soft skills you need to succeed?\n\n[00:36:21] How to be a better storyteller\n\n[00:37:39] Executive communication for data scientist\n\n[00:38:34] Don’t be afraid of looking like you don’t know something\n[00:40:15] Tips on networking\n\n[00:44:27] Sundas talks about her career coaching services\n\n[00:45:44] We should be allies for underrepresented groups in data science\n\n[00:50:33] It’s 100 years in the future, what do you want to be remembered for?\n\n[00:51:50] The random roundSpecial Guest: Sundas Khalid.","content_html":"Sundas is the first woman in her family to graduate university, earning her Bachelors from the University of Washington where she graduated as the class valedictorian. She’s currently a Senior Analytical Lead at Google. And before that she was a Data Scientist at Amazon, where she was awarded for her work driving large-scale experimentations and data science initiatives.
\n\nFIND SUNDAS ONLINE
\n\nWebsite: https://sundaskhalid.com/
\n\nLinkedIn: https://www.linkedin.com/in/sundaskhalid/
\n\nMedium: https://sundaskhalid.medium.com/
\n\nTwitter: https://twitter.com/sundaskhalid6
\n\nQUOTES
\n\n[00:17:33] "A difference between a good and a great data scientist is communication. We know that Data science can be a complicated field. The purpose of doing all the data science work is so we can impact the bottom line of the business. And in order to do that, a lot of the times we have to pitch our own work to non tech individuals."
\n\n[00:22:17] "In my life I have failed quite a bit of times. And every time I failed, I would get up again and try a different method. And try something else. And I will achieve it the second time, or the third time."
\n\n[00:23:27] "Do not change your goal, change your method. And that's what this whole growth mindset is about. Keep learning and try different things. If it doesn't work one way, try a different way."
\n\n[00:42:00] "You need to have a really good elevator pitch ready and you need to make sure that you have done the research on the person that you are reaching out to when you're cold messaging somebody."
\n\nSHOW NOTES
\n\n[00:01:35] Guest introduction
\n\n[00:02:47] How Sundas got into data science
\n\n[00:06:47] Sundas shares some of her struggles
\n\n[00:10:25] Where is data science headed in the next two to five years?
\n\n[00:12:49] What do you think will be the scariest application of data science in the near future?
\n\n[00:14:16] The things you should be concerned with while working with data
\n\n[00:17:22] What separates the good data scientists from the great data scientists?
\n\n[00:18:31] The tutorial trap
\n\n[00:21:31] The importance of embracing failure
\n\n[00:23:27] The growth mindset
\n\n[00:24:26] Sundas was rejected from a U of W and then got accepted and graduated as class valedictorian
\n\n[00:26:07] How to deal with imposter syndrome
\n\n[00:31:57] How can we foster the inclusion of women in data science?
\n\n[00:34:53] What are some soft skills you need to succeed?
\n\n[00:36:21] How to be a better storyteller
\n\n[00:37:39] Executive communication for data scientist
\n\n[00:38:34] Don’t be afraid of looking like you don’t know something
\n[00:40:15] Tips on networking
[00:44:27] Sundas talks about her career coaching services
\n\n[00:45:44] We should be allies for underrepresented groups in data science
\n\n[00:50:33] It’s 100 years in the future, what do you want to be remembered for?
\n\n[00:51:50] The random round
Special Guest: Sundas Khalid.
","summary":"","date_published":"2021-01-15T00:00:00.000-05:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/ef7c540a-267c-4f7b-b68a-69ccea08391b.mp3","mime_type":"audio/mpeg","size_in_bytes":69952854,"duration_in_seconds":3497}]},{"id":"39834f28-c612-43b9-973a-aa143d473184","title":"Data Science Happy Hours 15, 08JAN2021","url":"https://theartistsofdatascience.fireside.fm/oh15","content_text":"The Data Science Happy Hours keep getting happier! \n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh\n\nIf you want to interact with me multiple times a week, join Data Science Dream Job for 70% off: http://dsdj.co/artists70\n\nWatch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm\n\nWe were voted one of the top ten data science podcasts by FeedSpot - check it out here: https://blog.feedspot.com/data_science_podcasts/","content_html":"The Data Science Happy Hours keep getting happier!
\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh
\n\nIf you want to interact with me multiple times a week, join Data Science Dream Job for 70% off: http://dsdj.co/artists70
\n\nWatch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm
\n\nWe were voted one of the top ten data science podcasts by FeedSpot - check it out here: https://blog.feedspot.com/data_science_podcasts/
","summary":"","date_published":"2021-01-10T10:00:00.000-05:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/39834f28-c612-43b9-973a-aa143d473184.mp3","mime_type":"audio/mpeg","size_in_bytes":51222018,"duration_in_seconds":5414}]},{"id":"6d642eff-a051-4639-bb31-27f70bb0c167","title":"The Non-Obvious Skills You Need as a Data Scientist | Keith McCormick","url":"https://theartistsofdatascience.fireside.fm/keith-mccormick","content_text":"Keith is a sought after speaker, who routinely leads workshops at conferences. He’s given keynotes presentations at many international events and is an award winning instructor for UC Irvine's Predictive Analytics certificate program. \n\nYou may recognize him as the instructor of thirteen courses on LinkedIn Learning - where’s taught over 250,000 learners through his courses.\n\nFIND KEITH ONLINE\n\nWebsite: http://www.keithmccormick.com/\n\nTwitter: https://twitter.com/KMcCormickBlog\n\nAboutMe: https://about.me/keithmccormick\n\nCourses on LinkedIn Learning: https://www.linkedin.com/learning/instructors/keith-mccormick\n\nQUOTES\n\n[00:05:32] \"What I concluded was that 2012 was really the big year where the dominos started to fall, that we're calling everything A.I. now.\"\n\n[00:08:28] \"I think that most client organizations that I encounter, what they're missing, what prevents them from being effective is effective analytics middle management.\"\n\n[00:12:38] \"Because you always have to rethink things between a prototype and putting it into production. So the notion that you're going to dismiss anyone that's using any tool other than coding in the same coding language that the team has adopted, you're working off a false premise\"\n\n[00:14:10] \"It seems to me there's an inherent flaw there, if the entire Data science community recognizes that the job descriptions are nuts and therefore everybody should ignore them.\"\n\n[00:15:39] \"We put so much emphasis on the coding that there's this huge gap between running the code that creates something simple like a decision tree, and knowing the basic foundation and concepts of how the tree is growing and how to interpret it.\"\n\n[00:18:00] \"I would say from the statistics side of the house, I want to know if they know when to trust and when not to trust the data. I'm really big on that.\"\n\n[00:20:35] \"One thing I really don't like at all is when organizations use an external resource and they throw the data over the fence and then they just get the solution delivered on their desk. That's a nightmare.\" \n\n[00:22:39] \"I think that before someone contemplates leaving their current department and joining the Data science team, they should never do that until they've done a project from start to finish as a borrowed resource.\"\n\n[00:31:53] \"Your model is still degrading even if you're automatically rebuilding it. And the reason is that the model is only one step in a long process. You're also making assumptions about what data is relevant, what variables are used in the model, and all that.\"\n\n[00:48:03] \"Part of the problem is none of us know what data science is, right. And by that I mean that it's a puzzle that we haven't figured out yet. It's the term is being used in so many different ways.\"\n\nSHOW NOTES\n\n[00:01:31] Guest introduction\n\n[00:02:45] Keith’s journey into data science\n\n[00:04:42] How much more hyped data science has become since the 90s\n\n[00:05:32] What the year 2012 did for data science\n\n[00:06:29] How tools of the trade have impacted data science adoption in recent years\n\n[00:07:45] Excellent project idea for anybody that's listening\n\n[00:08:15] In order to make machine learning work, we need to have effective teams\n\n[00:10:11] Diversity, recruiting, and data science teams\n\n[00:12:09] How does groupthink inhibit or limit team effectiveness?\n\n[00:13:27] Why hiring and retention in analytics is broken, and how to fix it\n\n[00:15:23] The essential checkboxes for a data science candidate\n\n[00:18:50] The challenges of being the first data scientist in an organization\n\n[00:21:52] Remixing talent inside your organization\n\n[00:23:57] What is the goal of analytics?\n\n[00:25:55] What are insights and how do we use them?\n\n[00:28:06] The goal of achieving a deployable model\n\n[00:30:53] What to do once the model is deployed\n\n[00:34:57] How to measure ROI on your data science projects\n\n[00:36:18] What are some steps that we could take to turn a business problem into a data science research question?\n\n[00:38:06] The difference between a consulting data scientist and an internal data scientist\n\n[00:41:07] Freelancing as a data scientist\n\n[00:45:32] How to be a good leader in data science\n\n[00:47:52] Is statistics the same as data science?\n\n[00:50:04] Embrace ambiguity\n\n[00:52:35] The skill of cognitive empathy\n\n[00:55:35] Why it's important that we have a commitment to our craft\n\n[00:57:28] How can we be more effective with our persuasion skills in Data science?\n\n[01:00:32] The most important part of the data science lifecycle\n\n[01:02:47] Why you need a little skepticism\n\n[01:05:26] It’s 100 years in the future, what do you want to be remembered for?\n\n[01:06:39] The random roundSpecial Guest: Keith McCormick.","content_html":"Keith is a sought after speaker, who routinely leads workshops at conferences. He’s given keynotes presentations at many international events and is an award winning instructor for UC Irvine's Predictive Analytics certificate program.
\n\nYou may recognize him as the instructor of thirteen courses on LinkedIn Learning - where’s taught over 250,000 learners through his courses.
\n\nFIND KEITH ONLINE
\n\nWebsite: http://www.keithmccormick.com/
\n\nTwitter: https://twitter.com/KMcCormickBlog
\n\nAboutMe: https://about.me/keithmccormick
\n\nCourses on LinkedIn Learning: https://www.linkedin.com/learning/instructors/keith-mccormick
\n\nQUOTES
\n\n[00:05:32] "What I concluded was that 2012 was really the big year where the dominos started to fall, that we're calling everything A.I. now."
\n\n[00:08:28] "I think that most client organizations that I encounter, what they're missing, what prevents them from being effective is effective analytics middle management."
\n\n[00:12:38] "Because you always have to rethink things between a prototype and putting it into production. So the notion that you're going to dismiss anyone that's using any tool other than coding in the same coding language that the team has adopted, you're working off a false premise"
\n\n[00:14:10] "It seems to me there's an inherent flaw there, if the entire Data science community recognizes that the job descriptions are nuts and therefore everybody should ignore them."
\n\n[00:15:39] "We put so much emphasis on the coding that there's this huge gap between running the code that creates something simple like a decision tree, and knowing the basic foundation and concepts of how the tree is growing and how to interpret it."
\n\n[00:18:00] "I would say from the statistics side of the house, I want to know if they know when to trust and when not to trust the data. I'm really big on that."
\n\n[00:20:35] "One thing I really don't like at all is when organizations use an external resource and they throw the data over the fence and then they just get the solution delivered on their desk. That's a nightmare."
\n\n[00:22:39] "I think that before someone contemplates leaving their current department and joining the Data science team, they should never do that until they've done a project from start to finish as a borrowed resource."
\n\n[00:31:53] "Your model is still degrading even if you're automatically rebuilding it. And the reason is that the model is only one step in a long process. You're also making assumptions about what data is relevant, what variables are used in the model, and all that."
\n\n[00:48:03] "Part of the problem is none of us know what data science is, right. And by that I mean that it's a puzzle that we haven't figured out yet. It's the term is being used in so many different ways."
\n\nSHOW NOTES
\n\n[00:01:31] Guest introduction
\n\n[00:02:45] Keith’s journey into data science
\n\n[00:04:42] How much more hyped data science has become since the 90s
\n\n[00:05:32] What the year 2012 did for data science
\n\n[00:06:29] How tools of the trade have impacted data science adoption in recent years
\n\n[00:07:45] Excellent project idea for anybody that's listening
\n\n[00:08:15] In order to make machine learning work, we need to have effective teams
\n\n[00:10:11] Diversity, recruiting, and data science teams
\n\n[00:12:09] How does groupthink inhibit or limit team effectiveness?
\n\n[00:13:27] Why hiring and retention in analytics is broken, and how to fix it
\n\n[00:15:23] The essential checkboxes for a data science candidate
\n\n[00:18:50] The challenges of being the first data scientist in an organization
\n\n[00:21:52] Remixing talent inside your organization
\n\n[00:23:57] What is the goal of analytics?
\n\n[00:25:55] What are insights and how do we use them?
\n\n[00:28:06] The goal of achieving a deployable model
\n\n[00:30:53] What to do once the model is deployed
\n\n[00:34:57] How to measure ROI on your data science projects
\n\n[00:36:18] What are some steps that we could take to turn a business problem into a data science research question?
\n\n[00:38:06] The difference between a consulting data scientist and an internal data scientist
\n\n[00:41:07] Freelancing as a data scientist
\n\n[00:45:32] How to be a good leader in data science
\n\n[00:47:52] Is statistics the same as data science?
\n\n[00:50:04] Embrace ambiguity
\n\n[00:52:35] The skill of cognitive empathy
\n\n[00:55:35] Why it's important that we have a commitment to our craft
\n\n[00:57:28] How can we be more effective with our persuasion skills in Data science?
\n\n[01:00:32] The most important part of the data science lifecycle
\n\n[01:02:47] Why you need a little skepticism
\n\n[01:05:26] It’s 100 years in the future, what do you want to be remembered for?
\n\n[01:06:39] The random round
Special Guest: Keith McCormick.
","summary":"","date_published":"2021-01-08T00:00:00.000-05:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/6d642eff-a051-4639-bb31-27f70bb0c167.mp3","mime_type":"audio/mpeg","size_in_bytes":88709817,"duration_in_seconds":4435}]},{"id":"2e098264-b5f5-4680-a316-3a0c0d8cfb2b","title":"Are you DATAcated? | A Conversation with Kate Strachnyi","url":"https://theartistsofdatascience.fireside.fm/kate-strachnyi","content_text":"It's the first Friday of the month! That means its time for a conversations episode!\n\nKate Strachnyi is the founder of Story by Data & the DATAcated Academy. Story by Data is a LinkedIn content strategy for companies focused on innovation in artificial intelligence (AI), machine learning (ML), and data science. DATAcated Academy is a platform delivering training on data visualization best practices. \n\nKate's a LinkedIn Top Voice in Data Science & Analytics (2018 & 2019)\n\nWe have an excellent conversation talking about where she grew up, her early years, some the exciting projects she's worked on. We get to know Kate on a more personal level, and she shares things with us that she's never shared on any other podcast. Be sure to tune in!Special Guest: Kate Strachnyi.","content_html":"It's the first Friday of the month! That means its time for a conversations episode!
\n\nKate Strachnyi is the founder of Story by Data & the DATAcated Academy. Story by Data is a LinkedIn content strategy for companies focused on innovation in artificial intelligence (AI), machine learning (ML), and data science. DATAcated Academy is a platform delivering training on data visualization best practices.
\n\nKate's a LinkedIn Top Voice in Data Science & Analytics (2018 & 2019)
\n\nWe have an excellent conversation talking about where she grew up, her early years, some the exciting projects she's worked on. We get to know Kate on a more personal level, and she shares things with us that she's never shared on any other podcast. Be sure to tune in!
Special Guest: Kate Strachnyi.
","summary":"","date_published":"2021-01-01T00:00:00.000-05:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/2e098264-b5f5-4680-a316-3a0c0d8cfb2b.mp3","mime_type":"audio/mpeg","size_in_bytes":37017176,"duration_in_seconds":4101}]},{"id":"7e776abb-9138-4c9a-97c3-99ea5d716b0e","title":"Data Science Happy Hours 14, 18DEC2020","url":"https://theartistsofdatascience.fireside.fm/oh14","content_text":"The Data Science Happy Hours Holiday edition! You don't want to miss this one. We had some of the most influential names from the LinkedIn Data Science community in attendence!\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh\n\nIf you want to interact with me multiple times a week, join Data Science Dream Job for 70% off: http://dsdj.co/artists70\n\nWatch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm\n\nWe were voted one of the top ten data science podcasts by FeedSpot - check it out here: https://blog.feedspot.com/data_science_podcasts/\n\nChat Transcript: http://theartistsofdatascience.fireside.fm/articles/oh14-chat-transcript\n\nSHOW NOTES\n\n[00:02:05] Work-life balance of a data scientist\n\n[00:06:27] “So, to answer your question, data science is just a regular ass job”\n\n[00:08:45] Hello to everyone who joined!\n\n[00:09:58] So many ways to learn, which one should I choose?\n\n[00:12:26] Moving from project management to data science\n\n[00:14:19] Dave share’s his perspective after 20 years in the data industry\n\n[00:16:18] Tips for being more active on LinkedIn\n\n[00:17:16] Susan – The Classification Guru – shares her tips for posting on LinkedIn\n\n[00:18:45] Giovanna shares her tips on being positive on LinkedIn\n\n[00:20:27] Greg Coquillo - LinkedIn Top Voice for Data Science 2020 – shares his tips on creating content and networking on LinkedIn\n\n[00:24:06] What are your tips for debugging code?\n\n[00:24:38] Liuna – Senior Data Scientist at IBM – shares her tips for debugging code\n\n[00:25:28] Srivatsan Srinivasan shares his tips on how to debug code\n\n[00:28:35] Carlos Mercado talks about Debugging in R\n\n[00:30:21] Monica Kay Royal shares her secrets for debugging code\n\n[00:31:15] George Firican shares some tips as well!\n\n[00:32:08] Joe Reis closes out the discussion on debugging\n\n[00:37:32] A big debate on the never end Python vs R question\n\n[00:42:47] What are your expectations from someone in a junior data scientist position?\n\n[00:43:14] Sarah shares what she’s looking for in a junior level candidate\n\n[00:44:42] What Srivatsan looks for in a junior data scientist\n\n[00:45:53] Monica, what do you look for in a junior data scientist?\n\n[00:46:39] What Vin Vashishta looks for in a junior data scientist?\n\n[00:47:51] Liuna what do you look for in a junior data scientist?\n\n[00:49:09] Mikiko what do you look for in a junior data scientist?\n\n[00:51:55] What does Kamrin look for in an junior data scientist?\n\n[00:53:39] How to stay motivated and re-evaluate your hustle when you’re in the job search?\n\n[00:56:24] Sarah shares some tips on turning applications into interviews\n\n[00:58:19] Mikiko shares some words of encouragement as well\n\n[01:01:17] Ben shares some advice on what to so with the crazy job descriptions\n\n[01:05:25] Jean-Sebastian shares some advice as well\n\n[01:08:25] Greg Coquillo adds to the discussion on what to do when you don’t hear back from applying for a job\n\n[01:10:13] Liuna closes out the discussion\n\n[01:11:51] Eric comes in and shares a LinkedIn hack. Also asks a question on scoring clusters\n\n[01:14:06] Dave has an answer to this tough question\n\n[01:17:06] Greg has a question on Federated Learning\n\n[01:27:13] Switching to python from a SAS background\n\n[01:34:04] We congratulate a member of the community on landing a job!\n\n[01:38:39] What’s the difference between a data scientist and a data analyst?\n\n[01:58:41] Why do you do these happy hours?Special Guests: Carlos Mercado, David Tello, Greg Coquillo, Mikiko Bazeley, Srivatsan Srinivasan, and Vin Vashishta.","content_html":"The Data Science Happy Hours Holiday edition! You don't want to miss this one. We had some of the most influential names from the LinkedIn Data Science community in attendence!
\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh
\n\nIf you want to interact with me multiple times a week, join Data Science Dream Job for 70% off: http://dsdj.co/artists70
\n\nWatch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm
\n\nWe were voted one of the top ten data science podcasts by FeedSpot - check it out here: https://blog.feedspot.com/data_science_podcasts/
\n\nChat Transcript: http://theartistsofdatascience.fireside.fm/articles/oh14-chat-transcript
\n\nSHOW NOTES
\n\n[00:02:05] Work-life balance of a data scientist
\n\n[00:06:27] “So, to answer your question, data science is just a regular ass job”
\n\n[00:08:45] Hello to everyone who joined!
\n\n[00:09:58] So many ways to learn, which one should I choose?
\n\n[00:12:26] Moving from project management to data science
\n\n[00:14:19] Dave share’s his perspective after 20 years in the data industry
\n\n[00:16:18] Tips for being more active on LinkedIn
\n\n[00:17:16] Susan – The Classification Guru – shares her tips for posting on LinkedIn
\n\n[00:18:45] Giovanna shares her tips on being positive on LinkedIn
\n\n[00:20:27] Greg Coquillo - LinkedIn Top Voice for Data Science 2020 – shares his tips on creating content and networking on LinkedIn
\n\n[00:24:06] What are your tips for debugging code?
\n\n[00:24:38] Liuna – Senior Data Scientist at IBM – shares her tips for debugging code
\n\n[00:25:28] Srivatsan Srinivasan shares his tips on how to debug code
\n\n[00:28:35] Carlos Mercado talks about Debugging in R
\n\n[00:30:21] Monica Kay Royal shares her secrets for debugging code
\n\n[00:31:15] George Firican shares some tips as well!
\n\n[00:32:08] Joe Reis closes out the discussion on debugging
\n\n[00:37:32] A big debate on the never end Python vs R question
\n\n[00:42:47] What are your expectations from someone in a junior data scientist position?
\n\n[00:43:14] Sarah shares what she’s looking for in a junior level candidate
\n\n[00:44:42] What Srivatsan looks for in a junior data scientist
\n\n[00:45:53] Monica, what do you look for in a junior data scientist?
\n\n[00:46:39] What Vin Vashishta looks for in a junior data scientist?
\n\n[00:47:51] Liuna what do you look for in a junior data scientist?
\n\n[00:49:09] Mikiko what do you look for in a junior data scientist?
\n\n[00:51:55] What does Kamrin look for in an junior data scientist?
\n\n[00:53:39] How to stay motivated and re-evaluate your hustle when you’re in the job search?
\n\n[00:56:24] Sarah shares some tips on turning applications into interviews
\n\n[00:58:19] Mikiko shares some words of encouragement as well
\n\n[01:01:17] Ben shares some advice on what to so with the crazy job descriptions
\n\n[01:05:25] Jean-Sebastian shares some advice as well
\n\n[01:08:25] Greg Coquillo adds to the discussion on what to do when you don’t hear back from applying for a job
\n\n[01:10:13] Liuna closes out the discussion
\n\n[01:11:51] Eric comes in and shares a LinkedIn hack. Also asks a question on scoring clusters
\n\n[01:14:06] Dave has an answer to this tough question
\n\n[01:17:06] Greg has a question on Federated Learning
\n\n[01:27:13] Switching to python from a SAS background
\n\n[01:34:04] We congratulate a member of the community on landing a job!
\n\n[01:38:39] What’s the difference between a data scientist and a data analyst?
\n\n[01:58:41] Why do you do these happy hours?
Special Guests: Carlos Mercado, David Tello, Greg Coquillo, Mikiko Bazeley, Srivatsan Srinivasan, and Vin Vashishta.
","summary":"","date_published":"2020-12-20T00:00:00.000-05:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/7e776abb-9138-4c9a-97c3-99ea5d716b0e.mp3","mime_type":"audio/mpeg","size_in_bytes":68865920,"duration_in_seconds":7351}]},{"id":"c49b717e-032d-4162-8ba6-dbb3d4cdf551","title":"The Recap | Harpreet Sahota","url":"https://theartistsofdatascience.fireside.fm/recap-2020","content_text":"The first solo episode ever - I hope you enjoy it!\n\nI also hope you enjoy the tunes I've put together for you to listen to as we wind down this week before the holidays!\n\nTracks included in this episode (not in order):\n\nhttps://www.youtube.com/watch?v=2uAQjcVQdWw\nhttps://www.youtube.com/watch?v=9yX7P_IeiNQ\nhttps://www.youtube.com/watch?v=zkNXQUwHaDU\nhttps://www.youtube.com/watch?v=o6wRHQu9BgM\nhttps://www.youtube.com/watch?v=147A3e4rg1w\nhttps://www.youtube.com/watch?v=zaw2Y9mAOjw\nhttps://www.youtube.com/watch?v=A7MFfVraKx0\nhttps://www.youtube.com/watch?v=gFhq0hJbJOw\nhttps://www.youtube.com/watch?v=_05nKCf7N9E\nhttps://www.youtube.com/watch?v=yTgXHQPcYSs\nhttps://www.youtube.com/watch?v=1GLEJEYDdfg","content_html":"The first solo episode ever - I hope you enjoy it!
\n\nI also hope you enjoy the tunes I've put together for you to listen to as we wind down this week before the holidays!
\n\nTracks included in this episode (not in order):
\n\nhttps://www.youtube.com/watch?v=2uAQjcVQdWw
\nhttps://www.youtube.com/watch?v=9yX7P_IeiNQ
\nhttps://www.youtube.com/watch?v=zkNXQUwHaDU
\nhttps://www.youtube.com/watch?v=o6wRHQu9BgM
\nhttps://www.youtube.com/watch?v=147A3e4rg1w
\nhttps://www.youtube.com/watch?v=zaw2Y9mAOjw
\nhttps://www.youtube.com/watch?v=A7MFfVraKx0
\nhttps://www.youtube.com/watch?v=gFhq0hJbJOw
\nhttps://www.youtube.com/watch?v=_05nKCf7N9E
\nhttps://www.youtube.com/watch?v=yTgXHQPcYSs
\nhttps://www.youtube.com/watch?v=1GLEJEYDdfg
The Data Science Happy Hours keep getting happier!
\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh
\n\nIf you want to interact with me multiple times a week, join Data Science Dream Job for 70% off: http://dsdj.co/artists70
\n\nWatch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm
\n\nWe were voted one of the top ten data science podcasts by FeedSpot - check it out here: https://blog.feedspot.com/data_science_podcasts/
\n\nChat Transcript: http://theartistsofdatascience.fireside.fm/articles/oh13-chat-transcript
\n\nSHOW NOTES
\n\n[00:02:18] How much time do you REALLY spend on data cleaning, and what are you cleaning?
\n\n[00:11:14] Breaking into data science from a product management role
\n\n[00:17:28] How to find the right data to work on for a project?
\n\n[00:21:22] Landing that first interview in data science
\n\n[00:30:09] Do I need to normalize my data to do a t-test or ANOVA test?
\n\n[00:34:20] How do you account for an event like COVID with your machine learning models?
\n\n[00:45:32] The various degrees of deployment of machine learning models
\n\n[00:52:22] What do you love about being a data scientist?
\n\n[01:04:24] Breaking into data science: masters or online programs?
\n\n[01:10:09] How to balance stakeholder expectations for quick delivery and deal with impatient business partners?
\n\n[01:20:39] How to move up in your career as a data scientist if you don’t want to be a manager
Special Guests: Brandon Quach, PhD, Greg Coquillo, and Mikiko Bazeley.
","summary":"","date_published":"2020-12-13T00:00:00.000-05:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/23b597c8-656e-4398-8572-2c050398bcbf.mp3","mime_type":"audio/mpeg","size_in_bytes":47136013,"duration_in_seconds":5208}]},{"id":"e6c4a30c-6c03-49d3-ad17-8e8fcf59e37d","title":"Be Like Marcus Aurelius | Donald Robertson","url":"https://theartistsofdatascience.fireside.fm/donald-robertson","content_text":"Donald is a trainer and former Cognitive-Behavioural Psychotherapist, with a background in academic philosophy, who specializes in eLearning and web development. He's been involved in research on the use of eLearning as a vehicle for delivering training in CBT self-help. He's also interested in the relationship between ancient Stoic philosophy and modern evidence-based approaches to psychological-resilience training.\n\nFIND DONALD ONLINE\n\nWebsite: https://donaldrobertson.name/\n\nLinkedIn: https://www.linkedin.com/in/donaldjrobertson/\n\nQUOTES\n\n[00:06:25] \"I realize that Stoicism was the type of philosophy that inspired cognitive behavioral therapy. And so everything kind of came together for me. I saw the connection between philosophy and psychotherapy and a particular type of philosophy that ended up spending the rest of my life studying. \"\n\n[00:10:41] \"Therapists and researchers developed what's called the cognitive theory of emotion, which says our emotions are shaped fundamentally by underlying beliefs or cognitions. And that happens to be essentially what the Stoics also believed 2300 years ago.\"\n\n[00:19:04] \"The Stoics want us to reappraise our values so that we realize that our own faculty of judgment is really important and our own character is really important. \"\n\n[00:20:18] \"Stoicism and Socratic philosophy are designed to counteract the use of rhetoric, propaganda, emotional tactics that the media use in the same way that the sophists used to use them to manipulate crowds in ancient Athens, in ancient Rome. \"\n\n[00:23:33] \"But just asking in casual conversation, what makes somebody wise rather than smart, clever, shrewd by interesting conversation. That's the type of conversation that Socrates thought we should be having.\"\n\n[00:24:54] \"Maybe wisdom consists in being able to look at what other people think is important and realizing that it isn't. And realizing that things that other people seem to be ignoring are actually much more important. That shift in our values changes our emotions or behavior or quality of life, potentially.\" \n\n[00:28:23] \"Wisdom is the fundamental virtue of understanding the value of things. And then justice consists in understanding and to practice in terms of how you relate to society and your family and your friends and other individuals.\" \n\n[00:29:32] \"Courage is a virtue that we require in order to overcome the passion or the emotion of fear and to master that.\" \n\n[01:12:56] \"Marcus wants us to recognize our own imperfection and bias that's integral to Stoicism, to realize that we are creatures of passion. None of us are perfect. None of the Stoics found those claimed to be gurus of perfect.\"\n\nSHOW NOTES\n\n[00:01:51] Guest Introduction\n\n[00:05:19] How different is life now than you had imagined it would be when you were younger\n\n[00:06:13] How psychotherapy lead Donald to Stoicism\n\n[00:08:09] The relationship between CBT and Stoicism\n\n[00:14:35] How could CBT and Stoicism help us stay focused in a world where everything is meant to distract us?\n\n[00:20:44] Human nature hasn’t changed much\n\n[00:22:31] The Stoic concept of practical wisdom.\n\n[00:25:56] The cardinal virtues \n\n[00:31:55] Passions versus virtues\n\n[00:38:33] People may think that those who practice Stoicism are cold or unemotional, but that's not \n\n[00:41:34] The circles of Hierocles\n\n[00:47:15] What can Marcus teach us about improving our relationship with ourselves?\n\n[00:52:49] Helping those around you flourish\n\n[00:54:42] Who are some of the teachers and mentors that Marcus talks about and what role did these teachers and mentors play in his life?\n\n[01:04:57] How Lucius Verus improved Marcus’ character\n\n[01:10:02] What can we learn about being better teammates from Marcus?\n\n[01:16:16] It is one hundred years in the future. What do you want to be remembered for?\n\n[01:18:00] The Random RoundSpecial Guest: Donald J. Robertson.","content_html":"Donald is a trainer and former Cognitive-Behavioural Psychotherapist, with a background in academic philosophy, who specializes in eLearning and web development. He's been involved in research on the use of eLearning as a vehicle for delivering training in CBT self-help. He's also interested in the relationship between ancient Stoic philosophy and modern evidence-based approaches to psychological-resilience training.
\n\nFIND DONALD ONLINE
\n\nWebsite: https://donaldrobertson.name/
\n\nLinkedIn: https://www.linkedin.com/in/donaldjrobertson/
\n\nQUOTES
\n\n[00:06:25] "I realize that Stoicism was the type of philosophy that inspired cognitive behavioral therapy. And so everything kind of came together for me. I saw the connection between philosophy and psychotherapy and a particular type of philosophy that ended up spending the rest of my life studying. "
\n\n[00:10:41] "Therapists and researchers developed what's called the cognitive theory of emotion, which says our emotions are shaped fundamentally by underlying beliefs or cognitions. And that happens to be essentially what the Stoics also believed 2300 years ago."
\n\n[00:19:04] "The Stoics want us to reappraise our values so that we realize that our own faculty of judgment is really important and our own character is really important. "
\n\n[00:20:18] "Stoicism and Socratic philosophy are designed to counteract the use of rhetoric, propaganda, emotional tactics that the media use in the same way that the sophists used to use them to manipulate crowds in ancient Athens, in ancient Rome. "
\n\n[00:23:33] "But just asking in casual conversation, what makes somebody wise rather than smart, clever, shrewd by interesting conversation. That's the type of conversation that Socrates thought we should be having."
\n\n[00:24:54] "Maybe wisdom consists in being able to look at what other people think is important and realizing that it isn't. And realizing that things that other people seem to be ignoring are actually much more important. That shift in our values changes our emotions or behavior or quality of life, potentially."
\n\n[00:28:23] "Wisdom is the fundamental virtue of understanding the value of things. And then justice consists in understanding and to practice in terms of how you relate to society and your family and your friends and other individuals."
\n\n[00:29:32] "Courage is a virtue that we require in order to overcome the passion or the emotion of fear and to master that."
\n\n[01:12:56] "Marcus wants us to recognize our own imperfection and bias that's integral to Stoicism, to realize that we are creatures of passion. None of us are perfect. None of the Stoics found those claimed to be gurus of perfect."
\n\nSHOW NOTES
\n\n[00:01:51] Guest Introduction
\n\n[00:05:19] How different is life now than you had imagined it would be when you were younger
\n\n[00:06:13] How psychotherapy lead Donald to Stoicism
\n\n[00:08:09] The relationship between CBT and Stoicism
\n\n[00:14:35] How could CBT and Stoicism help us stay focused in a world where everything is meant to distract us?
\n\n[00:20:44] Human nature hasn’t changed much
\n\n[00:22:31] The Stoic concept of practical wisdom.
\n\n[00:25:56] The cardinal virtues
\n\n[00:31:55] Passions versus virtues
\n\n[00:38:33] People may think that those who practice Stoicism are cold or unemotional, but that's not
\n\n[00:41:34] The circles of Hierocles
\n\n[00:47:15] What can Marcus teach us about improving our relationship with ourselves?
\n\n[00:52:49] Helping those around you flourish
\n\n[00:54:42] Who are some of the teachers and mentors that Marcus talks about and what role did these teachers and mentors play in his life?
\n\n[01:04:57] How Lucius Verus improved Marcus’ character
\n\n[01:10:02] What can we learn about being better teammates from Marcus?
\n\n[01:16:16] It is one hundred years in the future. What do you want to be remembered for?
\n\n[01:18:00] The Random Round
Special Guest: Donald J. Robertson.
","summary":"Donald is a trainer and former Cognitive-Behavioural Psychotherapist, with a background in academic philosophy, who specializes in eLearning and web development. He's been involved in research on the use of eLearning as a vehicle for delivering training in CBT self-help. He's also interested in the relationship between ancient Stoic philosophy and modern evidence-based approaches to psychological-resilience training.\r\n","date_published":"2020-12-07T00:00:00.000-05:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/e6c4a30c-6c03-49d3-ad17-8e8fcf59e37d.mp3","mime_type":"audio/mpeg","size_in_bytes":48001303,"duration_in_seconds":5171}]},{"id":"b906d515-d6d6-4765-ab66-865d9a721127","title":"Data Science Happy Hours 12, 04DEC2020","url":"https://theartistsofdatascience.fireside.fm/oh12","content_text":"The Data Science Happy Hours keep getting happier! \n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh\n\nIf you want to interact with me multiple times a week, join Data Science Dream Job for 70% off: http://dsdj.co/artists70\n\nChat transcript here: https://theartistsofdatascience.fireside.fm/articles/oh12-chat-transcript\n\nWatch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm\n\nWe were voted one of the top ten data science podcasts by FeedSpot - check it out here: https://blog.feedspot.com/data_science_podcasts/\n\nI was on the Rising Laterally Podcast, check it out here: https://www.youtube.com/watch?v=cz6SpTi7tLc\n\nSHOW NOTES\n\n[00:01:27] When do I use Tableau vs Python?\n\n[00:04:01] The data architecture of Tableau\n\n[00:06:31] What is DBT?\nfrees up a lot of administrative burden for days for that stuff.\n\n[00:08:13] “Are you talking about you by yourself or when do you decide to actually buy Tableau versus PowerBI or something else?”\n\n[00:10:56] We start talking about SalesForce’s acquisition of Slack and what they plan on doing in the future.\n\n[00:18:10] We start talking about resumes and share tonne’s of great advice about them\n\n[00:19:30] Ben talks about the time he analyzed 400,000 resumes at HireVue\n\n[00:21:49] What should be on your resume?\n\n[00:23:15] “I would concentrate on people that have a social media presence because that was a way for me as a hiring manager to ascertain do they have this eclectic mix of skills that we call data science these days?”\n\n[00:32:33] Dealing with imbalanced data\n\n[00:36:50] Should I add pictures and graphics to my resume to make it stand out?\n\n[00:41:06] The ever-elusive domain expertise\n\n[00:46:49] Greg stumps us all with a question\n\n[00:51:28] How do you decide if you should use open source third party packages, or should you use your own code?\n\n[01:05:23] Portfolio projects\n\n[01:12:55] Learn to build, learn to sellSpecial Guests: Brandon Quach, PhD, Carlos Mercado, David Tello, Greg Coquillo, Mikiko Bazeley, and Nicole Janeway Bills.","content_html":"The Data Science Happy Hours keep getting happier!
\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh
\n\nIf you want to interact with me multiple times a week, join Data Science Dream Job for 70% off: http://dsdj.co/artists70
\n\nChat transcript here: https://theartistsofdatascience.fireside.fm/articles/oh12-chat-transcript
\n\nWatch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm
\n\nWe were voted one of the top ten data science podcasts by FeedSpot - check it out here: https://blog.feedspot.com/data_science_podcasts/
\n\nI was on the Rising Laterally Podcast, check it out here: https://www.youtube.com/watch?v=cz6SpTi7tLc
\n\nSHOW NOTES
\n\n[00:01:27] When do I use Tableau vs Python?
\n\n[00:04:01] The data architecture of Tableau
\n\n[00:06:31] What is DBT?
\nfrees up a lot of administrative burden for days for that stuff.
[00:08:13] “Are you talking about you by yourself or when do you decide to actually buy Tableau versus PowerBI or something else?”
\n\n[00:10:56] We start talking about SalesForce’s acquisition of Slack and what they plan on doing in the future.
\n\n[00:18:10] We start talking about resumes and share tonne’s of great advice about them
\n\n[00:19:30] Ben talks about the time he analyzed 400,000 resumes at HireVue
\n\n[00:21:49] What should be on your resume?
\n\n[00:23:15] “I would concentrate on people that have a social media presence because that was a way for me as a hiring manager to ascertain do they have this eclectic mix of skills that we call data science these days?”
\n\n[00:32:33] Dealing with imbalanced data
\n\n[00:36:50] Should I add pictures and graphics to my resume to make it stand out?
\n\n[00:41:06] The ever-elusive domain expertise
\n\n[00:46:49] Greg stumps us all with a question
\n\n[00:51:28] How do you decide if you should use open source third party packages, or should you use your own code?
\n\n[01:05:23] Portfolio projects
\n\n[01:12:55] Learn to build, learn to sell
Special Guests: Brandon Quach, PhD, Carlos Mercado, David Tello, Greg Coquillo, Mikiko Bazeley, and Nicole Janeway Bills.
","summary":"","date_published":"2020-12-06T00:00:00.000-05:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/b906d515-d6d6-4765-ab66-865d9a721127.mp3","mime_type":"audio/mpeg","size_in_bytes":43500348,"duration_in_seconds":4791}]},{"id":"f3b63e8a-38df-4524-a33b-51a6a257d8a9","title":"Creativity is a Mindset | Nir Bashan","url":"https://theartistsofdatascience.fireside.fm/nir-bashan","content_text":"Nir Bashan is a world-renowned creativity expert. He has taught thousands of leaders and individuals across the globe how to harness the power of creativity to improve profitability, increase sales and ultimately create more meaning in their work.\n\nFIND NIR ONLINE\n\nWebsite: https://www.nirbashan.com/\n\nLinkedIn: https://www.linkedin.com/in/nirbashan/\n\nTwitter: https://twitter.com/Nir_Bashan\n\nQUOTES\n\n[00:05:42] \"I wanted to be insanely creative, like some of my heroes and and I noticed that they had a technique, all of them a little bit different, but they all had some technique. And it turns out that that technique makes them no different than you or I or anybody listening to this podcast. And it makes them no different. It's all about coming up with that creativity and learning how to harness it and repeating it and pulling it out whenever you need.\"\n\n[00:08:32] \"We live in a society in a day and an age where we can get data on just about anything...But what does it do to have that data? If we're not able to make that information work for us, it's meaningless. \"\n\n[00:12:42] \"We've gotten so comfortable in modernity that we're looking for problems that don't exist\"\n\n[00:17:00] \"A good company and a good person on a career path is armed with creativity to be able to solve problems as they occur in new and different ways. And that really is the most important thing that we can do today to become relevant and to stay relevant and to stay successful.\"\n\n[00:22:49] \"I feel that quantification is just the mere act of assigning a number to something and that is just not good enough anymore. It's not.\"\n\n[00:25:51] \"The numbers aren't really telling us all that we need to know. There's got to be a different way. There's got to be a new way to look at those things. And that way is creativity.\"\n\n[00:39:25] \"Creativity is a component of our DNA and who we are. And in order for us to really be fully evolved human beings, I think we need to balance our analytical drive with the creative drive\"\n\n[00:53:41] \"Egos kill creativity, just like the self-doubt or perhaps even more. We develop egos when we think that we know what will happen and when we think that we have some advantage that other people don't have.\"\n\nSHOW NOTES\n\n[00:01:52] Guest introduction\n\n[00:02:53] Nir’s journey and path to now\n\n[00:05:31] Being creative isn’t just for artists and musicians \n\n[00:07:42] Why is it that just having logic alone is not going to be enough to propel us to the next level?\n\n[00:10:09] How can a non-creative person discover their creativity and put that to work?\n\nHarpreet Sahota:[00:14:29] How has creativity impacted business acumen?\n\n[00:18:14] What are some of the practices of the of yesteryear that we need to get rid of to really be successful in the business of tomorrow?\n\n[00:21:30] Talking about quantification, can you just kind of make that concept concrete for us? What is it that that you mean when you say quantification?\n\n[00:25:24] How you’ve been educated out of the creator’s mindset\n\n[00:27:16] The Trinity of Creativity. \n\n[00:29:15] Why is it crucial that we shut out the analytical part of our mind?\n\n[00:32:56] What creativity means to Harp\n\nNir Bashan: [00:35:06] Why you shouldn’t be afraid to ask “stupid” questions\n\n[00:38:12] The importance of being open and receptive and willing to wander off the path that you've paved for yourself\n\n[00:39:17] Emotional intelligence and creativity \n\n[00:41:04] Positivity and creativity\n\n[00:43:58] What questions can we ask ourselves when we’re coming up to a problem to make we frame these problems in more positive ways?\n\n[00:46:04] What are the personality traits of creativity? How do we cultivate those traits for ourselves?\n\n[00:48:24] How do we catch ourselves when we're having negative internal dialogs so that we can shift ourselves to have more of the cultivated personality traits for creativity?\n\n[00:51:24] Psychological safety and creativity\n\n[00:54:33] How do we fight something (the ego) that comes naturally to us?\n\n[00:55:51] The Four P’s of growth\n\n[00:57:10] Why discipline is a pathway to creativity\n\n[00:58:18] How we can use a creative mindset to be more creative with our time management?\n\n[00:59:36] It's one hundred years in the future: What do you want to be remembered for?\n\n[01:00:50] The Random RoundSpecial Guest: Nir Bashan.","content_html":"Nir Bashan is a world-renowned creativity expert. He has taught thousands of leaders and individuals across the globe how to harness the power of creativity to improve profitability, increase sales and ultimately create more meaning in their work.
\n\nFIND NIR ONLINE
\n\nWebsite: https://www.nirbashan.com/
\n\nLinkedIn: https://www.linkedin.com/in/nirbashan/
\n\nTwitter: https://twitter.com/Nir_Bashan
\n\nQUOTES
\n\n[00:05:42] "I wanted to be insanely creative, like some of my heroes and and I noticed that they had a technique, all of them a little bit different, but they all had some technique. And it turns out that that technique makes them no different than you or I or anybody listening to this podcast. And it makes them no different. It's all about coming up with that creativity and learning how to harness it and repeating it and pulling it out whenever you need."
\n\n[00:08:32] "We live in a society in a day and an age where we can get data on just about anything...But what does it do to have that data? If we're not able to make that information work for us, it's meaningless. "
\n\n[00:12:42] "We've gotten so comfortable in modernity that we're looking for problems that don't exist"
\n\n[00:17:00] "A good company and a good person on a career path is armed with creativity to be able to solve problems as they occur in new and different ways. And that really is the most important thing that we can do today to become relevant and to stay relevant and to stay successful."
\n\n[00:22:49] "I feel that quantification is just the mere act of assigning a number to something and that is just not good enough anymore. It's not."
\n\n[00:25:51] "The numbers aren't really telling us all that we need to know. There's got to be a different way. There's got to be a new way to look at those things. And that way is creativity."
\n\n[00:39:25] "Creativity is a component of our DNA and who we are. And in order for us to really be fully evolved human beings, I think we need to balance our analytical drive with the creative drive"
\n\n[00:53:41] "Egos kill creativity, just like the self-doubt or perhaps even more. We develop egos when we think that we know what will happen and when we think that we have some advantage that other people don't have."
\n\nSHOW NOTES
\n\n[00:01:52] Guest introduction
\n\n[00:02:53] Nir’s journey and path to now
\n\n[00:05:31] Being creative isn’t just for artists and musicians
\n\n[00:07:42] Why is it that just having logic alone is not going to be enough to propel us to the next level?
\n\n[00:10:09] How can a non-creative person discover their creativity and put that to work?
\n\nHarpreet Sahota:[00:14:29] How has creativity impacted business acumen?
\n\n[00:18:14] What are some of the practices of the of yesteryear that we need to get rid of to really be successful in the business of tomorrow?
\n\n[00:21:30] Talking about quantification, can you just kind of make that concept concrete for us? What is it that that you mean when you say quantification?
\n\n[00:25:24] How you’ve been educated out of the creator’s mindset
\n\n[00:27:16] The Trinity of Creativity.
\n\n[00:29:15] Why is it crucial that we shut out the analytical part of our mind?
\n\n[00:32:56] What creativity means to Harp
\n\nNir Bashan: [00:35:06] Why you shouldn’t be afraid to ask “stupid” questions
\n\n[00:38:12] The importance of being open and receptive and willing to wander off the path that you've paved for yourself
\n\n[00:39:17] Emotional intelligence and creativity
\n\n[00:41:04] Positivity and creativity
\n\n[00:43:58] What questions can we ask ourselves when we’re coming up to a problem to make we frame these problems in more positive ways?
\n\n[00:46:04] What are the personality traits of creativity? How do we cultivate those traits for ourselves?
\n\n[00:48:24] How do we catch ourselves when we're having negative internal dialogs so that we can shift ourselves to have more of the cultivated personality traits for creativity?
\n\n[00:51:24] Psychological safety and creativity
\n\n[00:54:33] How do we fight something (the ego) that comes naturally to us?
\n\n[00:55:51] The Four P’s of growth
\n\n[00:57:10] Why discipline is a pathway to creativity
\n\n[00:58:18] How we can use a creative mindset to be more creative with our time management?
\n\n[00:59:36] It's one hundred years in the future: What do you want to be remembered for?
\n\n[01:00:50] The Random Round
Special Guest: Nir Bashan.
","summary":"Nir Bashan is a world-renowned creativity expert. He has taught thousands of leaders and individuals across the globe how to harness the power of creativity to improve profitability, increase sales and ultimately create more meaning in their work.","date_published":"2020-11-30T00:00:00.000-05:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/f3b63e8a-38df-4524-a33b-51a6a257d8a9.mp3","mime_type":"audio/mpeg","size_in_bytes":38342908,"duration_in_seconds":4243}]},{"id":"9fdd1ce5-9b75-4c07-8273-0317b7b3c7c9","title":"Data Science Happy Hours 11, 27NOV2020","url":"https://theartistsofdatascience.fireside.fm/oh11","content_text":"The Data Science Happy Hours keep getting happier! \n\nGuest's that came by: Thom Ives, Dave Langer, Giovanna Malaga, Monica Kay Royal, and so many more!\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh\n\nIf you want to interact with me multiple times a week, join Data Science Dream Job for 70% off: http://dsdj.co/artists70\n\nWatch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm\n\nWe were voted one of the top ten data science podcasts by FeedSpot - check it out here: https://blog.feedspot.com/data_science_podcasts/\n\nCheckout Nicole's article shouting out the show: https://towardsdatascience.com/supercharge-data-science-3da3ccf103-13da3ccf103\n\nI was on the Rising Laterally Podcast, check it out here: https://www.youtube.com/watch?v=cz6SpTi7tLc\n\nSHOW NOTES\n\n[00:04:28] New to data science, trying to get to work experience, and need some tips\n\n[00:08:04] How do I find out what problems data scientists solving in various industries?\n\n[00:11:03] How to stay up on trends in the industry\n\n[00:12:52] How to understand what the business needs\n\n[00:14:52] One of the most interesting hires Joe made was a 19 year old punk rocker\n\n[00:16:07] Have more than just technical virtuosity\n\n[00:17:35] How to go from academia to industry\n\n[00:20:07] What challenges do you see somebody facing coming from an academic background that primarily uses R in that research setting to somebody who's going to be using it in a business type of role?\n\n[00:21:41] How to get hired at Amazon\n\n[00:22:49] Speaking of getting a job at Amazon, LinkedIn top twenty twenty cockier is actually an employee of Amazon. So what does someone need to do to get a job at Amazon?\n\n[00:25:30] Your previous work experience can still benefit you\n\n[00:26:35] Do you need a masters in to be a data scientist?\n\n[00:30:21] Don’t lead a life of meeting spec\n\n[00:33:58] Books to get business intuition\n\n[00:35:08] I just completed a masters in math, do I need to do a bootcamp?\n\n[00:38:30] Some great recommendations for SQL\neach project.\n\n[00:41:08] I'd like to get my management onboard into automating data integration and consistent dashboards. Do you have any specific suggestions for what goes into the elevator pitch to management over?\n\n[00:44:56] How to get ETL experience when you already know SQL\n\n[00:47:39] A question on text analytics\n\n[00:51:35] How much big data knowledge do I need?\n\n[00:54:39] What do you think is causing the shift back to the data warehouse?\n\n[00:57:08] Which book should I read to get up to speed on production ready code?\n\n[00:58:57] How do I choose the right data and how do I solve a problem?\n\n[01:01:08] Greg asks an awesome question about costs and benefits of automation\n\n[01:07:39] What makes a good data scientist?Special Guest: Greg Coquillo.","content_html":"The Data Science Happy Hours keep getting happier!
\n\nGuest's that came by: Thom Ives, Dave Langer, Giovanna Malaga, Monica Kay Royal, and so many more!
\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh
\n\nIf you want to interact with me multiple times a week, join Data Science Dream Job for 70% off: http://dsdj.co/artists70
\n\nWatch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm
\n\nWe were voted one of the top ten data science podcasts by FeedSpot - check it out here: https://blog.feedspot.com/data_science_podcasts/
\n\nCheckout Nicole's article shouting out the show: https://towardsdatascience.com/supercharge-data-science-3da3ccf103-13da3ccf103
\n\nI was on the Rising Laterally Podcast, check it out here: https://www.youtube.com/watch?v=cz6SpTi7tLc
\n\nSHOW NOTES
\n\n[00:04:28] New to data science, trying to get to work experience, and need some tips
\n\n[00:08:04] How do I find out what problems data scientists solving in various industries?
\n\n[00:11:03] How to stay up on trends in the industry
\n\n[00:12:52] How to understand what the business needs
\n\n[00:14:52] One of the most interesting hires Joe made was a 19 year old punk rocker
\n\n[00:16:07] Have more than just technical virtuosity
\n\n[00:17:35] How to go from academia to industry
\n\n[00:20:07] What challenges do you see somebody facing coming from an academic background that primarily uses R in that research setting to somebody who's going to be using it in a business type of role?
\n\n[00:21:41] How to get hired at Amazon
\n\n[00:22:49] Speaking of getting a job at Amazon, LinkedIn top twenty twenty cockier is actually an employee of Amazon. So what does someone need to do to get a job at Amazon?
\n\n[00:25:30] Your previous work experience can still benefit you
\n\n[00:26:35] Do you need a masters in to be a data scientist?
\n\n[00:30:21] Don’t lead a life of meeting spec
\n\n[00:33:58] Books to get business intuition
\n\n[00:35:08] I just completed a masters in math, do I need to do a bootcamp?
\n\n[00:38:30] Some great recommendations for SQL
\neach project.
[00:41:08] I'd like to get my management onboard into automating data integration and consistent dashboards. Do you have any specific suggestions for what goes into the elevator pitch to management over?
\n\n[00:44:56] How to get ETL experience when you already know SQL
\n\n[00:47:39] A question on text analytics
\n\n[00:51:35] How much big data knowledge do I need?
\n\n[00:54:39] What do you think is causing the shift back to the data warehouse?
\n\n[00:57:08] Which book should I read to get up to speed on production ready code?
\n\n[00:58:57] How do I choose the right data and how do I solve a problem?
\n\n[01:01:08] Greg asks an awesome question about costs and benefits of automation
\n\n[01:07:39] What makes a good data scientist?
Special Guest: Greg Coquillo.
","summary":"","date_published":"2020-11-29T00:00:00.000-05:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/9fdd1ce5-9b75-4c07-8273-0317b7b3c7c9.mp3","mime_type":"audio/mpeg","size_in_bytes":38785435,"duration_in_seconds":4330}]},{"id":"c732734d-228c-4fbe-931d-5486bfe1468e","title":"Don't Trust Yourself With Money | Jeff Kreisler","url":"https://theartistsofdatascience.fireside.fm/jeff-kreisler","content_text":"Jeff Kreisler uses behavioral science, real life, and humor to understand, explain, and change the world. He’s pretty much your friendly, typical Princeton educated lawyer turned award-winning comedian, best-selling author, and champion for behavioral economics .\n\nWHAT YOU'LL LEARN\nWhat money is, why we won't make good money decisions, the common cognitive biases we have related to money, how to not let the retailers dupe you during this holiday shopping season, and more! \n\nQUOTES\n\n[11:31] \"Traditional economics says everything's cost benefit analysis. Reality is that that's not how it works. We are busy people. We have a lot of stress and we don't always make the rational choice.\"\n\n[16:47] \"The there's no clear right or wrong choice with money that we all always know. There's always some uncertainty. And when uncertainty is in any decision, that gap gets filled by the emotional needs that we have. The need to feel like we're making the right choice. The need to feel like we've done the right thing. The need to feel good. And that's when we can be prone to make irrational decisions, because we go by our feelings and emotions. \"\n\n[18:08] \"I feel like marrying the data science with the people science is going to be like an incredible combination. To not just know where they're going and what buttons to push, but why why are people doing this?\"\n\n[20:30] \"When we pay for something, it stimulates the same region of our brain as physical pain. And that pain should serve a purpose. It should make us stop and think about what we're doing.\"\n\n[34:01] \"You can't pay your rent with the money you save shopping.\"\n\nFIND JEFF ONLINE\n\nWebsite: http://www.jeffkreisler.com/\n\nLinkedIn: https://www.linkedin.com/in/jeffkreisler/\n\nSHOW NOTES\n\n[00:01:32] Introduction for our guest\n\n[00:02:42] We talk about Jeff’s journey \n\n[00:08:06] How Jeff teamed up with Dan Ariely \n\n[00:13:54] The concept of money\n\n[00:18:46] Mental shortcuts and money\n\n[00:22:24] What would you do with $30,000\n\n[00:25:24] Relativity, money, and why we suck at comparing things\n\n[00:29:38] System 1 and System 2\n\n[00:31:33] Don’t fall for the sale price!\n\n[00:33:22] How retailers will trick you with discounts\n\n[00:35:52] The anchoring effect\n\n[00:41:16] Mental accounting\n\n[00:45:06] Extreme examples of mental accounting\n\n[00:46:38] Do we have the same cognitive biases for other people's money as we do for our own?\n\n[00:48:39] Herding and self-herding\n\n[00:52:28] Jeff shares his top three favorite tips for fighting our flawed financial thinking \n\n[00:56:37] Some other cognitive biases to watch out for this holiday shopping season\n\n[00:58:27] It's one hundred years in the future. What do you want to be remembered for?\n\n[00:59:23] The random roundSpecial Guest: Jeff Kreisler.","content_html":"Jeff Kreisler uses behavioral science, real life, and humor to understand, explain, and change the world. He’s pretty much your friendly, typical Princeton educated lawyer turned award-winning comedian, best-selling author, and champion for behavioral economics .
\n\nWHAT YOU'LL LEARN
\nWhat money is, why we won't make good money decisions, the common cognitive biases we have related to money, how to not let the retailers dupe you during this holiday shopping season, and more!
QUOTES
\n\n[11:31] "Traditional economics says everything's cost benefit analysis. Reality is that that's not how it works. We are busy people. We have a lot of stress and we don't always make the rational choice."
\n\n[16:47] "The there's no clear right or wrong choice with money that we all always know. There's always some uncertainty. And when uncertainty is in any decision, that gap gets filled by the emotional needs that we have. The need to feel like we're making the right choice. The need to feel like we've done the right thing. The need to feel good. And that's when we can be prone to make irrational decisions, because we go by our feelings and emotions. "
\n\n[18:08] "I feel like marrying the data science with the people science is going to be like an incredible combination. To not just know where they're going and what buttons to push, but why why are people doing this?"
\n\n[20:30] "When we pay for something, it stimulates the same region of our brain as physical pain. And that pain should serve a purpose. It should make us stop and think about what we're doing."
\n\n[34:01] "You can't pay your rent with the money you save shopping."
\n\nFIND JEFF ONLINE
\n\nWebsite: http://www.jeffkreisler.com/
\n\nLinkedIn: https://www.linkedin.com/in/jeffkreisler/
\n\nSHOW NOTES
\n\n[00:01:32] Introduction for our guest
\n\n[00:02:42] We talk about Jeff’s journey
\n\n[00:08:06] How Jeff teamed up with Dan Ariely
\n\n[00:13:54] The concept of money
\n\n[00:18:46] Mental shortcuts and money
\n\n[00:22:24] What would you do with $30,000
\n\n[00:25:24] Relativity, money, and why we suck at comparing things
\n\n[00:29:38] System 1 and System 2
\n\n[00:31:33] Don’t fall for the sale price!
\n\n[00:33:22] How retailers will trick you with discounts
\n\n[00:35:52] The anchoring effect
\n\n[00:41:16] Mental accounting
\n\n[00:45:06] Extreme examples of mental accounting
\n\n[00:46:38] Do we have the same cognitive biases for other people's money as we do for our own?
\n\n[00:48:39] Herding and self-herding
\n\n[00:52:28] Jeff shares his top three favorite tips for fighting our flawed financial thinking
\n\n[00:56:37] Some other cognitive biases to watch out for this holiday shopping season
\n\n[00:58:27] It's one hundred years in the future. What do you want to be remembered for?
\n\n[00:59:23] The random round
Special Guest: Jeff Kreisler.
","summary":"","date_published":"2020-11-23T00:00:00.000-05:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/c732734d-228c-4fbe-931d-5486bfe1468e.mp3","mime_type":"audio/mpeg","size_in_bytes":34735656,"duration_in_seconds":3912}]},{"id":"a2eec8c4-3905-4163-9f2f-da034eb88f8d","title":"Data Science Happy Hours 10, 20NOV2020","url":"https://theartistsofdatascience.fireside.fm/oh10","content_text":"The tenth episode of the Data Science Happy Hours were popping off! Guest's that came by: Thom Ives, Dave Langer, Ben Taylor, Sarah Nooravi, Giovanna Malaga, Monica Kay Royal, Kate Strachnyi, Carlos Mercado, and so many more!\n\nChat transcript: http://theartistsofdatascience.fireside.fm/articles/oh10-chat-transcript\n\nHuge thanks to our sponsor for this episode - Cloud Academy! Go to https://cloudacademy.com/ and use the coupon code ARTIST for 50% off the monthly subscription fee for life!\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh\n\nIf you want to interact with me multiple times a week, join Data Science Dream Job for 70% off: http://dsdj.co/artists70\n\nWatch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm\n\nWe were voted one of the top ten data science podcasts by FeedSpot - check it out here: https://blog.feedspot.com/data_science_podcasts/\n\nCheckout Nicole's article shouting out the show: https://towardsdatascience.com/supercharge-data-science-3da3ccf103-13da3ccf103\n\nI was on the Rising Laterally Podcast, check it out here: https://www.youtube.com/watch?v=cz6SpTi7tLc\n\nSHOW NOTES\n\n[00:02:09] How would you define a full stack scientist? \n\n[00:04:18] Intrapreneurship: being the first data scientist in an organization\n\n[00:06:04] What not to do as a data scientist\n\n[00:07:19] Mistake in cross-validation\n\n[00:08:54] Building minimal viable products\n\n[00:10:19] “We're not earthlings, all of us on the screen. We are barely earthlings.”\n\n[00:12:31] One of the problems with machine learning\n\n[00:14:04] Screwing up things, the hard way.\n\n[00:15:43] What do you think about people starting as data analysts and then transitioning into a data scientist, or is there a better way of doing that?\n\n[00:19:01] “I actually have something against titles themselves. In some cases, they're not what they seem.”\n\n[00:21:04] “You have to be skeptical of job descriptions. You have to be skeptical of titles.”\n\n[00:24:11] “How you present your skills is an important way of how they are going to consider you for their position.”\n\n[00:28:06] Do mock interviews\n\n[00:28:45] Take the beginner stuff off your resume\n\n[00:30:56] WTF is a unit test and when do I need them?\n\n[00:33:24] The rule of thumb for knowing when you need to do unit tests\n\n[00:33:35] “But it's not technical debt if the debt doesn't come due”\n\n[00:35:00] Is there going to be a shortage of business domain experts in data science?\n\n[00:41:27] “At the end of the day, Data analyst, data scientist, the main point that you're trying to do is to solve a problem, to help the business be better, make more money, get more customers.”\n\n[00:42:43] Can you give some input on how you manage your time? \n\n[00:44:06] How Sarah Nooravi manages her time\n\n[00:45:41] How Giovanna manages her time\n\n[00:47:35] How Harpreet manages his time\n\n[00:48:36] How Ben Taylor manages his time\n\n[00:50:07] Carlos says the most un-Carlos thing ever: “Listen to your body”\n\n[00:51:01] How Dave Langer manages his time\n\n[00:52:12] A summary of how to manage time\n\n[00:53:41] Network analytics?\n\n[00:55:15] Learn to build, learn to sell…if you can do both, you will be unstoppable\n\n[00:58:11] Test driven development and data engineering\n\n[01:02:44] What are some things that data scientist are not good at, but they probably should start getting good at?\n\n[01:07:33] The importance of curiositySpecial Guest: Carlos Mercado.","content_html":"The tenth episode of the Data Science Happy Hours were popping off! Guest's that came by: Thom Ives, Dave Langer, Ben Taylor, Sarah Nooravi, Giovanna Malaga, Monica Kay Royal, Kate Strachnyi, Carlos Mercado, and so many more!
\n\nChat transcript: http://theartistsofdatascience.fireside.fm/articles/oh10-chat-transcript
\n\nHuge thanks to our sponsor for this episode - Cloud Academy! Go to https://cloudacademy.com/ and use the coupon code ARTIST for 50% off the monthly subscription fee for life!
\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh
\n\nIf you want to interact with me multiple times a week, join Data Science Dream Job for 70% off: http://dsdj.co/artists70
\n\nWatch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm
\n\nWe were voted one of the top ten data science podcasts by FeedSpot - check it out here: https://blog.feedspot.com/data_science_podcasts/
\n\nCheckout Nicole's article shouting out the show: https://towardsdatascience.com/supercharge-data-science-3da3ccf103-13da3ccf103
\n\nI was on the Rising Laterally Podcast, check it out here: https://www.youtube.com/watch?v=cz6SpTi7tLc
\n\nSHOW NOTES
\n\n[00:02:09] How would you define a full stack scientist?
\n\n[00:04:18] Intrapreneurship: being the first data scientist in an organization
\n\n[00:06:04] What not to do as a data scientist
\n\n[00:07:19] Mistake in cross-validation
\n\n[00:08:54] Building minimal viable products
\n\n[00:10:19] “We're not earthlings, all of us on the screen. We are barely earthlings.”
\n\n[00:12:31] One of the problems with machine learning
\n\n[00:14:04] Screwing up things, the hard way.
\n\n[00:15:43] What do you think about people starting as data analysts and then transitioning into a data scientist, or is there a better way of doing that?
\n\n[00:19:01] “I actually have something against titles themselves. In some cases, they're not what they seem.”
\n\n[00:21:04] “You have to be skeptical of job descriptions. You have to be skeptical of titles.”
\n\n[00:24:11] “How you present your skills is an important way of how they are going to consider you for their position.”
\n\n[00:28:06] Do mock interviews
\n\n[00:28:45] Take the beginner stuff off your resume
\n\n[00:30:56] WTF is a unit test and when do I need them?
\n\n[00:33:24] The rule of thumb for knowing when you need to do unit tests
\n\n[00:33:35] “But it's not technical debt if the debt doesn't come due”
\n\n[00:35:00] Is there going to be a shortage of business domain experts in data science?
\n\n[00:41:27] “At the end of the day, Data analyst, data scientist, the main point that you're trying to do is to solve a problem, to help the business be better, make more money, get more customers.”
\n\n[00:42:43] Can you give some input on how you manage your time?
\n\n[00:44:06] How Sarah Nooravi manages her time
\n\n[00:45:41] How Giovanna manages her time
\n\n[00:47:35] How Harpreet manages his time
\n\n[00:48:36] How Ben Taylor manages his time
\n\n[00:50:07] Carlos says the most un-Carlos thing ever: “Listen to your body”
\n\n[00:51:01] How Dave Langer manages his time
\n\n[00:52:12] A summary of how to manage time
\n\n[00:53:41] Network analytics?
\n\n[00:55:15] Learn to build, learn to sell…if you can do both, you will be unstoppable
\n\n[00:58:11] Test driven development and data engineering
\n\n[01:02:44] What are some things that data scientist are not good at, but they probably should start getting good at?
\n\n[01:07:33] The importance of curiosity
Special Guest: Carlos Mercado.
","summary":"","date_published":"2020-11-22T00:00:00.000-05:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/a2eec8c4-3905-4163-9f2f-da034eb88f8d.mp3","mime_type":"audio/mpeg","size_in_bytes":39236970,"duration_in_seconds":4330}]},{"id":"126b7b5a-71cf-4cb1-b489-3b60653f081a","title":"Become Invaluable | Maya Grossman","url":"https://theartistsofdatascience.fireside.fm/maya-grossman","content_text":"Maya is a marketing executive, blogger, speaker, podcaster, and a career coach. She’s worked for companies like Microsoft and Google, racking up multiple promotions and raises, and strategically leveling up from an individual contributor to vice president. \n\nShe’s blended her experience climbing the corporate ladder, motivational leadership style, authentic voice, and love for helping others into a book designed to give you the tools you need to succeed!\n\nFIND MAYA ONLINE\n\nWebsite: https://www.mayagrossman.com/\n\nLinkedIn: https://www.linkedin.com/in/mayagrossman/\n\nTwitter: https://twitter.com/mayagrosmn\n\nWHAT YOU'LL LEARN\n\n[00:14:38] The Owner’s Mindset\n\n[00:41:48] The influence formula\n\n[00:43:36] How Maya stopped a plane from taking off\n\n[00:45:08] How to tell stories with data\n\n[00:47:37] Talent stacking \n\n[01:00:55] The top five skills you need to survive the next decade\n\n[01:01:47] How to ace an interview\n\nQUOTES\n\n[11:37] \"Invaluable is all about mindset.\"\n\n[12:39] \"We've had research for more than one hundred years that shows us that career success is best predicted by soft skills. Seventy five percent of your success can be predicted by our soft skills and only twenty five percent by your hard skills or your profession. And even though we know this, there's so little information about how to actually acquire soft skills.\"\n\n[15:37] \"You need to look at the bigger picture. You need to think like an owner, which means you want to make sure that you get the most out of everything that you do, and you spend the least\"\n\n[19:32] \"You need to understand that no matter how small your role is, you are part of a bigger machine. You're part of the company and your job is to make that company successful, however you can. And the minute you realize that, you're going to start seeing opportunities to do more and to help more and in most cases, that's going to give you more experience. You can experiment and see what you like, what you don't like. And it's also a great way to set yourself up for a promotion or for your next role.\"\n\n[24:12] \"Because if you don't learn and you don't grow, you're basically moving backward. And it's kind of weird. Most people don't read a book after they graduate from college. As we grow up, we were being taught that you need to learn. So you go to school if you're like you go to college. But the minute that ends, there's no motivation to continue learning.\"\n\n[28:38] \"It's not just someone who gets things done. It's someone who has the ability to identify a problem. They care enough to ask questions, to dig around, to actually take action when they come across something that doesn't work.\"\n\n[33:17] \"If you're a lateral thinker, you're able to take information from different areas, different disciplines, different things you've done in your life, and apply them to come up with a very creative idea.\"\n\n[01:03:08] \" I have 15 years of experience and my resume is not two pages long. So if you're early in your career, you do not need twenty thousand bullet points. Less is actually more.\"\n\nSHOW NOTES\n\n[00:01:35] Introduction for our guest\n\n[00:02:44] Maya talk to us about her upbringing\n\n[00:04:29] What Maya loves about marketing\n\n[00:05:36] How all of Maya’s experience culminated in a book\n\n[00:06:58] What was the process like for you writing the book? \n\n[00:08:21] What is invaluable all about? What does that mean?\n\n[00:10:08] Who is that person that you're writing this book for?\n\n[00:11:21] Is it ever too late to become invaluable?\n\n[00:12:33] What is the deal with soft skills and why are they so important?\n\n[00:14:38] The Owner’s Mindset\n\n[00:16:49] How do we start cultivating this owner's mindset?\n\n[00:20:03] What can we do to help develop a sense of purpose for ourselves and our work?\n\n[00:22:56] The craftsman mindset\n\n[00:23:39] Why you need to be a lifelong learner\n\n[00:24:48] How can we turn learning into a habit?\n\n[00:27:32] What are some newsletters that you are currently subscribed to?\n\n[00:28:10] What is a fixer?\n\n[00:31:33] Helping budding fixer’s climb the ranks\n\n[00:32:34] The importance of lateral thinking\n\n[00:35:18] Never let your fear decide your fate\n\n[00:37:19] Cultivating psychological safety\n\n[00:41:48] The influence formula\n\n[00:43:36] How Maya stopped a plane from taking off\n\n[00:45:08] How to tell stories with data\n\n[00:47:37] Talent stacking \n\n[00:53:20] The five pieces of career advice that Maya wishes somebody would have given her in her twenties. \n\n[00:55:41] Tips for networking\n\n[01:00:55] The top five skills you need to survive the next decade\n\n[01:01:47] How to ace an interview\n\n[01:05:51] Negotiations\n\n[01:08:39] Advice for the women in our audience \n\n[01:10:23] Its one hundred years in the future. What is it that you want to be remembered for?\n\n[01:10:49] The Random RoundSpecial Guest: Maya Grossman.","content_html":"Maya is a marketing executive, blogger, speaker, podcaster, and a career coach. She’s worked for companies like Microsoft and Google, racking up multiple promotions and raises, and strategically leveling up from an individual contributor to vice president.
\n\nShe’s blended her experience climbing the corporate ladder, motivational leadership style, authentic voice, and love for helping others into a book designed to give you the tools you need to succeed!
\n\nFIND MAYA ONLINE
\n\nWebsite: https://www.mayagrossman.com/
\n\nLinkedIn: https://www.linkedin.com/in/mayagrossman/
\n\nTwitter: https://twitter.com/mayagrosmn
\n\nWHAT YOU'LL LEARN
\n\n[00:14:38] The Owner’s Mindset
\n\n[00:41:48] The influence formula
\n\n[00:43:36] How Maya stopped a plane from taking off
\n\n[00:45:08] How to tell stories with data
\n\n[00:47:37] Talent stacking
\n\n[01:00:55] The top five skills you need to survive the next decade
\n\n[01:01:47] How to ace an interview
\n\nQUOTES
\n\n[11:37] "Invaluable is all about mindset."
\n\n[12:39] "We've had research for more than one hundred years that shows us that career success is best predicted by soft skills. Seventy five percent of your success can be predicted by our soft skills and only twenty five percent by your hard skills or your profession. And even though we know this, there's so little information about how to actually acquire soft skills."
\n\n[15:37] "You need to look at the bigger picture. You need to think like an owner, which means you want to make sure that you get the most out of everything that you do, and you spend the least"
\n\n[19:32] "You need to understand that no matter how small your role is, you are part of a bigger machine. You're part of the company and your job is to make that company successful, however you can. And the minute you realize that, you're going to start seeing opportunities to do more and to help more and in most cases, that's going to give you more experience. You can experiment and see what you like, what you don't like. And it's also a great way to set yourself up for a promotion or for your next role."
\n\n[24:12] "Because if you don't learn and you don't grow, you're basically moving backward. And it's kind of weird. Most people don't read a book after they graduate from college. As we grow up, we were being taught that you need to learn. So you go to school if you're like you go to college. But the minute that ends, there's no motivation to continue learning."
\n\n[28:38] "It's not just someone who gets things done. It's someone who has the ability to identify a problem. They care enough to ask questions, to dig around, to actually take action when they come across something that doesn't work."
\n\n[33:17] "If you're a lateral thinker, you're able to take information from different areas, different disciplines, different things you've done in your life, and apply them to come up with a very creative idea."
\n\n[01:03:08] " I have 15 years of experience and my resume is not two pages long. So if you're early in your career, you do not need twenty thousand bullet points. Less is actually more."
\n\nSHOW NOTES
\n\n[00:01:35] Introduction for our guest
\n\n[00:02:44] Maya talk to us about her upbringing
\n\n[00:04:29] What Maya loves about marketing
\n\n[00:05:36] How all of Maya’s experience culminated in a book
\n\n[00:06:58] What was the process like for you writing the book?
\n\n[00:08:21] What is invaluable all about? What does that mean?
\n\n[00:10:08] Who is that person that you're writing this book for?
\n\n[00:11:21] Is it ever too late to become invaluable?
\n\n[00:12:33] What is the deal with soft skills and why are they so important?
\n\n[00:14:38] The Owner’s Mindset
\n\n[00:16:49] How do we start cultivating this owner's mindset?
\n\n[00:20:03] What can we do to help develop a sense of purpose for ourselves and our work?
\n\n[00:22:56] The craftsman mindset
\n\n[00:23:39] Why you need to be a lifelong learner
\n\n[00:24:48] How can we turn learning into a habit?
\n\n[00:27:32] What are some newsletters that you are currently subscribed to?
\n\n[00:28:10] What is a fixer?
\n\n[00:31:33] Helping budding fixer’s climb the ranks
\n\n[00:32:34] The importance of lateral thinking
\n\n[00:35:18] Never let your fear decide your fate
\n\n[00:37:19] Cultivating psychological safety
\n\n[00:41:48] The influence formula
\n\n[00:43:36] How Maya stopped a plane from taking off
\n\n[00:45:08] How to tell stories with data
\n\n[00:47:37] Talent stacking
\n\n[00:53:20] The five pieces of career advice that Maya wishes somebody would have given her in her twenties.
\n\n[00:55:41] Tips for networking
\n\n[01:00:55] The top five skills you need to survive the next decade
\n\n[01:01:47] How to ace an interview
\n\n[01:05:51] Negotiations
\n\n[01:08:39] Advice for the women in our audience
\n\n[01:10:23] Its one hundred years in the future. What is it that you want to be remembered for?
\n\n[01:10:49] The Random Round
Special Guest: Maya Grossman.
","summary":"","date_published":"2020-11-16T00:00:00.000-05:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/126b7b5a-71cf-4cb1-b489-3b60653f081a.mp3","mime_type":"audio/mpeg","size_in_bytes":89079709,"duration_in_seconds":4453}]},{"id":"fb446b6f-a4dc-4f8a-b760-5db42f3871a0","title":"Data Science Happy Hours 9, 13NOV2020","url":"https://theartistsofdatascience.fireside.fm/oh9","content_text":"Thom Ives, David Knickerbocker, and Carlos Mercado join in on the fun!\n\nHuge thanks to our sponsor for this episode - Cloud Academy! Go to https://cloudacademy.com/ and use the coupon code ARTIST for 50% off the monthly subscription fee for life!\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh\n\nIf you want to interact with me multiple times a week, join Data Science Dream Job for 70% off: http://dsdj.co/artists70\n\nWatch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm\n\nWe were voted one of the top ten data science podcasts by FeedSpot - check it out here: https://blog.feedspot.com/data_science_podcasts/\n\nCheckout Nicole's article shouting out the show: https://towardsdatascience.com/supercharge-data-science-3da3ccf103-13da3ccf103\n\n[00:04:05] “I haven’t gotten a job in months, what should I learn next to get a job?”\n\n[00:05:39] Follow your own obsession\n\n[00:09:44] What to do when you feel like your brain is getting fried from everything you’re learning\n\n[00:11:29] Carlos joins in – “Start from the bottom”\n\n[00:12:53] Don’t worry about your title\n\n[00:15:18] How to get data science experience without having a data science job\n\n[00:18:11] David talks about the hype cycle he’s experienced through his career\n\n[00:19:40] How to balance your learning\n\n[00:22:10] Why you should speak your mind\n\n[00:25:01] Thom says a four letter word\n\n[00:28:50] Eric comes in with a question that has us stumped for a little bit\n\n[00:29:41] Balancing datasets\n\n[00:31:32] Rare events\n\n[00:35:13] Hyperparameter what?\n\n[00:36:15] The issue with random seeds\n\n[00:40:31] Thom compliments Eric on sharing his knowledge\n\n[00:41:55] Where’s everyone joining in from?\n\n[00:46:07] Diversity and representation is important and welcome\n\n[00:47:33] Correlation and causation\n\n[00:53:07] The importance of critical thinking\n\n[00:56:39] David gets into network theory\n\n[00:58:11] Functional vs OOP design principles in production\n\n[01:00:16] “I want production to be beautiful.” – David Knickerbocker \n\n[01:02:04] Network analysisSpecial Guest: Carlos Mercado.","content_html":"Thom Ives, David Knickerbocker, and Carlos Mercado join in on the fun!
\n\nHuge thanks to our sponsor for this episode - Cloud Academy! Go to https://cloudacademy.com/ and use the coupon code ARTIST for 50% off the monthly subscription fee for life!
\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh
\n\nIf you want to interact with me multiple times a week, join Data Science Dream Job for 70% off: http://dsdj.co/artists70
\n\nWatch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm
\n\nWe were voted one of the top ten data science podcasts by FeedSpot - check it out here: https://blog.feedspot.com/data_science_podcasts/
\n\nCheckout Nicole's article shouting out the show: https://towardsdatascience.com/supercharge-data-science-3da3ccf103-13da3ccf103
\n\n[00:04:05] “I haven’t gotten a job in months, what should I learn next to get a job?”
\n\n[00:05:39] Follow your own obsession
\n\n[00:09:44] What to do when you feel like your brain is getting fried from everything you’re learning
\n\n[00:11:29] Carlos joins in – “Start from the bottom”
\n\n[00:12:53] Don’t worry about your title
\n\n[00:15:18] How to get data science experience without having a data science job
\n\n[00:18:11] David talks about the hype cycle he’s experienced through his career
\n\n[00:19:40] How to balance your learning
\n\n[00:22:10] Why you should speak your mind
\n\n[00:25:01] Thom says a four letter word
\n\n[00:28:50] Eric comes in with a question that has us stumped for a little bit
\n\n[00:29:41] Balancing datasets
\n\n[00:31:32] Rare events
\n\n[00:35:13] Hyperparameter what?
\n\n[00:36:15] The issue with random seeds
\n\n[00:40:31] Thom compliments Eric on sharing his knowledge
\n\n[00:41:55] Where’s everyone joining in from?
\n\n[00:46:07] Diversity and representation is important and welcome
\n\n[00:47:33] Correlation and causation
\n\n[00:53:07] The importance of critical thinking
\n\n[00:56:39] David gets into network theory
\n\n[00:58:11] Functional vs OOP design principles in production
\n\n[01:00:16] “I want production to be beautiful.” – David Knickerbocker
\n\n[01:02:04] Network analysis
Special Guest: Carlos Mercado.
","summary":"","date_published":"2020-11-15T00:00:00.000-05:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/fb446b6f-a4dc-4f8a-b760-5db42f3871a0.mp3","mime_type":"audio/mpeg","size_in_bytes":35085506,"duration_in_seconds":3945}]},{"id":"52bf488f-f25c-4c15-9fb8-69efb169d3c6","title":"The Wild Ride into Data Science | Sadie St. Lawrence","url":"https://theartistsofdatascience.fireside.fm/sadie-st-lawrence","content_text":"Sadie is helping pave the way for women in data science as being the first female instructor for Data Science on the Coursera platform, and as founder and CEO of Women in Data - a non-profit organization focused on increasing diversity in data careers.\n\nFIND SADIE ONLINE\n\nWebsite: http://sadiestlawrence.com/\n\nLinkedIn: https://www.linkedin.com/in/sadiestlawrence/\n\nInstagram: https://www.instagram.com/sadiestlawrence\n\nTwitter: https://twitter.com/sadiestlawrence\n\nQUOTES\n\n[18:59] \"If you're looking to get into Data science and transitioning from something else, what made you successful in that area and what principles did you apply?\"\n\n[20:41] \"I knew that if I was going to do Data science, I needed a community and a tribe of people to be a part of.\"\n\n[22:54] \"Data science isn't just about building models and doing that type of work. You're working, usually for, a business...\"\n\n[34:18] \"If you want to own your career, really what you need to do is look at and see what things can I control in my life. And when you start hundred percent focusing on those the world around you is going to change.\"\n\nSHOW NOTES\n\n[00:00:40] Guest introduction\n\n[00:03:19] Sadie’s path to data science\n\n[00:05:52] Data collection in laboratory settings\n\n[00:07:32] The data science hype\n\n[00:08:24] Is data science going away any time soon?\n\n[00:09:39] The positive impact data science will have \n\n[00:11:34] Going from good to great as a data scientist\n\n[00:14:02] SQL skills you need for data science\n\n[00:17:23] An action plan for breaking into data science\n\n[00:22:02] Soft skills to elevate your career\n\n[00:23:19] Use verbal judo to be more persuasive \n\n[00:24:46] How to communicate with executives\n\n[00:25:55] The data science mindset\n\n[00:27:35] Making the most of networking events\n\n[00:29:24] Communication and teamwork\n\n[00:30:49] Is data science an art or science?\n\n[00:32:55] How to own your career\n\n[00:36:38] Steps for combating imposter syndrome?\n\n[00:38:17] Tips for women in data science\n\n[00:43:45] What can the Data community do to help foster the inclusion of women in the field?\n\n[00:45:31] What's the one thing you want people to learn from your story?\n\n[00:46:43] Lightning round Special Guest: Sadie St. Lawrence.","content_html":"Sadie is helping pave the way for women in data science as being the first female instructor for Data Science on the Coursera platform, and as founder and CEO of Women in Data - a non-profit organization focused on increasing diversity in data careers.
\n\nFIND SADIE ONLINE
\n\nWebsite: http://sadiestlawrence.com/
\n\nLinkedIn: https://www.linkedin.com/in/sadiestlawrence/
\n\nInstagram: https://www.instagram.com/sadiestlawrence
\n\nTwitter: https://twitter.com/sadiestlawrence
\n\nQUOTES
\n\n[18:59] "If you're looking to get into Data science and transitioning from something else, what made you successful in that area and what principles did you apply?"
\n\n[20:41] "I knew that if I was going to do Data science, I needed a community and a tribe of people to be a part of."
\n\n[22:54] "Data science isn't just about building models and doing that type of work. You're working, usually for, a business..."
\n\n[34:18] "If you want to own your career, really what you need to do is look at and see what things can I control in my life. And when you start hundred percent focusing on those the world around you is going to change."
\n\nSHOW NOTES
\n\n[00:00:40] Guest introduction
\n\n[00:03:19] Sadie’s path to data science
\n\n[00:05:52] Data collection in laboratory settings
\n\n[00:07:32] The data science hype
\n\n[00:08:24] Is data science going away any time soon?
\n\n[00:09:39] The positive impact data science will have
\n\n[00:11:34] Going from good to great as a data scientist
\n\n[00:14:02] SQL skills you need for data science
\n\n[00:17:23] An action plan for breaking into data science
\n\n[00:22:02] Soft skills to elevate your career
\n\n[00:23:19] Use verbal judo to be more persuasive
\n\n[00:24:46] How to communicate with executives
\n\n[00:25:55] The data science mindset
\n\n[00:27:35] Making the most of networking events
\n\n[00:29:24] Communication and teamwork
\n\n[00:30:49] Is data science an art or science?
\n\n[00:32:55] How to own your career
\n\n[00:36:38] Steps for combating imposter syndrome?
\n\n[00:38:17] Tips for women in data science
\n\n[00:43:45] What can the Data community do to help foster the inclusion of women in the field?
\n\n[00:45:31] What's the one thing you want people to learn from your story?
\n\n[00:46:43] Lightning round
Special Guest: Sadie St. Lawrence.
","summary":"","date_published":"2020-11-09T00:00:00.000-05:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/52bf488f-f25c-4c15-9fb8-69efb169d3c6.mp3","mime_type":"audio/mpeg","size_in_bytes":75524876,"duration_in_seconds":3146}]},{"id":"84c24092-e333-4af4-8a7b-2ad9069dc0e1","title":"Data Science Happy Hours 8, 06NOV2020","url":"https://theartistsofdatascience.fireside.fm/oh8","content_text":"We have a surprise visit from friend of the podcast, Srivatsan Srinivasan!\n\nLot's of awesome topics covered in this office hour session!\n\nHuge thanks to our sponsor for this episode - Cloud Academy! Go to https://cloudacademy.com/ and use the coupon code ARTIST for 50% off the monthly subscription fee for life!\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh\n\nIf you want to interact with me multiple times a week, join Data Science Dream Job for 70% off: http://dsdj.co/artists70\n\nWatch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm\n\nWe were voted one of the top ten data science podcasts by FeedSpot - check it out here: https://blog.feedspot.com/data_science_podcasts/\n\nCheckout Nicole's article shouting out the show: https://towardsdatascience.com/supercharge-data-science-3da3ccf103-13da3ccf103\n\nNOTES\n\n[00:01:57] Use your Excel skills to learn python\n\n[00:04:35] Automating stuff you do in Excel with python\n\n[00:06:49] Srivatsan shares tips on complementing your Excel skills with python\n\n[00:08:38] Some resources recommendations for learning python\n\n[00:10:34] How to find out what niche in data science to pursue\n\n[00:15:17] The main problem with graduate level education in data science\n\n[00:15:33] How the real world is different from Kaggle\n\n[00:17:27] How to broaden your skillset\n\n[00:19:47] Ideas for a data engineering project\n\n[00:21:56] What are people looking for in interviews?\n\n[00:24:50] Talking about portfolio projects in interviews\n\n[00:27:23] Think about the question behind the question in an interview\n\n[00:34:13] How Srivatsan comes up with new topics for his YouTube channel\n\n[00:36:08] The importance of understanding the basics\n\n[00:38:10] We talk about SQL \n\n[00:42:37] Real world experience beyond the workplace\n\n[00:44:47] Common SQL questions in an interview\n\n[00:49:57] Don’t undersell yourself in an interview\n\n[00:50:21] Storytelling in data science\n\n[00:57:37] Is a Masters programs in data analytics, data science and/or computer science valuable?\n\n[01:00:21] Finding datasets for projectsSpecial Guests: Nicole Janeway Bills and Srivatsan Srinivasan.","content_html":"We have a surprise visit from friend of the podcast, Srivatsan Srinivasan!
\n\nLot's of awesome topics covered in this office hour session!
\n\nHuge thanks to our sponsor for this episode - Cloud Academy! Go to https://cloudacademy.com/ and use the coupon code ARTIST for 50% off the monthly subscription fee for life!
\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh
\n\nIf you want to interact with me multiple times a week, join Data Science Dream Job for 70% off: http://dsdj.co/artists70
\n\nWatch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm
\n\nWe were voted one of the top ten data science podcasts by FeedSpot - check it out here: https://blog.feedspot.com/data_science_podcasts/
\n\nCheckout Nicole's article shouting out the show: https://towardsdatascience.com/supercharge-data-science-3da3ccf103-13da3ccf103
\n\nNOTES
\n\n[00:01:57] Use your Excel skills to learn python
\n\n[00:04:35] Automating stuff you do in Excel with python
\n\n[00:06:49] Srivatsan shares tips on complementing your Excel skills with python
\n\n[00:08:38] Some resources recommendations for learning python
\n\n[00:10:34] How to find out what niche in data science to pursue
\n\n[00:15:17] The main problem with graduate level education in data science
\n\n[00:15:33] How the real world is different from Kaggle
\n\n[00:17:27] How to broaden your skillset
\n\n[00:19:47] Ideas for a data engineering project
\n\n[00:21:56] What are people looking for in interviews?
\n\n[00:24:50] Talking about portfolio projects in interviews
\n\n[00:27:23] Think about the question behind the question in an interview
\n\n[00:34:13] How Srivatsan comes up with new topics for his YouTube channel
\n\n[00:36:08] The importance of understanding the basics
\n\n[00:38:10] We talk about SQL
\n\n[00:42:37] Real world experience beyond the workplace
\n\n[00:44:47] Common SQL questions in an interview
\n\n[00:49:57] Don’t undersell yourself in an interview
\n\n[00:50:21] Storytelling in data science
\n\n[00:57:37] Is a Masters programs in data analytics, data science and/or computer science valuable?
\n\n[01:00:21] Finding datasets for projects
Special Guests: Nicole Janeway Bills and Srivatsan Srinivasan.
","summary":"","date_published":"2020-11-08T00:00:00.000-05:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/84c24092-e333-4af4-8a7b-2ad9069dc0e1.mp3","mime_type":"audio/mpeg","size_in_bytes":35156636,"duration_in_seconds":3888}]},{"id":"9bad1ced-2149-46e4-9f35-017cc25676a4","title":"How To Make Better Decisions | Annie Duke","url":"https://theartistsofdatascience.fireside.fm/annie-duke","content_text":"Annie Duke is a is a poker champion turned author, consultant, and corporate speaker whose here to teach us how to get comfortable with uncertainty and make better decisions.\n\nWHAT YOU'LL LEARN\n\nWe go deep into Annie's books: Thinking in Bets and How to Decide. By the end of this episode you'll have a set of tools to help you in your decision making process. And you'll also get some insight into why the election doesn't go the way you thought it would!\n\nFIND ANNIE ONLINE\n\nWebsite: https://www.annieduke.com/\n\nTwitter: https://twitter.com/annieduke\n\nLinkedIn: https://www.linkedin.com/in/annie-duke-30ab2b5/\n\nQUOTES\n\n[12:25] \"Betting is basically saying I have some set of limited resources that I can invest in to and I'm choosing among options where how that option turns out is not deterministic. It's probabilistic.\"\n\n[15:09] \"...And whichever one you choose is just a prediction of which one's more likely to produce a happier version of you in the future.\"\n\n[16:25] \"We're living in a probabilistic world, meaning that there are very few decisions that you can make that are guaranteed to have one single outcome.\"\n\n[20:14] \"But presidential campaigns actually happen over quite a long period of time...Can you think of anything? Where in real time, people are analyzing the decisions that are being made more than a national presidential election.\"\n\n[26:10] \"Our brains really like to make a create a narrative that makes sense where one thing leads to another and kind of an orderly fashion. We really aren't comfortable with randomness.\" \n\n[28:19] \"So luck is intervening between the decisions that you make, the option that you choose, and the particular outcome that you happen to observe.\"\n\n[32:43] \"I also don't know a lot of stuff. The way to solve for that is to go explore the universe of stuff that I don't know, and to explore that in a really objective way. Where I'm kind of like maximizing my ability to run into information that is different than the things that I believe to be true,.\"\n\n[39:40] \"Well, smart people are just better at spinning narratives. They're better at looking at a set of data and interpreting that data to fit the model that they already have. That's just why it's just like this kind of narrative spinning that's kind of going on in our heads.\"\n\n[01:01:58] \"It's not that imagining failure causes failure. It's that imagining failure causes success, because if you imagine failure, you can see all the obstacles that might be lying in your path and then you can actually do something about them before you run into the obstacle\"\n\nSHOW NOTES\n\n[00:01:32] Guest introduction\n\n[00:03:00] How Annie became the “Duchess of Poker”\n\n[00:04:09] What Annie’s hometown was like\n\n[00:07:44] What high school was like for Annie\n\n[00:09:59] How Annie’s experiences led to writing her books\n\n[00:12:07] What are bets, what are decisions and what's the relationship between them?\n\n[00:16:06] The perils of “resulting”\n\n[00:17:45] Why you shouldn’t equate decision quality with outcome quality\n\n[00:20:14] Decisions and elections\n\n[00:24:19] Why our brains are not built for rationality\n\n[00:26:10] Why our brains need narrative\n\n[00:30:50] Your beliefs have two major weaknesses\n\n[00:35:48] Why being smarter makes you more susceptible to motivated reasoning\n\n[00:40:48] The decision multiverse\n\n[00:44:29] Your good outcomes aren’t always a result of good decision making\n\n[00:48:10] How Thinking in Bets changed my life and a case study of Bayesian psychology in the job search process\n\n[00:53:42] Dealing with things that are not in your control\n\n[00:55:46] The pre-mortem\n\n[00:59:01] The power of negative visualization \n\n[01:03:36] The influence of Stoic philosophy on Annie’s work\n\n[01:04:02] A dude in a basement has a hypothesis…\n\n[01:04:29] Positive thinking and the Reticular Activating System\n\n[01:08:07] The Alliance for Decision Education.\n\n[01:09:56] We’re not teaching kids the things they really need to know\n\n[01:14:41] It’s one hundred years in the future - what do you want to be remembered for?\n\n[01:15:01] The random roundSpecial Guest: Annie Duke.","content_html":"Annie Duke is a is a poker champion turned author, consultant, and corporate speaker whose here to teach us how to get comfortable with uncertainty and make better decisions.
\n\nWHAT YOU'LL LEARN
\n\nWe go deep into Annie's books: Thinking in Bets and How to Decide. By the end of this episode you'll have a set of tools to help you in your decision making process. And you'll also get some insight into why the election doesn't go the way you thought it would!
\n\nFIND ANNIE ONLINE
\n\nWebsite: https://www.annieduke.com/
\n\nTwitter: https://twitter.com/annieduke
\n\nLinkedIn: https://www.linkedin.com/in/annie-duke-30ab2b5/
\n\nQUOTES
\n\n[12:25] "Betting is basically saying I have some set of limited resources that I can invest in to and I'm choosing among options where how that option turns out is not deterministic. It's probabilistic."
\n\n[15:09] "...And whichever one you choose is just a prediction of which one's more likely to produce a happier version of you in the future."
\n\n[16:25] "We're living in a probabilistic world, meaning that there are very few decisions that you can make that are guaranteed to have one single outcome."
\n\n[20:14] "But presidential campaigns actually happen over quite a long period of time...Can you think of anything? Where in real time, people are analyzing the decisions that are being made more than a national presidential election."
\n\n[26:10] "Our brains really like to make a create a narrative that makes sense where one thing leads to another and kind of an orderly fashion. We really aren't comfortable with randomness."
\n\n[28:19] "So luck is intervening between the decisions that you make, the option that you choose, and the particular outcome that you happen to observe."
\n\n[32:43] "I also don't know a lot of stuff. The way to solve for that is to go explore the universe of stuff that I don't know, and to explore that in a really objective way. Where I'm kind of like maximizing my ability to run into information that is different than the things that I believe to be true,."
\n\n[39:40] "Well, smart people are just better at spinning narratives. They're better at looking at a set of data and interpreting that data to fit the model that they already have. That's just why it's just like this kind of narrative spinning that's kind of going on in our heads."
\n\n[01:01:58] "It's not that imagining failure causes failure. It's that imagining failure causes success, because if you imagine failure, you can see all the obstacles that might be lying in your path and then you can actually do something about them before you run into the obstacle"
\n\nSHOW NOTES
\n\n[00:01:32] Guest introduction
\n\n[00:03:00] How Annie became the “Duchess of Poker”
\n\n[00:04:09] What Annie’s hometown was like
\n\n[00:07:44] What high school was like for Annie
\n\n[00:09:59] How Annie’s experiences led to writing her books
\n\n[00:12:07] What are bets, what are decisions and what's the relationship between them?
\n\n[00:16:06] The perils of “resulting”
\n\n[00:17:45] Why you shouldn’t equate decision quality with outcome quality
\n\n[00:20:14] Decisions and elections
\n\n[00:24:19] Why our brains are not built for rationality
\n\n[00:26:10] Why our brains need narrative
\n\n[00:30:50] Your beliefs have two major weaknesses
\n\n[00:35:48] Why being smarter makes you more susceptible to motivated reasoning
\n\n[00:40:48] The decision multiverse
\n\n[00:44:29] Your good outcomes aren’t always a result of good decision making
\n\n[00:48:10] How Thinking in Bets changed my life and a case study of Bayesian psychology in the job search process
\n\n[00:53:42] Dealing with things that are not in your control
\n\n[00:55:46] The pre-mortem
\n\n[00:59:01] The power of negative visualization
\n\n[01:03:36] The influence of Stoic philosophy on Annie’s work
\n\n[01:04:02] A dude in a basement has a hypothesis…
\n\n[01:04:29] Positive thinking and the Reticular Activating System
\n\n[01:08:07] The Alliance for Decision Education.
\n\n[01:09:56] We’re not teaching kids the things they really need to know
\n\n[01:14:41] It’s one hundred years in the future - what do you want to be remembered for?
\n\n[01:15:01] The random round
Special Guest: Annie Duke.
","summary":"","date_published":"2020-11-02T00:00:00.000-05:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/9bad1ced-2149-46e4-9f35-017cc25676a4.mp3","mime_type":"audio/mpeg","size_in_bytes":42488999,"duration_in_seconds":4677}]},{"id":"e3c6b807-c93b-4f88-b25e-a143e91f7129","title":"Data Science Happy Hours 7, 30OCT2020","url":"https://theartistsofdatascience.fireside.fm/oh7","content_text":"Machine Learning legend Vin Vashishta swings by office hour to chat! Tonnes of awesome insight into what the future of data science is going to look like, why feature engineering can be dangerous, why a model is a hypothesis, and more!\n\nHuge thanks to our sponsor for this episode - Cloud Academy! Go to https://cloudacademy.com/ and use the coupon code ARTIST for 50% off the monthly subscription fee for life!\n\n[00:03:54] What I do as a mentor at DSDJ\n\n[00:10:56] Vin’s perspective on the data science job market due to COVID\n\n[00:14:09] Is data science going out of fashion?\n\n[00:16:55] The two types of data scientists out there, one of them won’t survive\n\n[00:21:50] You know, it's funny. It's got to be monitoring and production notes and stuff.\n\n[00:23:25] Question on a project that an attendee was working on – clustering and topic modeling\n\n[00:27:27] Saving models (to serve later)\n\n[00:32:02] Is analytics data science?\n\n[00:35:13] A philosophy less on feature engineering.\n\n[00:40:45] Old school data mining and feature engineering\n\n[00:46:02] You must validate your model\n\n[00:55:15] Why is a model a hypothesis\n\n[00:59:08] The importance of experimenting\n\n[01:04:37] Is it ever OK to build a biased model?\n\n[01:08:59] Preventing bad biases\n\n[01:10:54] A philosophy of modeling\n\n[01:15:24] Do we rule out deep learning?\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh\n\nIf you want to interact with me multiple times a week, join Data Science Dream Job for 70% off: http://dsdj.co/artists70\n\nWatch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm\n\nWe were voted one of the top ten data science podcasts by FeedSpot - check it out here: https://blog.feedspot.com/data_science_podcasts/\n\nI was on the Human Prosperity podcast, check it out here: https://www.youtube.com/watch?v=RbD3aV2TfKkSpecial Guest: Vin Vashishta.","content_html":"Machine Learning legend Vin Vashishta swings by office hour to chat! Tonnes of awesome insight into what the future of data science is going to look like, why feature engineering can be dangerous, why a model is a hypothesis, and more!
\n\nHuge thanks to our sponsor for this episode - Cloud Academy! Go to https://cloudacademy.com/ and use the coupon code ARTIST for 50% off the monthly subscription fee for life!
\n\n[00:03:54] What I do as a mentor at DSDJ
\n\n[00:10:56] Vin’s perspective on the data science job market due to COVID
\n\n[00:14:09] Is data science going out of fashion?
\n\n[00:16:55] The two types of data scientists out there, one of them won’t survive
\n\n[00:21:50] You know, it's funny. It's got to be monitoring and production notes and stuff.
\n\n[00:23:25] Question on a project that an attendee was working on – clustering and topic modeling
\n\n[00:27:27] Saving models (to serve later)
\n\n[00:32:02] Is analytics data science?
\n\n[00:35:13] A philosophy less on feature engineering.
\n\n[00:40:45] Old school data mining and feature engineering
\n\n[00:46:02] You must validate your model
\n\n[00:55:15] Why is a model a hypothesis
\n\n[00:59:08] The importance of experimenting
\n\n[01:04:37] Is it ever OK to build a biased model?
\n\n[01:08:59] Preventing bad biases
\n\n[01:10:54] A philosophy of modeling
\n\n[01:15:24] Do we rule out deep learning?
\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh
\n\nIf you want to interact with me multiple times a week, join Data Science Dream Job for 70% off: http://dsdj.co/artists70
\n\nWatch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm
\n\nWe were voted one of the top ten data science podcasts by FeedSpot - check it out here: https://blog.feedspot.com/data_science_podcasts/
\n\nI was on the Human Prosperity podcast, check it out here: https://www.youtube.com/watch?v=RbD3aV2TfKk
Special Guest: Vin Vashishta.
","summary":"Huge thanks to our sponsor for this episode - Cloud Academy! Go to https://cloudacademy.com/ and use the coupon code ARTIST for 50% off the monthly subscription fee for life!","date_published":"2020-11-01T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/e3c6b807-c93b-4f88-b25e-a143e91f7129.mp3","mime_type":"audio/mpeg","size_in_bytes":43827257,"duration_in_seconds":4915}]},{"id":"f8fbaa63-15de-41a5-bfe5-7573ca09165b","title":"A Mad Scientist Fights Against Stupidity | Sean Derrig","url":"https://theartistsofdatascience.fireside.fm/sean-derrig","content_text":"Sean Derrig is NOT an eco-warrior, he’s a scientist. And he’s on a mission. \n\nHe’s also the author of a wildly entertaining and informative blog called Rectofossal Ambiguity - where he takes on the alter ego RectoFossa, a grumpy microbiologist who thinks writing this blog might be an antidote to all The Stupid on the internet. Recto Fossa is latin for arsehole, apparently. \n\nHuge thanks to our sponsor for this episode - Cloud Academy! Go to https://cloudacademy.com/ and use the coupon code ARTIST for 50% off the monthly subscription fee for life!\n\nFIND SEAN ONLINE\n\nLinkedIn: https://www.linkedin.com/in/seanderrig/\n\nWebsite/Blog: https://rectofossal.com/\n\nQUOTES \n\n[00:20:59] \"So biologists, we're all obsessed with shit, basically. Especially microbiologists.\"\n\n[58:32] \"You need to understand that your success is entirely wrapped up in how successful you can make your team. It's not about you. It's about the people you put around you and what you can do to make them truly successful. And that's what you got your success from, because the company isn't going to grow based on finding your own ego. It just it doesn't - it would be great if it did. It would be marvelous. But life life doesn't work like that.\"\n\n[01:00:17] \"And without the ego and the drive of the lunatic at the helm, things don't happen. However, you do need somebody with their eye on the brakes and an eye on the cliff that you're hurtling towards, as well as.\"\n\n[01:03:56] \"I'd say just keep canceling the bullshit out there. Let's get the good studies out there and make sure that people understand what they mean and what they don't. And in terms of science communication, what you need to suss out really quickly is the level you need to pitch something at\"\n\n[01:05:11] \"Either we can do this, which we think is quite a good idea, or we can hold our hands in a bucket of shit and give ourselves a huge round of applause. Which would you prefer to do? Well, I think I'll do the first one.\"\n\n[01:06:36] \"One of the greatest advertising copywriters ever once said when you're writing it out, that's only 10 percent of people read beyond the first line. So you've blown 90 percent of your budget on the first sentence. Make it a fucking good one. And in that, I think sums it up, it's you need to make sure you grab their attention\"\n\n[01:11:35] \"I'm basically unemployable, and I haven't done too badly.\"\n\n[01:16:39] \"You need to continually reinvent and improve yourself because there's always a superior model.\"\n\n[01:18:13] \"There's just so much stuff out there that I don't know. I'm totally disappearing in a bubble of my own confirmation bias.\"\n\n[01:21:46] \"If you're uncomfortable with gay marriage or opposite marriage, it's fine, you can be as uncomfortable with that as you want, just don't marry a gay person and it will never, ever, ever, ever affect you\"\n\nSHOW NOTES\n\n[00:01:33] Guest Introduction\n\n[00:03:28] Sean’s journey to the dark side of microbiology\n\n[00:06:56] What's the dark side of microbiology?\n\nSean Derrig: [00:09:18] 70 billion friendly bacteria \n\n[00:14:31] WTF is a “fatburg”\n\n[00:18:27] All the unexpected ways we’re wasting water\n\n[00:21:05] What is a radicle?\n\n[00:21:42] Debunking big pharma\n\nSean Derrig: [00:23:36] The importance of good inclusion criteria\n\n[00:27:08] Biases in biological sciences\n\n[00:31:21] The importance of randomized trials\n\n[00:34:56] How to not bullshit yourself\n\n[00:38:34] Question everything\n\n[00:39:53] Bayes theorem and COVID testing\n\n[00:49:34] Which is worse for COVID testing: false positives or false negatives?\n\n[00:52:31] Sensitivity and specificity in COVID testing\n\n[00:53:35] Bayesian psychology\n\n[00:56:00] Tips for entrepreneurs\n\n[00:58:32] What success is all about\n\n[00:59:55] Traits of successful entrepreneurs\n\n[01:01:39] Entrepreneur in the COVID era\n\n[01:01:50] That would be kind of interesting inside. \n\n[01:04:52] How to communicate with executives in a way that will make them care\n\n[01:08:15] Sean talks about his patents\n\n[01:11:27] What's the one thing you want people to learn from your story?\n\n[01:12:02] Lightning roundSpecial Guest: Sean Derrig.","content_html":"Sean Derrig is NOT an eco-warrior, he’s a scientist. And he’s on a mission.
\n\nHe’s also the author of a wildly entertaining and informative blog called Rectofossal Ambiguity - where he takes on the alter ego RectoFossa, a grumpy microbiologist who thinks writing this blog might be an antidote to all The Stupid on the internet. Recto Fossa is latin for arsehole, apparently.
\n\nHuge thanks to our sponsor for this episode - Cloud Academy! Go to https://cloudacademy.com/ and use the coupon code ARTIST for 50% off the monthly subscription fee for life!
\n\nFIND SEAN ONLINE
\n\nLinkedIn: https://www.linkedin.com/in/seanderrig/
\n\nWebsite/Blog: https://rectofossal.com/
\n\nQUOTES
\n\n[00:20:59] "So biologists, we're all obsessed with shit, basically. Especially microbiologists."
\n\n[58:32] "You need to understand that your success is entirely wrapped up in how successful you can make your team. It's not about you. It's about the people you put around you and what you can do to make them truly successful. And that's what you got your success from, because the company isn't going to grow based on finding your own ego. It just it doesn't - it would be great if it did. It would be marvelous. But life life doesn't work like that."
\n\n[01:00:17] "And without the ego and the drive of the lunatic at the helm, things don't happen. However, you do need somebody with their eye on the brakes and an eye on the cliff that you're hurtling towards, as well as."
\n\n[01:03:56] "I'd say just keep canceling the bullshit out there. Let's get the good studies out there and make sure that people understand what they mean and what they don't. And in terms of science communication, what you need to suss out really quickly is the level you need to pitch something at"
\n\n[01:05:11] "Either we can do this, which we think is quite a good idea, or we can hold our hands in a bucket of shit and give ourselves a huge round of applause. Which would you prefer to do? Well, I think I'll do the first one."
\n\n[01:06:36] "One of the greatest advertising copywriters ever once said when you're writing it out, that's only 10 percent of people read beyond the first line. So you've blown 90 percent of your budget on the first sentence. Make it a fucking good one. And in that, I think sums it up, it's you need to make sure you grab their attention"
\n\n[01:11:35] "I'm basically unemployable, and I haven't done too badly."
\n\n[01:16:39] "You need to continually reinvent and improve yourself because there's always a superior model."
\n\n[01:18:13] "There's just so much stuff out there that I don't know. I'm totally disappearing in a bubble of my own confirmation bias."
\n\n[01:21:46] "If you're uncomfortable with gay marriage or opposite marriage, it's fine, you can be as uncomfortable with that as you want, just don't marry a gay person and it will never, ever, ever, ever affect you"
\n\nSHOW NOTES
\n\n[00:01:33] Guest Introduction
\n\n[00:03:28] Sean’s journey to the dark side of microbiology
\n\n[00:06:56] What's the dark side of microbiology?
\n\nSean Derrig: [00:09:18] 70 billion friendly bacteria
\n\n[00:14:31] WTF is a “fatburg”
\n\n[00:18:27] All the unexpected ways we’re wasting water
\n\n[00:21:05] What is a radicle?
\n\n[00:21:42] Debunking big pharma
\n\nSean Derrig: [00:23:36] The importance of good inclusion criteria
\n\n[00:27:08] Biases in biological sciences
\n\n[00:31:21] The importance of randomized trials
\n\n[00:34:56] How to not bullshit yourself
\n\n[00:38:34] Question everything
\n\n[00:39:53] Bayes theorem and COVID testing
\n\n[00:49:34] Which is worse for COVID testing: false positives or false negatives?
\n\n[00:52:31] Sensitivity and specificity in COVID testing
\n\n[00:53:35] Bayesian psychology
\n\n[00:56:00] Tips for entrepreneurs
\n\n[00:58:32] What success is all about
\n\n[00:59:55] Traits of successful entrepreneurs
\n\n[01:01:39] Entrepreneur in the COVID era
\n\n[01:01:50] That would be kind of interesting inside.
\n\n[01:04:52] How to communicate with executives in a way that will make them care
\n\n[01:08:15] Sean talks about his patents
\n\n[01:11:27] What's the one thing you want people to learn from your story?
\n\n[01:12:02] Lightning round
Special Guest: Sean Derrig.
","summary":"Huge thanks to our sponsor for this episode - Cloud Academy! Go to https://cloudacademy.com/ and use the coupon code ARTIST for 50% off the monthly subscription fee for life!","date_published":"2020-10-29T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/f8fbaa63-15de-41a5-bfe5-7573ca09165b.mp3","mime_type":"audio/mpeg","size_in_bytes":46173229,"duration_in_seconds":5079}]},{"id":"d75240aa-72f0-404c-84e1-0deb551d3a51","title":"Happiness and Productivity Tips from a Data Engineer | Max Zheng","url":"https://theartistsofdatascience.fireside.fm/max-zheng","content_text":"Despite Max's outward success, he spent much of his life unmotivated and depressed. Struggling with bouts of frustrations, conflicts with others, relationship and career failures, he felt so unhappy he was contemplating suicide.\n\nHe’s since taken on a journey of personal growth and development acquiring a brand new mindset and changing his relationship with himself, and those around him. \n\nToday he comes on the show to talk to us about data engineering and shares SEVERAL PRICELESS tips for productivity and happiness\n\nHuge thanks to our sponsor for this episode - Cloud Academy! Go to https://cloudacademy.com/ and use the coupon code ARTIST for 50% off the monthly subscription fee for life!\n\nFIND MAX ONLINE\n\nLinkedIn: https://www.linkedin.com/in/maxzheng/\n\nThe Life Guide on GitHub: https://github.com/maxzheng/great-life-guide\n\nDouble Your Happiness Guide: http://double.guide\n\nI was Max's Human Prosperity podcast, check it out here: https://www.youtube.com/watch?v=RbD3aV2TfKk\n\nWHAT YOU'LL LEARN\n\n[07:00] The difference between data engineering and software engineering\n\n[08:46] The spectrum of data engineering\n\n[11:21] Tips for people transitioning from software engineering to data engineering\n\n[12:26] How to prepare for interviews in data engineering\n\nQUOTES\n\n[43:19] \"And that applies to software development or data engineering work and things like that. So when you have a data project, make sure that you are actually working on a solid foundation. Don't just build everything as fast as just writing some script and then throw it out and be done with it because you have to maintain that goal. So you copy and paste a duplicate bunch of code everywhere. And that's a wrong thing, obviously.\"\n\n[55:18] \"And that's one way of seeing it, because when you actually really look at it, a failure is simply something you don't know for now. And then once you do go through it and figure out what you don't know, you have learned something is a lesson in disguise.\"\n\n[58:00] \"It's really important that we understand two or three aspect of memory and how it works. And one is that memory works based on association, so it's always associated with something that you already know. And if you try to remember something without associating something you to know, it'll be very hard\"\n\n[01:06:29] \"So loving yourself completely is to remove any shackles from you. Believe in yourself what you can actually use to push yourself forward. So and once you have this nothing holding you back, then you can run a full throttle\"\n\nSHOW NOTES\n\n[00:01:34] Guest introduction\n\n[00:02:47] How did you get to where you are today in your career?\n\n[00:05:45] How’d you get into data engineering?\n\n[00:07:01] The key difference between software engineering and Data engineering?\n\n[00:08:46] Tips for transitioning from software engineering to data engineering\n\n[00:11:21] How to get hands-on data engineering experience\n\n[00:12:11] Tips for the job search process\n\n[00:15:38] How and why Max grew his LinkedIn network so quickly\n\n[00:17:16] How Max defeated his old self and emerged a stronger, better person\n\n[00:22:21] Depression hidden in plain sight\n\n[00:23:18] How Max started his new mission in life\n\n[00:29:36] The difference between being right and doing right \n\n[00:31:02] Four steps to being happy\n\n[00:33:50] The happiness framework\n\n[00:35:21] Nurture your drive\n\n[00:38:14] A framework for being more productive\n\n[00:46:08] Max helps me with my battle against distraction\n\n[00:50:02] What to do when you lose momentum and motivation\n\n[00:52:14] How to fight imposter syndrome\n\n[00:57:00] Choose your belief system\n\n[00:57:51] Tips to increase your memory\n\n[01:01:46] The importance of a growth mindset\n\n[01:05:58] What's the one thing you want people to learn from your story?\n\n[01:06:45] Lightning roundSpecial Guest: Max Zheng.","content_html":"Despite Max's outward success, he spent much of his life unmotivated and depressed. Struggling with bouts of frustrations, conflicts with others, relationship and career failures, he felt so unhappy he was contemplating suicide.
\n\nHe’s since taken on a journey of personal growth and development acquiring a brand new mindset and changing his relationship with himself, and those around him.
\n\nToday he comes on the show to talk to us about data engineering and shares SEVERAL PRICELESS tips for productivity and happiness
\n\nHuge thanks to our sponsor for this episode - Cloud Academy! Go to https://cloudacademy.com/ and use the coupon code ARTIST for 50% off the monthly subscription fee for life!
\n\nFIND MAX ONLINE
\n\nLinkedIn: https://www.linkedin.com/in/maxzheng/
\n\nThe Life Guide on GitHub: https://github.com/maxzheng/great-life-guide
\n\nDouble Your Happiness Guide: http://double.guide
\n\nI was Max's Human Prosperity podcast, check it out here: https://www.youtube.com/watch?v=RbD3aV2TfKk
\n\nWHAT YOU'LL LEARN
\n\n[07:00] The difference between data engineering and software engineering
\n\n[08:46] The spectrum of data engineering
\n\n[11:21] Tips for people transitioning from software engineering to data engineering
\n\n[12:26] How to prepare for interviews in data engineering
\n\nQUOTES
\n\n[43:19] "And that applies to software development or data engineering work and things like that. So when you have a data project, make sure that you are actually working on a solid foundation. Don't just build everything as fast as just writing some script and then throw it out and be done with it because you have to maintain that goal. So you copy and paste a duplicate bunch of code everywhere. And that's a wrong thing, obviously."
\n\n[55:18] "And that's one way of seeing it, because when you actually really look at it, a failure is simply something you don't know for now. And then once you do go through it and figure out what you don't know, you have learned something is a lesson in disguise."
\n\n[58:00] "It's really important that we understand two or three aspect of memory and how it works. And one is that memory works based on association, so it's always associated with something that you already know. And if you try to remember something without associating something you to know, it'll be very hard"
\n\n[01:06:29] "So loving yourself completely is to remove any shackles from you. Believe in yourself what you can actually use to push yourself forward. So and once you have this nothing holding you back, then you can run a full throttle"
\n\nSHOW NOTES
\n\n[00:01:34] Guest introduction
\n\n[00:02:47] How did you get to where you are today in your career?
\n\n[00:05:45] How’d you get into data engineering?
\n\n[00:07:01] The key difference between software engineering and Data engineering?
\n\n[00:08:46] Tips for transitioning from software engineering to data engineering
\n\n[00:11:21] How to get hands-on data engineering experience
\n\n[00:12:11] Tips for the job search process
\n\n[00:15:38] How and why Max grew his LinkedIn network so quickly
\n\n[00:17:16] How Max defeated his old self and emerged a stronger, better person
\n\n[00:22:21] Depression hidden in plain sight
\n\n[00:23:18] How Max started his new mission in life
\n\n[00:29:36] The difference between being right and doing right
\n\n[00:31:02] Four steps to being happy
\n\n[00:33:50] The happiness framework
\n\n[00:35:21] Nurture your drive
\n\n[00:38:14] A framework for being more productive
\n\n[00:46:08] Max helps me with my battle against distraction
\n\n[00:50:02] What to do when you lose momentum and motivation
\n\n[00:52:14] How to fight imposter syndrome
\n\n[00:57:00] Choose your belief system
\n\n[00:57:51] Tips to increase your memory
\n\n[01:01:46] The importance of a growth mindset
\n\n[01:05:58] What's the one thing you want people to learn from your story?
\n\n[01:06:45] Lightning round
Special Guest: Max Zheng.
","summary":"Huge thanks to our sponsor for this episode - Cloud Academy! Go to https://cloudacademy.com/ and use the coupon code ARTIST for 50% off the monthly subscription fee for life!\r\n\r\nIf you want to interact with me multiple times a week, join Data Science Dream Job for 70% off: http://dsdj.co/artists70. \r\n\r\n","date_published":"2020-10-26T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/d75240aa-72f0-404c-84e1-0deb551d3a51.mp3","mime_type":"audio/mpeg","size_in_bytes":46364245,"duration_in_seconds":4477}]},{"id":"cdd378f1-9bce-4b33-8037-30813aae295e","title":"Data Science Happy Hours 6, 23OCT2020","url":"https://theartistsofdatascience.fireside.fm/oh6","content_text":"Another awesome episode this week! Our friends Ashen, Navya, and Haseeb come back for some really insightful questions.\n\nWe talk about whether you really need math skills in data science, how to answer the \"what's your salary expectations\" questions, a resume review, some ideas for data science projects, and our friends turn the tables and interview me...again!\n\nI really enjoyed this weeks session - tune in and let me know what you think: theartistsofdatascience@gmail.com. By the way you can email me anytime with any question and I promise I will respond!\n\nHuge thanks to our sponsor for this episode - Cloud Academy! Go to https://cloudacademy.com/ and use the coupon code ARTIST for 50% off the monthly subscription fee for life!\n\nIf you want to interact with me multiple times a week, join Data Science Dream Job for 70% off: http://dsdj.co/artists70\n\nWatch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm\n\nWe were voted one of the top ten data science podcasts by FeedSpot - check it out here: https://blog.feedspot.com/data_science_podcasts/\n\nI was on the Human Prosperity podcast, check it out here: https://www.youtube.com/watch?v=RbD3aV2TfKk\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh","content_html":"Another awesome episode this week! Our friends Ashen, Navya, and Haseeb come back for some really insightful questions.
\n\nWe talk about whether you really need math skills in data science, how to answer the "what's your salary expectations" questions, a resume review, some ideas for data science projects, and our friends turn the tables and interview me...again!
\n\nI really enjoyed this weeks session - tune in and let me know what you think: theartistsofdatascience@gmail.com. By the way you can email me anytime with any question and I promise I will respond!
\n\nHuge thanks to our sponsor for this episode - Cloud Academy! Go to https://cloudacademy.com/ and use the coupon code ARTIST for 50% off the monthly subscription fee for life!
\n\nIf you want to interact with me multiple times a week, join Data Science Dream Job for 70% off: http://dsdj.co/artists70
\n\nWatch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm
\n\nWe were voted one of the top ten data science podcasts by FeedSpot - check it out here: https://blog.feedspot.com/data_science_podcasts/
\n\nI was on the Human Prosperity podcast, check it out here: https://www.youtube.com/watch?v=RbD3aV2TfKk
\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh
","summary":"Huge thanks to our sponsor for this epsiode - Cloud Academy! Go to https://cloudacademy.com/ and use the coupon code ARTIST for 50% off the monthly subscription fee for life!\r\n","date_published":"2020-10-25T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/cdd378f1-9bce-4b33-8037-30813aae295e.mp3","mime_type":"audio/mpeg","size_in_bytes":38694301,"duration_in_seconds":4550}]},{"id":"be6d6955-44a3-43bd-98f7-3a4aa2f12f44","title":"Fighting Churn with Data Science | Carl Gold, PhD","url":"https://theartistsofdatascience.fireside.fm/carl-gold","content_text":"Carl is a former Wall Street Quant turned data scientist who is leading the battle against churn, using data as his weapon.\n\nA data scientist, he uses a variety of tools and techniques to analyze data around online systems, and his expertise has led to the creation of the Subscription Economy Index.\n\nCurrently, he’s the Chief Data Scientist at Zuora - a comprehensive subscription management platform and newly public Silicon Valley “unicorn” with more than 1,000 customers worldwide. \n\nFIND CARL ONLINE\n\nWebsite: https://fightchurnwithdata.com/\n\nLinkedIn: https://www.linkedin.com/in/carlgold/\n\nTwitter: https://twitter.com/carl24k\n\nGitHub: https://github.com/carl24k\n\nWHAT YOU'LL LEARN\n\n[00:16:01] What is churn?\n\n[00:21:48] Metrics for understanding churn\n\n[00:24:01] Feature engineering for churn\n\n[00:27:22] Why ratio metrics are the best best in your battle against churn\n\n[00:33:09] Dealing with outliers \n\n[00:39:34] More feature engineering tips\n\nQUOTES\n\n[09:06] \"When I started out, of course, people thought machine learning was trash...No one was that interested in machine learning back in the early 2000s. It wasn't until after Google essentially had showed how much they could do with machine learning in a production environment with big data.\"\n\n[12:22] \"It should enable better decisions, too. Not just faster decisions by getting the right data to the right people and giving them the right tools. We really should see companies making more optimal decisions.\"\n\n[13:30] \"There should be like a Hippocratic Oath for Data scientists, which means that goes beyond just you don't want to make mistakes. It means that you shouldn't be working on those, you know, on those dangerous applications. \"\n\n[22:04] \"the features that you choose in my mind are really the main part of solving any data science problem and not the algorithm. I show actually in my book that if you do a good job on your feature engineering, the algorithm that you choose is not that important for your accuracy. So feature engineering always has number one importance in Data science\"\n\nSHOW NOTES\n\n[00:01:31] Introduction for our guest\n\n[00:02:54] Carl’s path into data science\n\n[00:04:30] The fascination with churn\n\n[00:08:04] How much more hyped do you think the field has become since you first broke into it?\n\n[00:09:41] Where do you see the field headed in the next two to five years?\n\n[00:11:20] What do you think would be the biggest positive impact that Data science will have on society in the next two to five years?\n\n[00:12:36] What do you think would be the scariest application of machine learning and data science in the next two to five years?\n\n[00:13:17] As practitioners of machine learning, what do you think would be some of our biggest concerns when we're out there doing our work?\n\n[00:16:01] What is Churn? Is that what we do we make butter.\n\n[00:17:27] So why is churn so hard to fight?\n\n[00:21:48] The importance of metrics in our battle against churn\n\n[00:24:01] How do we go from raw event data to metrics?\n\n[00:24:45] How do cohorts help us analyze, predict, and understand churn?\n\n[00:27:22] What are ratio metrics and why are they so powerful?\n\n[00:33:09] Why are outliers so problematic to deal with?\n\nmodel and get information from them, but without them ruining your numbers.\n\n[00:34:57] What are some common mistakes that you've seen Data scientists make when it comes to dealing with outliers?\n\n[00:39:14] How to be more thoughtful when it comes to feature engineering?\n\n[00:42:31] Debunking the common misconception that the choice of algorithm is the most important thing that contributes to model performance.\n\n[00:43:56] Your features don’t need to be the most creative\n\n[00:45:28] Your job isn’t over once you deploy the model\n\n[00:49:05] What are some things that we need to monitor and track - the context of churn - to make sure that our model is doing what it should be, that is performing as we've designed it?\n\n[00:50:26] How COVID is messing up everyone’s churn models\n\n[00:53:14] Is data science an art or science?\n\n[00:55:24] What are some soft skills that Data scientists are missing that are really going to help them take their careers to the next level?\n\n[00:56:51] How could a data scientist develop their business acumen and their product sense \n\n[00:57:44] What to do with these crazy job descriptions\n\n[00:59:27] What’s the one thing you want people to learn from your story?\n\n[01:00:39] The lightning roundSpecial Guest: Carl Gold, Phd.","content_html":"Carl is a former Wall Street Quant turned data scientist who is leading the battle against churn, using data as his weapon.
\n\nA data scientist, he uses a variety of tools and techniques to analyze data around online systems, and his expertise has led to the creation of the Subscription Economy Index.
\n\nCurrently, he’s the Chief Data Scientist at Zuora - a comprehensive subscription management platform and newly public Silicon Valley “unicorn” with more than 1,000 customers worldwide.
\n\nFIND CARL ONLINE
\n\nWebsite: https://fightchurnwithdata.com/
\n\nLinkedIn: https://www.linkedin.com/in/carlgold/
\n\nTwitter: https://twitter.com/carl24k
\n\nGitHub: https://github.com/carl24k
\n\nWHAT YOU'LL LEARN
\n\n[00:16:01] What is churn?
\n\n[00:21:48] Metrics for understanding churn
\n\n[00:24:01] Feature engineering for churn
\n\n[00:27:22] Why ratio metrics are the best best in your battle against churn
\n\n[00:33:09] Dealing with outliers
\n\n[00:39:34] More feature engineering tips
\n\nQUOTES
\n\n[09:06] "When I started out, of course, people thought machine learning was trash...No one was that interested in machine learning back in the early 2000s. It wasn't until after Google essentially had showed how much they could do with machine learning in a production environment with big data."
\n\n[12:22] "It should enable better decisions, too. Not just faster decisions by getting the right data to the right people and giving them the right tools. We really should see companies making more optimal decisions."
\n\n[13:30] "There should be like a Hippocratic Oath for Data scientists, which means that goes beyond just you don't want to make mistakes. It means that you shouldn't be working on those, you know, on those dangerous applications. "
\n\n[22:04] "the features that you choose in my mind are really the main part of solving any data science problem and not the algorithm. I show actually in my book that if you do a good job on your feature engineering, the algorithm that you choose is not that important for your accuracy. So feature engineering always has number one importance in Data science"
\n\nSHOW NOTES
\n\n[00:01:31] Introduction for our guest
\n\n[00:02:54] Carl’s path into data science
\n\n[00:04:30] The fascination with churn
\n\n[00:08:04] How much more hyped do you think the field has become since you first broke into it?
\n\n[00:09:41] Where do you see the field headed in the next two to five years?
\n\n[00:11:20] What do you think would be the biggest positive impact that Data science will have on society in the next two to five years?
\n\n[00:12:36] What do you think would be the scariest application of machine learning and data science in the next two to five years?
\n\n[00:13:17] As practitioners of machine learning, what do you think would be some of our biggest concerns when we're out there doing our work?
\n\n[00:16:01] What is Churn? Is that what we do we make butter.
\n\n[00:17:27] So why is churn so hard to fight?
\n\n[00:21:48] The importance of metrics in our battle against churn
\n\n[00:24:01] How do we go from raw event data to metrics?
\n\n[00:24:45] How do cohorts help us analyze, predict, and understand churn?
\n\n[00:27:22] What are ratio metrics and why are they so powerful?
\n\n[00:33:09] Why are outliers so problematic to deal with?
\n\nmodel and get information from them, but without them ruining your numbers.
\n\n[00:34:57] What are some common mistakes that you've seen Data scientists make when it comes to dealing with outliers?
\n\n[00:39:14] How to be more thoughtful when it comes to feature engineering?
\n\n[00:42:31] Debunking the common misconception that the choice of algorithm is the most important thing that contributes to model performance.
\n\n[00:43:56] Your features don’t need to be the most creative
\n\n[00:45:28] Your job isn’t over once you deploy the model
\n\n[00:49:05] What are some things that we need to monitor and track - the context of churn - to make sure that our model is doing what it should be, that is performing as we've designed it?
\n\n[00:50:26] How COVID is messing up everyone’s churn models
\n\n[00:53:14] Is data science an art or science?
\n\n[00:55:24] What are some soft skills that Data scientists are missing that are really going to help them take their careers to the next level?
\n\n[00:56:51] How could a data scientist develop their business acumen and their product sense
\n\n[00:57:44] What to do with these crazy job descriptions
\n\n[00:59:27] What’s the one thing you want people to learn from your story?
\n\n[01:00:39] The lightning round
Special Guest: Carl Gold, Phd.
","summary":"Fight Churn with Data","date_published":"2020-10-22T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/be6d6955-44a3-43bd-98f7-3a4aa2f12f44.mp3","mime_type":"audio/mpeg","size_in_bytes":99916556,"duration_in_seconds":4163}]},{"id":"8bb0562d-c16f-49dc-800e-476a3d769b71","title":"Storytelling and Public Speaking Tips | Brenden Kumarsamay","url":"https://theartistsofdatascience.fireside.fm/brenden-kumarasamy","content_text":"Brenden is passionate about helping others achieve rocket level success.\n\nHe lives by the philosophy that when you care about serving others and aim to add value to people’s lives, you’ll be able to overcome any fear or obstacle in your path.\n\nThis philosophy has led to him coaching purpose driven entrepreneurs on how to master their message and share their ideas with the world.\n\nFIND BRENDEN ONLINE\n\nLinkedIn :https://www.linkedin.com/in/brendenkumarasamy\n\nWebsite: https://www.mastertalk.ca/\n\nYouTube: https://www.youtube.com/channel/UCBYFP4mZLQovr7W6Si6sueA\n\nTwitter: https://twitter.com/masteryourtalks\n\nInstagram: https://www.instagram.com/masteryourtalk/\n\nQUOTES\n\n[00:05:00] \"Everyone seems to ask themselves what they're passionate about. And I think that is a stupid question. And let me explain why.\" \n\n[00:07:04] \"There are a lot of people who know how to crunch numbers but don't know how to explain them back to people in a way that can be philosophically transformative in people's lives.\"\n\n[00:08:14] \"Public speaking is a skill that anyone can master, but very few people do because it's the hardest skill to hold yourself accountable to.\"\n\n[00:12:33] \"The secret is there is no secret in the sense that if you think you're able to engage your audience from the first time you present something, you're wrong. \"\n\n[00:18:29] \"But if you talk to your audience and just ask yourself the simple question, what are you trying to achieve here? You're trying to help them take a first step. You're trying to get the introverted data scientist to say, hey, you have an idea to share.\"\n\n[00:21:27] \"We're taught to believe that public speaking is a chore. Public speaking is a responsibility and obligation. \"\n\n[00:32:43] \"If you want to stand out in general, you need to be able to tell a story with that data. And by story, I don't mean storytelling going in all this persona's stuff. I mean structuring your ideas in a way that makes sense to a fifth grader who doesn't understand Data science.\"\n\nSHOW NOTES\n\n[00:01:35] Introduction for our guest today\n\n[00:02:47] How Brenden paved his own lane\n\n[00:04:36] How Brenden decided that his mission is to help people be better public speakers\n\n[00:08:04] The “Public Speaking Why”\n\n[00:09:49] How would the world change if you were an exceptional communicator? \n\n[00:10:56] What's the difference between just talking and speaking?\n\n[00:12:16] How do we present information in a way that will get our audience excited and get them excited to hear us share our ideas with them and with the world?\n\n[00:15:41] How do the best speakers in the world design presentations for maximum effect?\n\n[00:19:18] Change your mindset about public speaking\n\n[00:23:18] An exercise for improving your public speaking (I give Brenden a random word and he makes a speech on the spot).\n\n[00:27:05] How important is writing when preparing for speaking?\n\n[00:28:53] How can we become better storytellers?\n\n[00:31:15] How can we identify personas in our audience \n\n[00:33:57] How to communicate with executives\n\n[00:37:06] How to make the most of networking events\n\n[00:39:55] Tips to start becoming a better presenter, today!\n\n[00:41:28] How to move from individual contributor to leadership\n\n[00:43:58] Charity water\n\n[00:48:35] What's the one thing you want people to learn from your story?\n\n[00:50:52] The lightning roundSpecial Guest: Brenden Kumarasamy.","content_html":"Brenden is passionate about helping others achieve rocket level success.
\n\nHe lives by the philosophy that when you care about serving others and aim to add value to people’s lives, you’ll be able to overcome any fear or obstacle in your path.
\n\nThis philosophy has led to him coaching purpose driven entrepreneurs on how to master their message and share their ideas with the world.
\n\nFIND BRENDEN ONLINE
\n\nLinkedIn :https://www.linkedin.com/in/brendenkumarasamy
\n\nWebsite: https://www.mastertalk.ca/
\n\nYouTube: https://www.youtube.com/channel/UCBYFP4mZLQovr7W6Si6sueA
\n\nTwitter: https://twitter.com/masteryourtalks
\n\nInstagram: https://www.instagram.com/masteryourtalk/
\n\nQUOTES
\n\n[00:05:00] "Everyone seems to ask themselves what they're passionate about. And I think that is a stupid question. And let me explain why."
\n\n[00:07:04] "There are a lot of people who know how to crunch numbers but don't know how to explain them back to people in a way that can be philosophically transformative in people's lives."
\n\n[00:08:14] "Public speaking is a skill that anyone can master, but very few people do because it's the hardest skill to hold yourself accountable to."
\n\n[00:12:33] "The secret is there is no secret in the sense that if you think you're able to engage your audience from the first time you present something, you're wrong. "
\n\n[00:18:29] "But if you talk to your audience and just ask yourself the simple question, what are you trying to achieve here? You're trying to help them take a first step. You're trying to get the introverted data scientist to say, hey, you have an idea to share."
\n\n[00:21:27] "We're taught to believe that public speaking is a chore. Public speaking is a responsibility and obligation. "
\n\n[00:32:43] "If you want to stand out in general, you need to be able to tell a story with that data. And by story, I don't mean storytelling going in all this persona's stuff. I mean structuring your ideas in a way that makes sense to a fifth grader who doesn't understand Data science."
\n\nSHOW NOTES
\n\n[00:01:35] Introduction for our guest today
\n\n[00:02:47] How Brenden paved his own lane
\n\n[00:04:36] How Brenden decided that his mission is to help people be better public speakers
\n\n[00:08:04] The “Public Speaking Why”
\n\n[00:09:49] How would the world change if you were an exceptional communicator?
\n\n[00:10:56] What's the difference between just talking and speaking?
\n\n[00:12:16] How do we present information in a way that will get our audience excited and get them excited to hear us share our ideas with them and with the world?
\n\n[00:15:41] How do the best speakers in the world design presentations for maximum effect?
\n\n[00:19:18] Change your mindset about public speaking
\n\n[00:23:18] An exercise for improving your public speaking (I give Brenden a random word and he makes a speech on the spot).
\n\n[00:27:05] How important is writing when preparing for speaking?
\n\n[00:28:53] How can we become better storytellers?
\n\n[00:31:15] How can we identify personas in our audience
\n\n[00:33:57] How to communicate with executives
\n\n[00:37:06] How to make the most of networking events
\n\n[00:39:55] Tips to start becoming a better presenter, today!
\n\n[00:41:28] How to move from individual contributor to leadership
\n\n[00:43:58] Charity water
\n\n[00:48:35] What's the one thing you want people to learn from your story?
\n\n[00:50:52] The lightning round
Special Guest: Brenden Kumarasamy.
","summary":"","date_published":"2020-10-19T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/8bb0562d-c16f-49dc-800e-476a3d769b71.mp3","mime_type":"audio/mpeg","size_in_bytes":84076321,"duration_in_seconds":3503}]},{"id":"690bac7e-82e4-41f2-a789-55235ab847f4","title":"Data Science Happy Hours 5, 16OCT2020","url":"https://theartistsofdatascience.fireside.fm/oh5","content_text":"Excellent questions this week! \n\nWe do a portfolio project review, technical feedback for a project, talk about what the difference is between a data science manager and a data science project manager.\n\nThe participants turn the tables on me in this one and interview me about my process, routines, and how I find people for the podcast.\n\nJoin Data Science Dream Job for 70% off: http://dsdj.co/artists70\n\nWatch the episode on YouTube here: https://youtu.be/1TNjun5t5O8\n\nWe were voted one of the top ten data science podcasts by FeedSpot - check it out here: https://blog.feedspot.com/data_science_podcasts/\n\nI was on the Human Prosperity podcast, check it out here: https://www.youtube.com/watch?v=RbD3aV2TfKk\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh","content_html":"Excellent questions this week!
\n\nWe do a portfolio project review, technical feedback for a project, talk about what the difference is between a data science manager and a data science project manager.
\n\nThe participants turn the tables on me in this one and interview me about my process, routines, and how I find people for the podcast.
\n\nJoin Data Science Dream Job for 70% off: http://dsdj.co/artists70
\n\nWatch the episode on YouTube here: https://youtu.be/1TNjun5t5O8
\n\nWe were voted one of the top ten data science podcasts by FeedSpot - check it out here: https://blog.feedspot.com/data_science_podcasts/
\n\nI was on the Human Prosperity podcast, check it out here: https://www.youtube.com/watch?v=RbD3aV2TfKk
\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh
","summary":"","date_published":"2020-10-18T14:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/690bac7e-82e4-41f2-a789-55235ab847f4.mp3","mime_type":"audio/mpeg","size_in_bytes":29876983,"duration_in_seconds":3346}]},{"id":"dd02fc4f-4903-4c46-a3fe-e252b7fcf1b8","title":"Extreme Ownership in Data Science | Anderson Prewitt, PhD","url":"https://theartistsofdatascience.fireside.fm/anderson-prewitt","content_text":"Dr. Prewitt is a thought leader in innovation, education, and entrepreneurship.\n\nIn addition to working as an engineer and researcher with several Fortune 500 companies and universities before starting his business, he’s also an active researcher, author, and speaker in the areas of innovation, education, and entrepreneurship.\n\nHe’s given talks across the country on topics ranging from student success in STEM to how to leverage technology for business success. He’s also co-author of a book for students interested in pursuing careers in technology titled STEM Navigators.\n\nFIND ANDERSON ONLINE\n\nWEBSITE: https://www.andersondprewitt.com/\n\nLINKEDIN: https://www.linkedin.com/in/dradprewitt/\n\nTWITTER: https://twitter.com/prewittsolution\n\nFACEBOOK: https://www.facebook.com/DrADPrewitt\n\nQUOTES\n\n[00:12:03] \"Data can also be used to harm if it's not done the correct way...I think a very good example is something like we look at things like systemic inequalities, systemic racism, systemic sexism, all of it. We spend a lot of time talking about the sexism, the racism, the inequality. We don't spend as much time talking about the system and really about race. \"\n\n[00:15:51] \"I try to talk about A.I. in terms of raising a child or maybe whatever else... if you train that child up or that baby up and you teach them a wide range of things, they get experiences and you yourself have enough knowledge and actually take the time to learn the right things, teach them, then that child has a better chance of growing up and doing the right things.\"\n\n[00:22:17] \"I think that even some basic understanding of information can be misused for how one wrong line of code or not having a full dataset can affect people in real terms in real time\"\n\n[00:30:27] \"If you don't know that there are multiple paths to get to something, you're only going to go down the road that you know\"\n\nSHOW NOTES \n\n[00:02:05] Introduction for our guest\n\n[00:03:25] How Dr. Prewitt got into data science\n\n[00:07:56] Challenges on the path to getting a PhD\n\n[00:10:43] Where is data science headed in the next two to five years?\n\n[00:15:38] What can we do as practitioners of Data science machine learning to make sure that the work that we're doing isn't perpetuating negative biases?\n\n[00:18:43] Think holistically about what you are building\n\n[00:20:36] How can we educate ourselves on ethics? Where do we turn to for guidance on that to make sure that the work that we are doing is ethical?\n\n[00:23:35] How to take ownership of your self-education\n\n[00:26:17] Don’t torture the data until it confesses\n\n[00:27:47] STEM Navigators\n\n[00:33:02] How do systems work? \n\n[00:38:15] How to use AI for good\n\n[00:41:09] Advice to students who are interested in studying science, technology, engineering or math\n\n[00:44:13] Going back to your book, STEM Navigator's talk about curiosity and asking why not? Do you feel that was a common thread or formula to success for the navigator's that you highlighted in the book?\n\n[00:45:42] Battling imposter syndrome\n\n[00:48:24] The cookie jar\n\n[00:51:26] What can the STEM community do to foster the inclusion of people of color, especially black Americans in particular in our field?\n\n[00:54:46] What's the one thing you want people to learn from your story?\n\n[00:55:38] The lightning roundSpecial Guest: Anderson Prewitt, PhD.","content_html":"Dr. Prewitt is a thought leader in innovation, education, and entrepreneurship.
\n\nIn addition to working as an engineer and researcher with several Fortune 500 companies and universities before starting his business, he’s also an active researcher, author, and speaker in the areas of innovation, education, and entrepreneurship.
\n\nHe’s given talks across the country on topics ranging from student success in STEM to how to leverage technology for business success. He’s also co-author of a book for students interested in pursuing careers in technology titled STEM Navigators.
\n\nFIND ANDERSON ONLINE
\n\nWEBSITE: https://www.andersondprewitt.com/
\n\nLINKEDIN: https://www.linkedin.com/in/dradprewitt/
\n\nTWITTER: https://twitter.com/prewittsolution
\n\nFACEBOOK: https://www.facebook.com/DrADPrewitt
\n\nQUOTES
\n\n[00:12:03] "Data can also be used to harm if it's not done the correct way...I think a very good example is something like we look at things like systemic inequalities, systemic racism, systemic sexism, all of it. We spend a lot of time talking about the sexism, the racism, the inequality. We don't spend as much time talking about the system and really about race. "
\n\n[00:15:51] "I try to talk about A.I. in terms of raising a child or maybe whatever else... if you train that child up or that baby up and you teach them a wide range of things, they get experiences and you yourself have enough knowledge and actually take the time to learn the right things, teach them, then that child has a better chance of growing up and doing the right things."
\n\n[00:22:17] "I think that even some basic understanding of information can be misused for how one wrong line of code or not having a full dataset can affect people in real terms in real time"
\n\n[00:30:27] "If you don't know that there are multiple paths to get to something, you're only going to go down the road that you know"
\n\nSHOW NOTES
\n\n[00:02:05] Introduction for our guest
\n\n[00:03:25] How Dr. Prewitt got into data science
\n\n[00:07:56] Challenges on the path to getting a PhD
\n\n[00:10:43] Where is data science headed in the next two to five years?
\n\n[00:15:38] What can we do as practitioners of Data science machine learning to make sure that the work that we're doing isn't perpetuating negative biases?
\n\n[00:18:43] Think holistically about what you are building
\n\n[00:20:36] How can we educate ourselves on ethics? Where do we turn to for guidance on that to make sure that the work that we are doing is ethical?
\n\n[00:23:35] How to take ownership of your self-education
\n\n[00:26:17] Don’t torture the data until it confesses
\n\n[00:27:47] STEM Navigators
\n\n[00:33:02] How do systems work?
\n\n[00:38:15] How to use AI for good
\n\n[00:41:09] Advice to students who are interested in studying science, technology, engineering or math
\n\n[00:44:13] Going back to your book, STEM Navigator's talk about curiosity and asking why not? Do you feel that was a common thread or formula to success for the navigator's that you highlighted in the book?
\n\n[00:45:42] Battling imposter syndrome
\n\n[00:48:24] The cookie jar
\n\n[00:51:26] What can the STEM community do to foster the inclusion of people of color, especially black Americans in particular in our field?
\n\n[00:54:46] What's the one thing you want people to learn from your story?
\n\n[00:55:38] The lightning round
Special Guest: Anderson Prewitt, PhD.
","summary":"","date_published":"2020-10-15T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/dd02fc4f-4903-4c46-a3fe-e252b7fcf1b8.mp3","mime_type":"audio/mpeg","size_in_bytes":37743074,"duration_in_seconds":4016}]},{"id":"3feb0ded-8187-44a5-bfc6-9bb1303bf1f1","title":"Statistics is the Least Important Part of Data Science | Andrew Gelman, PhD","url":"https://theartistsofdatascience.fireside.fm/andrew-gelman","content_text":"Andrew is an American statistician, professor of statistics and political science, and director of the Applied Statistics Center at Columbia University.\n\nHe frequently writes about Bayesian statistics, displaying data, and interesting trends in social science. \n\nHe’s also well known for writing posts sharing his thoughts on best statistical practices in the sciences, with a frequent emphasis on what he sees as the absurd and unscientific. \n\nFIND ANDREW ONLINE\n\nWebsite: https://statmodeling.stat.columbia.edu/\n\nTwitter: https://twitter.com/StatModeling\n\nQUOTES\n\n[00:04:16] \"We've already passed peak statistics...\"\n\n[00:05:13] \"One thing that we sometimes like to say is that big data need big model because big data are available data. They're not designed experiments, they're not random samples. Often big data means these are measurements. \"\n\n[00:22:05] \"If you design an experiment, you want to know what you're going to do later. So most obviously, you want your sample size to be large enough so that given the effect size that you expect to see, you'll get a strong enough signal that you can make a strong statement.\" \n\n[00:31:00] \"The alternative to good philosophy is not no philosophy, it's bad philosophy. \"\n\nSHOW NOTES\n\n[00:03:12] How Dr. Gelman got interested in statistics\n\n[00:04:09] How much more hyped has statistical and machine learning become since you first broke into the field?\n\n[00:04:44] Where do you see the field of statistical machine learning headed in the next two to five years? \n\n[00:06:12] What do you think the biggest positive impact machine learning will have in society in the next two to five years?\n\n[00:07:24] What do you think would be some of our biggest concerns in the future?\n\n[00:09:07] The thee parts of Bayesian inference\n\n[00:12:05] What's the main difference between the frequentist and the Bayesian?\n\n[00:13:02] What is a workflow?\n\n[00:16:21] Iteratively building models\n\n[00:17:50] How does the Bayesian workflow differ from the frequent workflow?\n\n[00:18:32] Why is it that what makes this statistical method effective is not what it does with the data, but what data it uses?\n\n[00:20:48] Why do Bayesians then tend to be a little bit more skeptical in their thought processes?\n\n[00:21:47] Your method of evaluation can be inspired by the model or the model can be inspired by your method of evaluation\n\n[00:24:38] What is the usual story when it comes to statistics? And why don't you like it?\n\n[00:30:16] Why should statisticians and data scientist care about philosophy?\n\n[00:35:04] How can we solve all of our statistics problems using P values?\n\n[00:36:14] Is there a difference in interpretations for P-Values between Bayesian and frequentist.\n\n[00:36:54] Do you feel like the P value is a difficult concept for a lot of people to understand? And if so, why do you think it's a bit challenging?\n\n[00:38:22] Why the least important part of data science is statistics. \n\n[00:40:09] Why is it that Americans vote the way they do?\n\n[00:42:40] What's the one thing you want people to learn from your story?\n\n[00:44:48] The lightning roundSpecial Guest: Andrew Gelman, PhD.","content_html":"Andrew is an American statistician, professor of statistics and political science, and director of the Applied Statistics Center at Columbia University.
\n\nHe frequently writes about Bayesian statistics, displaying data, and interesting trends in social science.
\n\nHe’s also well known for writing posts sharing his thoughts on best statistical practices in the sciences, with a frequent emphasis on what he sees as the absurd and unscientific.
\n\nFIND ANDREW ONLINE
\n\nWebsite: https://statmodeling.stat.columbia.edu/
\n\nTwitter: https://twitter.com/StatModeling
\n\nQUOTES
\n\n[00:04:16] "We've already passed peak statistics..."
\n\n[00:05:13] "One thing that we sometimes like to say is that big data need big model because big data are available data. They're not designed experiments, they're not random samples. Often big data means these are measurements. "
\n\n[00:22:05] "If you design an experiment, you want to know what you're going to do later. So most obviously, you want your sample size to be large enough so that given the effect size that you expect to see, you'll get a strong enough signal that you can make a strong statement."
\n\n[00:31:00] "The alternative to good philosophy is not no philosophy, it's bad philosophy. "
\n\nSHOW NOTES
\n\n[00:03:12] How Dr. Gelman got interested in statistics
\n\n[00:04:09] How much more hyped has statistical and machine learning become since you first broke into the field?
\n\n[00:04:44] Where do you see the field of statistical machine learning headed in the next two to five years?
\n\n[00:06:12] What do you think the biggest positive impact machine learning will have in society in the next two to five years?
\n\n[00:07:24] What do you think would be some of our biggest concerns in the future?
\n\n[00:09:07] The thee parts of Bayesian inference
\n\n[00:12:05] What's the main difference between the frequentist and the Bayesian?
\n\n[00:13:02] What is a workflow?
\n\n[00:16:21] Iteratively building models
\n\n[00:17:50] How does the Bayesian workflow differ from the frequent workflow?
\n\n[00:18:32] Why is it that what makes this statistical method effective is not what it does with the data, but what data it uses?
\n\n[00:20:48] Why do Bayesians then tend to be a little bit more skeptical in their thought processes?
\n\n[00:21:47] Your method of evaluation can be inspired by the model or the model can be inspired by your method of evaluation
\n\n[00:24:38] What is the usual story when it comes to statistics? And why don't you like it?
\n\n[00:30:16] Why should statisticians and data scientist care about philosophy?
\n\n[00:35:04] How can we solve all of our statistics problems using P values?
\n\n[00:36:14] Is there a difference in interpretations for P-Values between Bayesian and frequentist.
\n\n[00:36:54] Do you feel like the P value is a difficult concept for a lot of people to understand? And if so, why do you think it's a bit challenging?
\n\n[00:38:22] Why the least important part of data science is statistics.
\n\n[00:40:09] Why is it that Americans vote the way they do?
\n\n[00:42:40] What's the one thing you want people to learn from your story?
\n\n[00:44:48] The lightning round
Special Guest: Andrew Gelman, PhD.
","summary":"","date_published":"2020-10-12T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/3feb0ded-8187-44a5-bfc6-9bb1303bf1f1.mp3","mime_type":"audio/mpeg","size_in_bytes":29995005,"duration_in_seconds":3421}]},{"id":"76276dc1-aedd-40ca-bf8d-3761aee7c05b","title":"Data Science Happy Hours 4, 09OCT2020","url":"https://theartistsofdatascience.fireside.fm/oh4","content_text":"This office hours was jam packed with some amazing insights from data scientists at all levels!\n\nCarlos Mercado stops by and brings some friends with him! \n\nWe help a community member with their workflow for a Kaggle project and discuss some best practices for working on a project.\n\nWe also talk about how a data scientists needs to have a duality mindset - they're the bridge between technical engineers and the research scientists in their organizations. \n\nWe also get to hear what it's like being a data scientist in a consulting organizations, and the challenges of working with clients who don't know what they want.\n\nSome awesome book recommendations in this episode as well!\n\nWe were ranked one of the top data science podcasts by FeedSpot! Check it out here: https://blog.feedspot.com/data_science_podcasts/\n\nYou can checkout the video on YouTube here: https://www.youtube.com/watch?v=dtrGkaqniyQ\n\nGet 70% off of Data Science Dream Job's registration fee: dsdj.co/artists70\n\nRegister for future office hours: bit.ly/adsoh\n\nJoin Data Science Dream Job for 70% off: http://dsdj.co/artists70Special Guest: Carlos Mercado.","content_html":"This office hours was jam packed with some amazing insights from data scientists at all levels!
\n\nCarlos Mercado stops by and brings some friends with him!
\n\nWe help a community member with their workflow for a Kaggle project and discuss some best practices for working on a project.
\n\nWe also talk about how a data scientists needs to have a duality mindset - they're the bridge between technical engineers and the research scientists in their organizations.
\n\nWe also get to hear what it's like being a data scientist in a consulting organizations, and the challenges of working with clients who don't know what they want.
\n\nSome awesome book recommendations in this episode as well!
\n\nWe were ranked one of the top data science podcasts by FeedSpot! Check it out here: https://blog.feedspot.com/data_science_podcasts/
\n\nYou can checkout the video on YouTube here: https://www.youtube.com/watch?v=dtrGkaqniyQ
\n\nGet 70% off of Data Science Dream Job's registration fee: dsdj.co/artists70
\n\nRegister for future office hours: bit.ly/adsoh
\n\nJoin Data Science Dream Job for 70% off: http://dsdj.co/artists70
Special Guest: Carlos Mercado.
","summary":"","date_published":"2020-10-11T15:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/76276dc1-aedd-40ca-bf8d-3761aee7c05b.mp3","mime_type":"audio/mpeg","size_in_bytes":31705498,"duration_in_seconds":3608}]},{"id":"06b53872-537e-43b2-ad74-0e896fdb2879","title":"An Introduction to Stoicism | Anderson Silver","url":"https://theartistsofdatascience.fireside.fm/anderson-silver","content_text":"Anderson Silver is a CPA who landed his dream job earning a six-figure salary, complete with high profile notoriety and accolade.\n\nLooking for a guide to life, he turned to philosophy and for the last five years he’s been practicing the philosophy of Stoicism, and it’s changed his life completely.\n\nQUOTES\n\n[00:11:40] \"The more the island of my knowledge grows, the more the shore of my ignorance grows.\"\n\n[00:17:06] \"Let's stop pretending that this fake structure we have actually means something. Which is ironic coming from the Stoics because, you know, some of the most famous Stoics were the richest man in all the lands. \"\n\n[00:25:42] \"Your intention, your decision for the action is in your control. But as soon as you start trying to do it, it's already out of your control.\"\n\n[00:32:04] \"There's no one right answer to what the purpose of life is. We all have our own unique purpose for life. And but the thing is, we never take the time to identify this, right?\"\n\nWHAT YOU'LL LEARN\n\n[00:07:26] The difference between stoic and Stoicism\n\n[00:09:40] Socrates, wisdom, and virtue\n\n[00:21:31] The key disciplines of Stoicism\n\n[00:26:28] Premeditation of adversity\n\n[00:56:03] The three reasons Anderson practices Stoicism\n\nFIND ANDERSON ONLINE\n\nTwitter: https://twitter.com/yourmanual\n\nPatreon: https://www.patreon.com/AndersonSilver\n\nPodcast: https://anchor.fm/AndersonSilver\n\nSHOW NOTES\n\n[00:01:37] Guest introduction\n\n[00:02:55] The journey to now\n\n[00:03:53] The path to philosophy\n\n[00:05:14] The search for a philosophy of life\n\n[00:07:26] The difference between stoic and Stoicism\n\n[00:09:40] Socrates, wisdom, and virtue\n\n[00:14:10] The difference between cynicism and Cynicism\n\n[00:16:35] Living in accordance with nature\n\n[00:19:16] The nature of the 21st century\n\n[00:21:31] The key disciplines of Stoicism\n\n[00:26:28] Premeditation of adversity\n\n[00:30:12] How can we make sure that we're not being busy for the wrong reasons?\n\n[00:33:18] Dealing with selfish impulses and distractions\n\n[00:36:34] The Stoic practice of journaling \n\n[00:37:44] Stoicism and job interviews\n\n[00:40:55] Stoicism and the art of being a sports fan\n\n[00:42:29] Owning up to insecurities at work\n\n[00:45:07] How to handle feeling overwhelmed at work\n\n[00:46:56] Love your destiny\n\n[00:48:49] What do to do if your boss is an asshole\n\n[00:50:10] How not to be angry at your coworkers\n\n[00:52:33] Throw away your books\n\n[00:56:03] The three reasons Anderson practices Stoicism\n\n[00:57:08] The Stoic parent\n\n[01:00:05] What do the Stoics have to say about worrying about what other people think about you?\n\n[01:02:27] What's the one thing you want people to learn from your story?\n\n[01:03:54] The lightning roundSpecial Guest: Anderson Silver.","content_html":"Anderson Silver is a CPA who landed his dream job earning a six-figure salary, complete with high profile notoriety and accolade.
\n\nLooking for a guide to life, he turned to philosophy and for the last five years he’s been practicing the philosophy of Stoicism, and it’s changed his life completely.
\n\nQUOTES
\n\n[00:11:40] "The more the island of my knowledge grows, the more the shore of my ignorance grows."
\n\n[00:17:06] "Let's stop pretending that this fake structure we have actually means something. Which is ironic coming from the Stoics because, you know, some of the most famous Stoics were the richest man in all the lands. "
\n\n[00:25:42] "Your intention, your decision for the action is in your control. But as soon as you start trying to do it, it's already out of your control."
\n\n[00:32:04] "There's no one right answer to what the purpose of life is. We all have our own unique purpose for life. And but the thing is, we never take the time to identify this, right?"
\n\nWHAT YOU'LL LEARN
\n\n[00:07:26] The difference between stoic and Stoicism
\n\n[00:09:40] Socrates, wisdom, and virtue
\n\n[00:21:31] The key disciplines of Stoicism
\n\n[00:26:28] Premeditation of adversity
\n\n[00:56:03] The three reasons Anderson practices Stoicism
\n\nFIND ANDERSON ONLINE
\n\nTwitter: https://twitter.com/yourmanual
\n\nPatreon: https://www.patreon.com/AndersonSilver
\n\nPodcast: https://anchor.fm/AndersonSilver
\n\nSHOW NOTES
\n\n[00:01:37] Guest introduction
\n\n[00:02:55] The journey to now
\n\n[00:03:53] The path to philosophy
\n\n[00:05:14] The search for a philosophy of life
\n\n[00:07:26] The difference between stoic and Stoicism
\n\n[00:09:40] Socrates, wisdom, and virtue
\n\n[00:14:10] The difference between cynicism and Cynicism
\n\n[00:16:35] Living in accordance with nature
\n\n[00:19:16] The nature of the 21st century
\n\n[00:21:31] The key disciplines of Stoicism
\n\n[00:26:28] Premeditation of adversity
\n\n[00:30:12] How can we make sure that we're not being busy for the wrong reasons?
\n\n[00:33:18] Dealing with selfish impulses and distractions
\n\n[00:36:34] The Stoic practice of journaling
\n\n[00:37:44] Stoicism and job interviews
\n\n[00:40:55] Stoicism and the art of being a sports fan
\n\n[00:42:29] Owning up to insecurities at work
\n\n[00:45:07] How to handle feeling overwhelmed at work
\n\n[00:46:56] Love your destiny
\n\n[00:48:49] What do to do if your boss is an asshole
\n\n[00:50:10] How not to be angry at your coworkers
\n\n[00:52:33] Throw away your books
\n\n[00:56:03] The three reasons Anderson practices Stoicism
\n\n[00:57:08] The Stoic parent
\n\n[01:00:05] What do the Stoics have to say about worrying about what other people think about you?
\n\n[01:02:27] What's the one thing you want people to learn from your story?
\n\n[01:03:54] The lightning round
Special Guest: Anderson Silver.
","summary":"","date_published":"2020-10-08T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/06b53872-537e-43b2-ad74-0e896fdb2879.mp3","mime_type":"audio/mpeg","size_in_bytes":39494119,"duration_in_seconds":4183}]},{"id":"84a882dd-2e4f-4dce-a37b-36d7552ec66a","title":"Build A Career in Data Science | Jacqueline Nolis and Emily Robinson","url":"https://theartistsofdatascience.fireside.fm/jacqueline-nolis-emily-robinson","content_text":"Jacqueline Nolis is currently a principal data scientist at Brightloom where she creates models to help restaurants and retailers improve the customer experience.\n\nEmily Robinson is currently a senior data scientist at Warby Parker, where she works on a centralized team tackling some of the company’s biggest projects. \n\nWHAT YOU'LL LEARN\n\n[00:10:42] The three types of data scientists \n\n[00:13:09] How to make an effective analysis\n\n[00:16:08] How to convert a business problem into a data science problem\n\n[00:19:39] What the heck is deploying a model into production mean anyways?\n\n[00:29:57] Who are the various types of stakeholders that we may encounter in our data science career and what do they care about?\n\n[00:37:56] How to build a data science practice as the first data scientist\n\nFIND JACQUELINE ONLINE\n\nWebsite: https://jnolis.com/\n\nLinkedIn: https://www.linkedin.com/in/jnolis/\n\nTwitter: https://twitter.com/skyetetra\n\nGitHub: https://github.com/jnolis\n\nFIND EMILY ONLINE\n\nWebsite: https://hookedondata.org/\n\nLinkedIn: https://www.linkedin.com/in/robinsones/\n\nGitHub: https://github.com/robinsones\n\nSHOW NOTES\n\n[00:01:46] Guest introduction\n\n[00:03:15] The path into data science\n\n[00:04:58] How they met\n\n[00:05:37] Challenges of working on a book online and across time zones\n\n[00:07:50] Silly frustrations while writing the book\n\n[00:10:42] The three types of data scientists \n\n[00:13:09] How to make an effective analysis\n\n[00:14:29] Good versus bad analysis\n\n[00:15:21] How are the types of analysis different for the different types of data scientists?\n\n[00:16:08] How to convert a business problem into a data science problem\n\n[00:18:15] What to think about before diving into data and coding\n\n[00:19:39] What the heck is deploying a model into production mean anyways?\n\n[00:22:05] An illustrative example of putting a model into production\n\n[00:23:50] How to keep a model running in production\n\n[00:25:17] At what point do we retrain the model?\n\n[00:28:36] How to handle interview questions about deploying a model to production\n\n[00:29:57] Who are the various types of stakeholders that we may encounter in our data science career and what do they care about?\n\n[00:31:49] Tailor your communication to your audience\n\n[00:33:10] How to decide which projects to take on at work\n\n[00:35:41] How to establish a data culture when you’re the first data scientist in an organization\n\n[00:37:56] How to build a data science practice as the first data scientist\n\n[00:41:11] Non-technical skills for success\n\n[00:43:34] Is data science an art or science?\n\n[00:46:43] The creative process in data science\n\n[00:48:26] Advice for women in data science\n\n[00:51:51] How to promote diversity and inclusion in data science\n\n[00:54:50] What's the one thing you want people to learn from your story?\n\n[00:57:47] The lightning roundSpecial Guests: Emily Robinson and Jacqueline Nolis.","content_html":"Jacqueline Nolis is currently a principal data scientist at Brightloom where she creates models to help restaurants and retailers improve the customer experience.
\n\nEmily Robinson is currently a senior data scientist at Warby Parker, where she works on a centralized team tackling some of the company’s biggest projects.
\n\nWHAT YOU'LL LEARN
\n\n[00:10:42] The three types of data scientists
\n\n[00:13:09] How to make an effective analysis
\n\n[00:16:08] How to convert a business problem into a data science problem
\n\n[00:19:39] What the heck is deploying a model into production mean anyways?
\n\n[00:29:57] Who are the various types of stakeholders that we may encounter in our data science career and what do they care about?
\n\n[00:37:56] How to build a data science practice as the first data scientist
\n\nFIND JACQUELINE ONLINE
\n\nWebsite: https://jnolis.com/
\n\nLinkedIn: https://www.linkedin.com/in/jnolis/
\n\nTwitter: https://twitter.com/skyetetra
\n\nGitHub: https://github.com/jnolis
\n\nFIND EMILY ONLINE
\n\nWebsite: https://hookedondata.org/
\n\nLinkedIn: https://www.linkedin.com/in/robinsones/
\n\nGitHub: https://github.com/robinsones
\n\nSHOW NOTES
\n\n[00:01:46] Guest introduction
\n\n[00:03:15] The path into data science
\n\n[00:04:58] How they met
\n\n[00:05:37] Challenges of working on a book online and across time zones
\n\n[00:07:50] Silly frustrations while writing the book
\n\n[00:10:42] The three types of data scientists
\n\n[00:13:09] How to make an effective analysis
\n\n[00:14:29] Good versus bad analysis
\n\n[00:15:21] How are the types of analysis different for the different types of data scientists?
\n\n[00:16:08] How to convert a business problem into a data science problem
\n\n[00:18:15] What to think about before diving into data and coding
\n\n[00:19:39] What the heck is deploying a model into production mean anyways?
\n\n[00:22:05] An illustrative example of putting a model into production
\n\n[00:23:50] How to keep a model running in production
\n\n[00:25:17] At what point do we retrain the model?
\n\n[00:28:36] How to handle interview questions about deploying a model to production
\n\n[00:29:57] Who are the various types of stakeholders that we may encounter in our data science career and what do they care about?
\n\n[00:31:49] Tailor your communication to your audience
\n\n[00:33:10] How to decide which projects to take on at work
\n\n[00:35:41] How to establish a data culture when you’re the first data scientist in an organization
\n\n[00:37:56] How to build a data science practice as the first data scientist
\n\n[00:41:11] Non-technical skills for success
\n\n[00:43:34] Is data science an art or science?
\n\n[00:46:43] The creative process in data science
\n\n[00:48:26] Advice for women in data science
\n\n[00:51:51] How to promote diversity and inclusion in data science
\n\n[00:54:50] What's the one thing you want people to learn from your story?
\n\n[00:57:47] The lightning round
Special Guests: Emily Robinson and Jacqueline Nolis.
","summary":"","date_published":"2020-10-05T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/84a882dd-2e4f-4dce-a37b-36d7552ec66a.mp3","mime_type":"audio/mpeg","size_in_bytes":43894663,"duration_in_seconds":4506}]},{"id":"6e9cc6e6-4715-417d-ad5c-ac45312812eb","title":"Data Science Happy Hours 3, 02OCT2020","url":"https://theartistsofdatascience.fireside.fm/oh3","content_text":"Carlos swings by the office hours again and we have an excellent discussion with a community member on why it's important to not compare yourself to what other data scientists know and don't know.\n\nThis is a great session to listen to for anyone who may be feeling a bit of imposter syndrome, or maybe feeling like that don't have value to contribute.\n\nWe also talk about the perils of working out of a notebook, and how to move beyond them.\n\nCheckout the recording on YouTube: https://www.youtube.com/watch?v=m_dxtAIKbZc\n\nCheck out the live session I did with Kate Strachnyi: https://www.youtube.com/watch?v=soyWLCsAEuY\n\nThe Artists of Data Science was named #8 on the Top 15 Data Science podcasts by FeedSpot!\nhttps://blog.feedspot.com/data_science_podcasts/\n\nSome items we talked about in this episode:\n\n[16:42] Carlos Mercado tells Haseeb about a quote has made his life in meetings of 10 people much easier: \n\n\"Sometimes the most basic obvious thoughts are just coincidentally not in anyone else's head at the time. \n\n'What's obvious to you is amazing to others'\"\n\n[16:43] Carlos Mercado : \"Also most data science problems are shockingly not complex\"\n\n[16:45] Harpreet Sahota talks about an episode with Brandon Quach that addresses the question that Haseeb has. Check it out here: https://theartistsofdatascience.fireside.fm/brandon-quach\n\n[16:59] Community member Haseeb shares an awesome video he made on the perils of working in a notebook: https://youtu.be/kBnCOOrSh1U\n\n[17:11] I talk about the future of The Artists of Data Science and what my vision for it is\n\n[17:12] Carlos shares his ideas for a couple of cool things that The Artists of Data Science could do. \n\n[17:22] Haseeb shares a data science meet-up group \n\nJoin Data Science Dream Job for 70% off: http://dsdj.co/artists70\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsohSpecial Guest: Carlos Mercado.","content_html":"Carlos swings by the office hours again and we have an excellent discussion with a community member on why it's important to not compare yourself to what other data scientists know and don't know.
\n\nThis is a great session to listen to for anyone who may be feeling a bit of imposter syndrome, or maybe feeling like that don't have value to contribute.
\n\nWe also talk about the perils of working out of a notebook, and how to move beyond them.
\n\nCheckout the recording on YouTube: https://www.youtube.com/watch?v=m_dxtAIKbZc
\n\nCheck out the live session I did with Kate Strachnyi: https://www.youtube.com/watch?v=soyWLCsAEuY
\n\nThe Artists of Data Science was named #8 on the Top 15 Data Science podcasts by FeedSpot!
\nhttps://blog.feedspot.com/data_science_podcasts/
Some items we talked about in this episode:
\n\n[16:42] Carlos Mercado tells Haseeb about a quote has made his life in meetings of 10 people much easier:
\n\n"Sometimes the most basic obvious thoughts are just coincidentally not in anyone else's head at the time.
\n\n'What's obvious to you is amazing to others'"
\n\n[16:43] Carlos Mercado : "Also most data science problems are shockingly not complex"
\n\n[16:45] Harpreet Sahota talks about an episode with Brandon Quach that addresses the question that Haseeb has. Check it out here: https://theartistsofdatascience.fireside.fm/brandon-quach
\n\n[16:59] Community member Haseeb shares an awesome video he made on the perils of working in a notebook: https://youtu.be/kBnCOOrSh1U
\n\n[17:11] I talk about the future of The Artists of Data Science and what my vision for it is
\n\n[17:12] Carlos shares his ideas for a couple of cool things that The Artists of Data Science could do.
\n\n[17:22] Haseeb shares a data science meet-up group
\n\nJoin Data Science Dream Job for 70% off: http://dsdj.co/artists70
\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh
Special Guest: Carlos Mercado.
","summary":"","date_published":"2020-10-04T13:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/6e9cc6e6-4715-417d-ad5c-ac45312812eb.mp3","mime_type":"audio/mpeg","size_in_bytes":33388671,"duration_in_seconds":3731}]},{"id":"300f7e44-7ce2-4bf7-87fe-ff7c341899be","title":"The Data Girl | Ashley M. Scott","url":"https://theartistsofdatascience.fireside.fm/ashley-m-scott","content_text":"Ashley's a Data Analyst collaborating with administrators and medical professionals to develop impactful analysis utilizing data mining, visualizations, and modeling to drive business solutions. \n\nShe’s a passionate advocate for educating women regarding data career opportunities and spreading awareness about the advancement of women in the tech industry.\n\nAnd she's a Forbes Under 30 Scholar!\n\nWHAT YOU'LL LEARN\n\n[00:05:32] The importance of cultivating the right mindset\n\n[00:17:04] Privacy, biometrics, and data\n\n[00:25:03] How to become a Forbes under-30 scholar\n\n[00:27:09] The unique experiences of a health care data analyst\n\n[00:30:54] Bridinging the patient satisfaction gap with data\n\n[00:43:57] Emotional intelligence in data science\n\nFIND ASHLEY ONLINE\n\nLinkedIn: https://www.linkedin.com/in/ashleym-scott/\n\nInstagram: https://www.instagram.com/datagirlash/\n\nTwitter: https://twitter.com/datagirlash\n\nSHOW NOTES\n\n[00:01:35] Introduction for our guest today\n\n[00:02:49] The journey into analytics\n\n[00:07:50] The data hype cycle\n\n[00:11:09] How do you see data analytics impacting the health care industry in the next two to five years?\n\n[00:17:04] Privacy, biometrics, and data\n\n[00:21:24] What do you think will separate the great Data scientists from the merely good ones?\n\n[00:25:03] How to become a Forbes under-30 scholar \n\n[00:27:09] The unique experiences of a health care data analyst\n\n[00:30:54] How is Data bridging the gap between medical education and patient satisfaction?\n\n[00:32:51] Health care data analyst project ideas\n\n[00:39:08] How to decide your data science career path\n\n[00:43:57] Emotional intelligence in data science\n\n[00:48:01] What are some common mistakes that you see people make when visualizing their data?\n\n[00:50:37] Communicating with non-technical audience\n\n[00:54:17] Openly communicate with your teammates\n\n[00:56:26] Being a woman in data science\n\n[00:59:12] The Women in Data Science organization\n\n[01:05:26] Fostering inclusion of women in data science\n\n[01:07:45] What's the one thing you want people to learn from your story?\n\n[01:09:00] The lightning roundSpecial Guest: Ashley M. Scott.","content_html":"Ashley's a Data Analyst collaborating with administrators and medical professionals to develop impactful analysis utilizing data mining, visualizations, and modeling to drive business solutions.
\n\nShe’s a passionate advocate for educating women regarding data career opportunities and spreading awareness about the advancement of women in the tech industry.
\n\nAnd she's a Forbes Under 30 Scholar!
\n\nWHAT YOU'LL LEARN
\n\n[00:05:32] The importance of cultivating the right mindset
\n\n[00:17:04] Privacy, biometrics, and data
\n\n[00:25:03] How to become a Forbes under-30 scholar
\n\n[00:27:09] The unique experiences of a health care data analyst
\n\n[00:30:54] Bridinging the patient satisfaction gap with data
\n\n[00:43:57] Emotional intelligence in data science
\n\nFIND ASHLEY ONLINE
\n\nLinkedIn: https://www.linkedin.com/in/ashleym-scott/
\n\nInstagram: https://www.instagram.com/datagirlash/
\n\nTwitter: https://twitter.com/datagirlash
\n\nSHOW NOTES
\n\n[00:01:35] Introduction for our guest today
\n\n[00:02:49] The journey into analytics
\n\n[00:07:50] The data hype cycle
\n\n[00:11:09] How do you see data analytics impacting the health care industry in the next two to five years?
\n\n[00:17:04] Privacy, biometrics, and data
\n\n[00:21:24] What do you think will separate the great Data scientists from the merely good ones?
\n\n[00:25:03] How to become a Forbes under-30 scholar
\n\n[00:27:09] The unique experiences of a health care data analyst
\n\n[00:30:54] How is Data bridging the gap between medical education and patient satisfaction?
\n\n[00:32:51] Health care data analyst project ideas
\n\n[00:39:08] How to decide your data science career path
\n\n[00:43:57] Emotional intelligence in data science
\n\n[00:48:01] What are some common mistakes that you see people make when visualizing their data?
\n\n[00:50:37] Communicating with non-technical audience
\n\n[00:54:17] Openly communicate with your teammates
\n\n[00:56:26] Being a woman in data science
\n\n[00:59:12] The Women in Data Science organization
\n\n[01:05:26] Fostering inclusion of women in data science
\n\n[01:07:45] What's the one thing you want people to learn from your story?
\n\n[01:09:00] The lightning round
Special Guest: Ashley M. Scott.
","summary":"","date_published":"2020-10-01T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/300f7e44-7ce2-4bf7-87fe-ff7c341899be.mp3","mime_type":"audio/mpeg","size_in_bytes":46735634,"duration_in_seconds":4910}]},{"id":"7c948c13-49a8-48f1-9568-ba074972acef","title":"The Philosopher of Data Science | Giuseppe Bonaccorso","url":"https://theartistsofdatascience.fireside.fm/giuseppe-bonaccorso","content_text":"Giuseppe Bonaccorso is an experienced and goal-oriented leader with wide expertise in the management of Artificial Intelligence, Machine Learning, Deep Learning, and Data Science. His experience spans projects for a wide variety of industries including: healthcare, B2C and Military industries, and Fortune 500 firms.\n\nHis main interests include machine/deep learning, data science strategy, and digital innovation in the healthcare industry. \n\nYou may recognize him from the many best-selling machine learning books he’s published including: Python: Advanced Guide to Artificial Intelligence, Fundamentals of Machine Learning with scikit-learn, and Hands-On Unsupervised Learning with Python.\n\nWHAT YOU'LL LEARN\n\n[00:13:01] The need for creating a culture of data science\n\n[00:16:08] Why you need to be more than a nerd\n\n[00:27:06] Heuristics for scaling data\n\n[00:35:50] How to cross-validate\n\n[00:43:53] Feature engineering techniques\n\n[00:46:50] A lesson on tuning hyperparameters\n\n[00:51:33] A lesson on using regularization\n\n[00:58:01] What to do after model deployment\n\nQUOTES:\n\n[00:10:29] \"Data science is not something that can be learned in a week or even in a month. It's a real topic with a lot of theory behind. And it's very important for the practitioners to have clear ideas about what they do.\"\n\n[00:22:45] \"Another very important thing when defining a model is that our goal is not necessarily to describe what we already know, but to make predictions. So our model must become a sort of container of future possibilities. \"\n\n[01:06:14] \"Data science is a science for sure. There is mathematics behind and we never we should never forget this. But I consider also mathematics and mix of science and art.\"\n\n[01:09:48] \"The only way you can really expand yourself is to be curious, to learn the new processes, to learn how other people work, to talk to other people, to understand how your business work.\" \n\nFIND GIUSEPPTE ONLINE:\n\nWebsite: https://www.bonaccorso.eu/\n\nLinkedIn: https://www.linkedin.com/in/giuseppebonaccorso/\n\nTwitter: https://twitter.com/GiuseppeB\n\nSHOW NOTES:\n\n[00:01:44] Introduction for our guest\n\n[00:03:06] How Giuseppe got into data science\n\n[00:04:37] The hype around data science\n\n[00:06:10] Machine learning in the future\n\n[00:07:33] The biggest positive impact data science will have in the near future\n\n[00:10:13] How to minimize the negative impacts of data science\n\n[00:13:39] Healthy vs unhealthy data science culture\n\n[00:17:45] Good vs great data scientists\n\n[00:21:50] What's artists I would love to hear from you.\n\n[00:22:33] What is a model and why do we build them in the first place?\n\n[00:27:06] Heuristics for scaling data\n\n[00:35:50] With so many methods of cross-validation out there, how can we know which one to utilize for any given scenario?\n\n[00:43:43] How we can be more thoughtful with our feature engineering feature?\n\n[00:46:50] Tips on tuning hyperparameters\n\n[00:51:33] A lesson on using regularization\n\n[00:58:01] What to do after deployment\n\n[01:01:24] The data generating process\n\n[01:04:00] Keywords you need to search to learn more about different parts of the machine learning pipeline\n\n[01:06:01] Do you consider Data science and machine learning to be an art or purely a hard science?\n\n[01:07:21] Creativity and curiosity\n\n[01:10:38] How could Data scientists develop their business acumen and cultivate a product sense?\n\n[01:13:50] Advice for people breaking into the field\n\n[01:17:19] What’s the one thing you want people to learn from your story?\n\n[01:19:08] The lightning roundSpecial Guest: Giuseppe Bonaccorso.","content_html":"Giuseppe Bonaccorso is an experienced and goal-oriented leader with wide expertise in the management of Artificial Intelligence, Machine Learning, Deep Learning, and Data Science. His experience spans projects for a wide variety of industries including: healthcare, B2C and Military industries, and Fortune 500 firms.
\n\nHis main interests include machine/deep learning, data science strategy, and digital innovation in the healthcare industry.
\n\nYou may recognize him from the many best-selling machine learning books he’s published including: Python: Advanced Guide to Artificial Intelligence, Fundamentals of Machine Learning with scikit-learn, and Hands-On Unsupervised Learning with Python.
\n\nWHAT YOU'LL LEARN
\n\n[00:13:01] The need for creating a culture of data science
\n\n[00:16:08] Why you need to be more than a nerd
\n\n[00:27:06] Heuristics for scaling data
\n\n[00:35:50] How to cross-validate
\n\n[00:43:53] Feature engineering techniques
\n\n[00:46:50] A lesson on tuning hyperparameters
\n\n[00:51:33] A lesson on using regularization
\n\n[00:58:01] What to do after model deployment
\n\nQUOTES:
\n\n[00:10:29] "Data science is not something that can be learned in a week or even in a month. It's a real topic with a lot of theory behind. And it's very important for the practitioners to have clear ideas about what they do."
\n\n[00:22:45] "Another very important thing when defining a model is that our goal is not necessarily to describe what we already know, but to make predictions. So our model must become a sort of container of future possibilities. "
\n\n[01:06:14] "Data science is a science for sure. There is mathematics behind and we never we should never forget this. But I consider also mathematics and mix of science and art."
\n\n[01:09:48] "The only way you can really expand yourself is to be curious, to learn the new processes, to learn how other people work, to talk to other people, to understand how your business work."
\n\nFIND GIUSEPPTE ONLINE:
\n\nWebsite: https://www.bonaccorso.eu/
\n\nLinkedIn: https://www.linkedin.com/in/giuseppebonaccorso/
\n\nTwitter: https://twitter.com/GiuseppeB
\n\nSHOW NOTES:
\n\n[00:01:44] Introduction for our guest
\n\n[00:03:06] How Giuseppe got into data science
\n\n[00:04:37] The hype around data science
\n\n[00:06:10] Machine learning in the future
\n\n[00:07:33] The biggest positive impact data science will have in the near future
\n\n[00:10:13] How to minimize the negative impacts of data science
\n\n[00:13:39] Healthy vs unhealthy data science culture
\n\n[00:17:45] Good vs great data scientists
\n\n[00:21:50] What's artists I would love to hear from you.
\n\n[00:22:33] What is a model and why do we build them in the first place?
\n\n[00:27:06] Heuristics for scaling data
\n\n[00:35:50] With so many methods of cross-validation out there, how can we know which one to utilize for any given scenario?
\n\n[00:43:43] How we can be more thoughtful with our feature engineering feature?
\n\n[00:46:50] Tips on tuning hyperparameters
\n\n[00:51:33] A lesson on using regularization
\n\n[00:58:01] What to do after deployment
\n\n[01:01:24] The data generating process
\n\n[01:04:00] Keywords you need to search to learn more about different parts of the machine learning pipeline
\n\n[01:06:01] Do you consider Data science and machine learning to be an art or purely a hard science?
\n\n[01:07:21] Creativity and curiosity
\n\n[01:10:38] How could Data scientists develop their business acumen and cultivate a product sense?
\n\n[01:13:50] Advice for people breaking into the field
\n\n[01:17:19] What’s the one thing you want people to learn from your story?
\n\n[01:19:08] The lightning round
Special Guest: Giuseppe Bonaccorso.
","summary":"","date_published":"2020-09-28T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/7c948c13-49a8-48f1-9568-ba074972acef.mp3","mime_type":"audio/mpeg","size_in_bytes":54141447,"duration_in_seconds":5311}]},{"id":"23db6c15-b233-414a-abd3-17096eb0ab5a","title":"Data Science Happy Hours 2, 25SEP2020","url":"https://theartistsofdatascience.fireside.fm/oh2","content_text":"The recording from open office hours! Carlos Mercado swings by the show and he talks about the work he does with the government, talks about health care data scientists, and discusses some project ideas.\n\nWe also have a Q&A session with communit members, talk about blockchain, and more!\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh\n\nCheckout the recording on YouTube: https://bit.ly/3jc2qcf\n\nJoin Data Science Dream Job for 70% off: http://dsdj.co/artists70Special Guest: Carlos Mercado.","content_html":"The recording from open office hours! Carlos Mercado swings by the show and he talks about the work he does with the government, talks about health care data scientists, and discusses some project ideas.
\n\nWe also have a Q&A session with communit members, talk about blockchain, and more!
\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh
\n\nCheckout the recording on YouTube: https://bit.ly/3jc2qcf
\n\nJoin Data Science Dream Job for 70% off: http://dsdj.co/artists70
Special Guest: Carlos Mercado.
","summary":"","date_published":"2020-09-27T16:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/23db6c15-b233-414a-abd3-17096eb0ab5a.mp3","mime_type":"audio/mpeg","size_in_bytes":32430978,"duration_in_seconds":3728}]},{"id":"17aaee00-bd3b-4403-a1e0-0d3ed32c9071","title":"Work Less and Get More Done | Alex Pang","url":"https://theartistsofdatascience.fireside.fm/alex-pang-phd","content_text":"Dr. Alex Pang studies people, technologies, and the worlds they make. Since 2000 he's worked as a technology forecaster and futurist, helping companies understand new technologies and global trends, and their strategic and business implications. \n\nQUOTES\n\n\"The challenge is figuring out how it's going to play out in different industries or different parts of the world, thinking about how we can control and shape those technologies and their users so that they give us more flexibility, more autonomy, more freedom, as opposed to just eliminating our jobs or doing other bad things.\" ] [00:09:11]\n\n\"I think that the this is we live in a world that doesn't take work seriously, but we also live in a world that provides us with all the tools necessary to figure out how to harness rest and bring it back on our lives and use it as something that makes our lives better and makes our work better. \" [00:16:53]\n\n\"One of the other things, though, is that Ericsson found was that not only the top performers practiced differently, they also rested differently. They actually slept more than average performers...\" [00:19:52] \n\n\"And why that's important is that our creative minds seem to do better when - with these routines. Stephen King has this line about how the muse will descend if it knows that you're working.\" [00:34:18]\n\n\"Basically, intensive periods of focused work be periods of long semi distracted work. Knowledge work is a little bit more like high intensity interval training than like running a marathon. It turns out that intense-ivity turns turns out to be a better route to higher performance and better results than the long, long grind. [00:50:41]\" [00:50:16]\n\nFIND ALEX ONLINE: \n\nLinkedIn: https://www.linkedin.com/in/askpang/\n\nTwitter: https://twitter.com/askpang\n\nSHOW NOTES\n\n[00:02:16] Introduction for our guest\n\n[00:03:16] How Alex got so interested in the role of rest in creative lives\n\n[00:06:36] Where do you see technology headed in the next two to five years?\n\n[00:09:32] Society’s biggest concern with technology in the next 2-5 years.\n\n[00:12:03] What can we do now and perhaps going into the future to mitigate our distraction from technology\n\n[00:14:41] What is rest and what's the problem with it?\n\n[00:17:13] The problem with the “hustle culture”\n\n[00:18:54] Deliberate practice, deliberate rest\n\n[00:20:42] Why is it that rest is important for those of us who don't use our bodies or tactile kind of appendages, we use our brains?\n\n[00:23:02] The default mode network of the brain\n\n[00:27:51] How can we convince our boss that all we need is a solid four hours?\n\n[00:29:11] What are some horrible ways that people are resting and we should probably stop resting that way? \n\n[00:33:42] How does having a daily routine help us be more creative? How does that help us be more productive?\n\n[00:36:44] I talk about my struggles with my morning routine\n\n[00:37:50] What is the design thinking framework?\n\n[00:42:54] How can this framework then help us work better, smarter and less?\n\n[00:49:02] How to work more effectively as a knowledge worker\n\n[00:50:42] Flex time is not really that great\n\n[00:52:44] What's the one thing you want people to learn from your story.\n\n[00:53:36] Lightning round. What is your favorite way to rest?\n\n[00:53:46] If you could put up a billboard anywhere in the world, what would it say and why?\n\n[00:54:00] What something you believe that other people think is crazy.\n\n[00:54:50] What would you say is the most bizarre aspect or quality of the human mind that you've come across?\n\n[00:56:04] An interesting topic you should study\n\n[00:56:25] What's the number one book you'd recommend our audience read and your most impactful takeaway from it?\n\n[00:57:12] If you could somehow get a magic telephone that allows you to to contact 20 year old Alex, what would you tell him?\n\n[00:58:20] What does creativity have to do with being a good scientist?\n\n[00:59:34] What song do you have on repeat right now?\n\n[01:01:02] What's the best advice you've ever received?\n\n[01:01:48] Where to find Alex onlineSpecial Guest: Alex Pang, PhD.","content_html":"Dr. Alex Pang studies people, technologies, and the worlds they make. Since 2000 he's worked as a technology forecaster and futurist, helping companies understand new technologies and global trends, and their strategic and business implications.
\n\nQUOTES
\n\n"The challenge is figuring out how it's going to play out in different industries or different parts of the world, thinking about how we can control and shape those technologies and their users so that they give us more flexibility, more autonomy, more freedom, as opposed to just eliminating our jobs or doing other bad things." ] [00:09:11]
\n\n"I think that the this is we live in a world that doesn't take work seriously, but we also live in a world that provides us with all the tools necessary to figure out how to harness rest and bring it back on our lives and use it as something that makes our lives better and makes our work better. " [00:16:53]
\n\n"One of the other things, though, is that Ericsson found was that not only the top performers practiced differently, they also rested differently. They actually slept more than average performers..." [00:19:52]
\n\n"And why that's important is that our creative minds seem to do better when - with these routines. Stephen King has this line about how the muse will descend if it knows that you're working." [00:34:18]
\n\n"Basically, intensive periods of focused work be periods of long semi distracted work. Knowledge work is a little bit more like high intensity interval training than like running a marathon. It turns out that intense-ivity turns turns out to be a better route to higher performance and better results than the long, long grind. [00:50:41]" [00:50:16]
\n\nFIND ALEX ONLINE:
\n\nLinkedIn: https://www.linkedin.com/in/askpang/
\n\nTwitter: https://twitter.com/askpang
\n\nSHOW NOTES
\n\n[00:02:16] Introduction for our guest
\n\n[00:03:16] How Alex got so interested in the role of rest in creative lives
\n\n[00:06:36] Where do you see technology headed in the next two to five years?
\n\n[00:09:32] Society’s biggest concern with technology in the next 2-5 years.
\n\n[00:12:03] What can we do now and perhaps going into the future to mitigate our distraction from technology
\n\n[00:14:41] What is rest and what's the problem with it?
\n\n[00:17:13] The problem with the “hustle culture”
\n\n[00:18:54] Deliberate practice, deliberate rest
\n\n[00:20:42] Why is it that rest is important for those of us who don't use our bodies or tactile kind of appendages, we use our brains?
\n\n[00:23:02] The default mode network of the brain
\n\n[00:27:51] How can we convince our boss that all we need is a solid four hours?
\n\n[00:29:11] What are some horrible ways that people are resting and we should probably stop resting that way?
\n\n[00:33:42] How does having a daily routine help us be more creative? How does that help us be more productive?
\n\n[00:36:44] I talk about my struggles with my morning routine
\n\n[00:37:50] What is the design thinking framework?
\n\n[00:42:54] How can this framework then help us work better, smarter and less?
\n\n[00:49:02] How to work more effectively as a knowledge worker
\n\n[00:50:42] Flex time is not really that great
\n\n[00:52:44] What's the one thing you want people to learn from your story.
\n\n[00:53:36] Lightning round. What is your favorite way to rest?
\n\n[00:53:46] If you could put up a billboard anywhere in the world, what would it say and why?
\n\n[00:54:00] What something you believe that other people think is crazy.
\n\n[00:54:50] What would you say is the most bizarre aspect or quality of the human mind that you've come across?
\n\n[00:56:04] An interesting topic you should study
\n\n[00:56:25] What's the number one book you'd recommend our audience read and your most impactful takeaway from it?
\n\n[00:57:12] If you could somehow get a magic telephone that allows you to to contact 20 year old Alex, what would you tell him?
\n\n[00:58:20] What does creativity have to do with being a good scientist?
\n\n[00:59:34] What song do you have on repeat right now?
\n\n[01:01:02] What's the best advice you've ever received?
\n\n[01:01:48] Where to find Alex online
Special Guest: Alex Pang, PhD.
","summary":"","date_published":"2020-09-24T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/17aaee00-bd3b-4403-a1e0-0d3ed32c9071.mp3","mime_type":"audio/mpeg","size_in_bytes":36999991,"duration_in_seconds":3791}]},{"id":"a540778c-68c7-43ea-8135-46e3c0f914e2","title":"Emotional Intelligence for Data Scientists | Gilbert Eijkelenboom","url":"https://theartistsofdatascience.fireside.fm/gilbert-eijkelenboom","content_text":"On this episode of The Artists of Data Science, we get a chance to hear from Gilbert Eijkelenboom, an author and behavioral economist that is passionate about bridging the gap between analytical thinkers and emotional intelligence. His passion for psychology and numbers has led him to understand the need for analytical minds to become better at communication with people. \n\nHe gives insight into how your brain works, his methods for getting great feedback, and the importance of emotional intelligence.\n\nGilbert talks about his background as a poker player, and how human behavior impacts the success that is possible in data science and beyond. This episode brings an interesting and very important perspective into soft skills and actionable tips to implement into your daily workstream.\n\nWHAT YOU'LL LEARN\n\n[12:14] How your brain works and influences daily decisions \n\n[18:58] The importance of saying no\n\n[21:38] What is emotional intelligence and how it impacts your personal and professional life\n\n[37:26] How to identify your bright spot\n\n[44:21] The three step process to change your algorithms\n\n[46:58] Gilbert’s take on intrapreneurship\n\nQUOTES\n\n[21:38] “...to become a really good data scientist, you need to understand the business problem...and without emotional intelligence, it's going to be very difficult.”\n\n[24:53] “...if you don't try it yourself and fail and learn and experiment, then you're never going to be good…”\n\n[53:12] “every day you make small decisions that all combine to really big growth”\n\nFIND GILBERT ONLINE:\n\nWebsite: https://www.mindspeaking.com/\n\nQuora: https://www.quora.com/profile/Gilbert-Eijkelenboom\n\nLinkedIn: https://www.linkedin.com/in/eijkelenboom/\n\nSHOW NOTES\n\n[00:01:35] Introduction for our guest\n\n[00:02:45] How Gilbert went from poker pro to data dude\n\n[00:04:48] Where do you see the field of analytics and data science headed in the next two to five years?\n\n[00:06:09] The difference between good and great data scientists\n\n[00:06:55] Data science and behavioral economics \n\n[00:08:44] How we can see our brain as a set of algorithms with an input process and output?\n\n[00:12:01] The two systems in the brain\n\n[00:15:40] How to cope with rejection in our job search\n\n[00:18:36] The importance of saying no\n\n[00:21:03] What is emotional intelligence\n\n[00:21:31] The importance of emotional intelligence in our personal and professional lives\n\n[00:23:52] Why emotional intelligence is so important and the challenges of acquiring this skill\n\n[00:26:22] Tips on what we could do to start developing better emotional intelligence\n\n[00:28:16] How to ask for feedback \n\n[00:33:07] We talk about our shared love of Steven Pressfield\n\n[00:35:43] Emotional intelligence in the virtual world.\n\n[00:37:14] How we can identify our \"bright spots\"\n\n[00:39:00] How to cultivate better self-awareness\n\n[00:41:15] How we create a better awareness of the algorithms in our head\n\n[00:44:02] A three-step process for changing the negative algorithms in our heads\n\n[00:46:34] What it means to be an intrapreneur \n\n[00:49:24] What's the one thing you want people to learn from your story?\n\n[00:50:54] Why Gilbert wants to impact 100,000 people\n\n[00:51:57] The Lightning RoundSpecial Guest: Gilbert Eijkelenboom.","content_html":"On this episode of The Artists of Data Science, we get a chance to hear from Gilbert Eijkelenboom, an author and behavioral economist that is passionate about bridging the gap between analytical thinkers and emotional intelligence. His passion for psychology and numbers has led him to understand the need for analytical minds to become better at communication with people.
\n\nHe gives insight into how your brain works, his methods for getting great feedback, and the importance of emotional intelligence.
\n\nGilbert talks about his background as a poker player, and how human behavior impacts the success that is possible in data science and beyond. This episode brings an interesting and very important perspective into soft skills and actionable tips to implement into your daily workstream.
\n\nWHAT YOU'LL LEARN
\n\n[12:14] How your brain works and influences daily decisions
\n\n[18:58] The importance of saying no
\n\n[21:38] What is emotional intelligence and how it impacts your personal and professional life
\n\n[37:26] How to identify your bright spot
\n\n[44:21] The three step process to change your algorithms
\n\n[46:58] Gilbert’s take on intrapreneurship
\n\nQUOTES
\n\n[21:38] “...to become a really good data scientist, you need to understand the business problem...and without emotional intelligence, it's going to be very difficult.”
\n\n[24:53] “...if you don't try it yourself and fail and learn and experiment, then you're never going to be good…”
\n\n[53:12] “every day you make small decisions that all combine to really big growth”
\n\nFIND GILBERT ONLINE:
\n\nWebsite: https://www.mindspeaking.com/
\n\nQuora: https://www.quora.com/profile/Gilbert-Eijkelenboom
\n\nLinkedIn: https://www.linkedin.com/in/eijkelenboom/
\n\nSHOW NOTES
\n\n[00:01:35] Introduction for our guest
\n\n[00:02:45] How Gilbert went from poker pro to data dude
\n\n[00:04:48] Where do you see the field of analytics and data science headed in the next two to five years?
\n\n[00:06:09] The difference between good and great data scientists
\n\n[00:06:55] Data science and behavioral economics
\n\n[00:08:44] How we can see our brain as a set of algorithms with an input process and output?
\n\n[00:12:01] The two systems in the brain
\n\n[00:15:40] How to cope with rejection in our job search
\n\n[00:18:36] The importance of saying no
\n\n[00:21:03] What is emotional intelligence
\n\n[00:21:31] The importance of emotional intelligence in our personal and professional lives
\n\n[00:23:52] Why emotional intelligence is so important and the challenges of acquiring this skill
\n\n[00:26:22] Tips on what we could do to start developing better emotional intelligence
\n\n[00:28:16] How to ask for feedback
\n\n[00:33:07] We talk about our shared love of Steven Pressfield
\n\n[00:35:43] Emotional intelligence in the virtual world.
\n\n[00:37:14] How we can identify our "bright spots"
\n\n[00:39:00] How to cultivate better self-awareness
\n\n[00:41:15] How we create a better awareness of the algorithms in our head
\n\n[00:44:02] A three-step process for changing the negative algorithms in our heads
\n\n[00:46:34] What it means to be an intrapreneur
\n\n[00:49:24] What's the one thing you want people to learn from your story?
\n\n[00:50:54] Why Gilbert wants to impact 100,000 people
\n\n[00:51:57] The Lightning Round
Special Guest: Gilbert Eijkelenboom.
","summary":"In this episode we talk emotional intelligence and the algorithms in our mind with Gilbert Eijkelenboom!","date_published":"2020-09-21T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/a540778c-68c7-43ea-8135-46e3c0f914e2.mp3","mime_type":"audio/mpeg","size_in_bytes":31581264,"duration_in_seconds":3572}]},{"id":"c15fda33-e2ff-40d4-81a5-4fce2ee5f2d7","title":"Data Science Happy Hours 1, 18SEP2020","url":"https://theartistsofdatascience.fireside.fm/oh1","content_text":"Check YouTube for the video recording of this weeks call! https://www.youtube.com/watch?v=BjNbfp0s_Z4\n\n[00:21] What is a mentor?\n\n[00:02:52] How to network\n\n[00:08:32] Navigate the data science job market\n\n[00:10:36] Should I specialize in NLP or Computer Vision?\n\n[00:23:12] How to prepare for job interviews and ask for feedback\n\n[00:34:04] The different types of data science jobs\n\n[00:42:27] How to find case studies online\n\n[00:46:03] Somebody asked a forbidden question\n\n[00:48:09] Preparing for a in-person interview\n\nJoin Data Science Dream Job for 70% off: http://dsdj.co/artists70\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh","content_html":"Check YouTube for the video recording of this weeks call! https://www.youtube.com/watch?v=BjNbfp0s_Z4
\n\n[00:21] What is a mentor?
\n\n[00:02:52] How to network
\n\n[00:08:32] Navigate the data science job market
\n\n[00:10:36] Should I specialize in NLP or Computer Vision?
\n\n[00:23:12] How to prepare for job interviews and ask for feedback
\n\n[00:34:04] The different types of data science jobs
\n\n[00:42:27] How to find case studies online
\n\n[00:46:03] Somebody asked a forbidden question
\n\n[00:48:09] Preparing for a in-person interview
\n\nJoin Data Science Dream Job for 70% off: http://dsdj.co/artists70
\n\nCheck it out and don't forget to register for future office hours: http://bit.ly/adsoh
","summary":"Open office hours! Raw, unedited audio!\r\n\r\n","date_published":"2020-09-20T14:30:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/c15fda33-e2ff-40d4-81a5-4fce2ee5f2d7.mp3","mime_type":"audio/mpeg","size_in_bytes":32896713,"duration_in_seconds":3593}]},{"id":"ce09918a-2c64-48ed-aec2-44995815363c","title":"Become an Ultralearner | Scott H. Young","url":"https://theartistsofdatascience.fireside.fm/scott-h-young","content_text":"Scott H Young a writer, programmer, traveler and avid reader of interesting things. For the last ten years he's been experimenting to find out how to learn and think better. Today he swings by the show and talks to us about how we can master hard skills faster!\n\nQUOTES\n\"What you need is not just motivation, but you need some kind of system. You need some way to channel that initial burst of enthusiasm into creating structures for your life so that you will kind of consistently re-engage with it and consistently do what you need to do to learn things better.\" [00:07:37] \n\n\"The way that mental models become useful is when you really spent a lot of time thinking about them, not when you just heard their name and kind of written them down and understood a few sentences.\" [00:13:54] \n\n\"There is a certain type of person, I guess you could say that like they do need to stop reading, they need to actually start just taking action on things and implementing things.\" [00:24:23] \n\n\"The possibilities of learning are a lot more vast than you've maybe previously considered.\" [01:02:17]\n\nCONNECT WITH SCOTT\nWebsite: https://www.scotthyoung.com/\n\nTwitter: https://twitter.com/scotthyoung/\n\nFacebook: https://www.facebook.com/AuthorScottYoung/\n\nSHOW NOTES\n\n[00:01:27] Introduction for our guest\n\n[00:02:42] Talk to us a bit about your journey. How did you get to where you are today?\n\n[00:03:30] The struggles on the path to becoming an ultralearner\n\n[00:06:22] The pitfalls of motivation\n\n[00:08:25] A walk down the narrow path to success\n\n[00:10:47] How to make sure you’re applying effort intelligently\n\n[00:13:18] The benefits and limits of mental models\n\n[00:16:32] The difference between knowing the name of a thing and knowing the thing\n\n[00:18:54] Scott’s favorite mental model\n\n[00:21:05] Scott talks about his doodles\n\n[00:23:33] Is reading making you stupid?\n\n[00:26:23] The danger of learning theories and not applying them\n\n[00:27:37] You need to do more than just homework\n\n[00:29:27] What to do when you’re stunned into inaction\n\n[00:31:39] You can’t see your brain getting buff\n\n[00:33:36] Luck to destiny\n\n[00:39:14] What exactly is ultralearning?\n\n[00:40:27] How can we use ultralearning to accelerate, transition, or rescue our careers?\n\n[00:41:46] Why is it that we procrastinate?\n\n[00:42:55] Mental habits to combat procrastination\n\n[00:45:40] You’re more ready than you think you are\n\n[00:49:32] How can we mitigate the distraction of our mind?\n\n[00:51:18] Do you have any tips for our listeners for what they could start doing today to improve the quality of their focus?\n\n[00:53:27] The principle of intuition\n\n[00:59:47] Building expert intuition\n\n[01:02:04] What's the one thing you want people to learn from your story?\n\n[01:03:25] The random roundSpecial Guest: Scott H. Young.","content_html":"Scott H Young a writer, programmer, traveler and avid reader of interesting things. For the last ten years he's been experimenting to find out how to learn and think better. Today he swings by the show and talks to us about how we can master hard skills faster!
\n\nQUOTES
\n"What you need is not just motivation, but you need some kind of system. You need some way to channel that initial burst of enthusiasm into creating structures for your life so that you will kind of consistently re-engage with it and consistently do what you need to do to learn things better." [00:07:37]
"The way that mental models become useful is when you really spent a lot of time thinking about them, not when you just heard their name and kind of written them down and understood a few sentences." [00:13:54]
\n\n"There is a certain type of person, I guess you could say that like they do need to stop reading, they need to actually start just taking action on things and implementing things." [00:24:23]
\n\n"The possibilities of learning are a lot more vast than you've maybe previously considered." [01:02:17]
\n\nCONNECT WITH SCOTT
\nWebsite: https://www.scotthyoung.com/
Twitter: https://twitter.com/scotthyoung/
\n\nFacebook: https://www.facebook.com/AuthorScottYoung/
\n\nSHOW NOTES
\n\n[00:01:27] Introduction for our guest
\n\n[00:02:42] Talk to us a bit about your journey. How did you get to where you are today?
\n\n[00:03:30] The struggles on the path to becoming an ultralearner
\n\n[00:06:22] The pitfalls of motivation
\n\n[00:08:25] A walk down the narrow path to success
\n\n[00:10:47] How to make sure you’re applying effort intelligently
\n\n[00:13:18] The benefits and limits of mental models
\n\n[00:16:32] The difference between knowing the name of a thing and knowing the thing
\n\n[00:18:54] Scott’s favorite mental model
\n\n[00:21:05] Scott talks about his doodles
\n\n[00:23:33] Is reading making you stupid?
\n\n[00:26:23] The danger of learning theories and not applying them
\n\n[00:27:37] You need to do more than just homework
\n\n[00:29:27] What to do when you’re stunned into inaction
\n\n[00:31:39] You can’t see your brain getting buff
\n\n[00:33:36] Luck to destiny
\n\n[00:39:14] What exactly is ultralearning?
\n\n[00:40:27] How can we use ultralearning to accelerate, transition, or rescue our careers?
\n\n[00:41:46] Why is it that we procrastinate?
\n\n[00:42:55] Mental habits to combat procrastination
\n\n[00:45:40] You’re more ready than you think you are
\n\n[00:49:32] How can we mitigate the distraction of our mind?
\n\n[00:51:18] Do you have any tips for our listeners for what they could start doing today to improve the quality of their focus?
\n\n[00:53:27] The principle of intuition
\n\n[00:59:47] Building expert intuition
\n\n[01:02:04] What's the one thing you want people to learn from your story?
\n\n[01:03:25] The random round
Special Guest: Scott H. Young.
","summary":"","date_published":"2020-09-17T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/ce09918a-2c64-48ed-aec2-44995815363c.mp3","mime_type":"audio/mpeg","size_in_bytes":42179611,"duration_in_seconds":4277}]},{"id":"36bb3932-812b-453c-8359-c37293732110","title":"Freelancing for Data Scientists | Alison Grade","url":"https://theartistsofdatascience.fireside.fm/alison-grade","content_text":"Living life on your own time and terms is a goal that many of us have. Alison Grade comes by the show to share her insight into how Freelancing can help you achieve this goal. She's the author of the Penguin best-selling book The Freelance Bible and shares a wealth of information with us to help get us started on the freelance journey.\n\nQUOTES\n\n\"The key thing about being a freelancer is that... you are in charge of your destiny. You're not waiting for someone to say, please do this...But with that comes huge autonomy because you can work where you like, when you like.\" [00:06:57] \n\n\"I always start by asking what's in it for me to do this for nothing? So I would only do something for nothing if it was delivering value for me. \" [00:21:03] \n\n\"The times when I've broken my own rule, it's always been a pain in the ass because they don't value me.\" [00:23:01] \n\n\"You've got to be self-motivated. You've got to just get out of bed and want to get on with it. If you're waiting for someone to tell you what to do you need to think about how do you change that. Because either it's really not suited to you or what you're looking at doing is just not motivating you. You know, you've got to you've got to have fire in the belly for go to want to do it.\" [00:31:32] \n\nFIND ALISON ONLINE\n\nWebsite: https://alisongrade.com/\n\nLinkedIn: https://www.linkedin.com/in/alisongrade/\n\nTwitter: https://twitter.com/alisongrade\n\nSHOW NOTES\n\n[00:01:31] Introduction for our guest\n\n[00:04:04] What are some of the documentaries and feature films that you've worked on that perhaps our audience might have heard of?\n\n[00:05:09] How COVID will affect the movie and theater industry\n\n[00:06:45] What does being a freelancer mean?\n\n[00:08:51] I-shaped versus T-shaped people\n\n[00:10:56] The Three C’s analysis\n\n[00:15:01] What can we do to make sure that we're pricing our services adequately?\n\n[00:19:30] How to determine your baseline rate for freelancing\n\n[00:20:52] Is there ever a situation where we should work for free? \n\n[00:23:15] Doing free work to build your portfolio\n\n[00:24:32] How can we make sure that we're getting the most out of our client meetings?\n\n[00:26:24] How can we clearly identify the problem that our client is trying to solve \n\n[00:28:33] So where do you see the future of freelancing headed in the next two to five years? \n\n[00:30:03] How do you think technology will impact freelancers in the next two to five years? \n\n[00:31:20] What do you think are some key traits that you think someone who wants to become a full-fledged entrepreneur should be cultivating within themselves?\n\n[00:33:26] Is there a difference between freelancing and entrepreneurship, or can those terms be used a bit interchangeably? \n\n[00:34:38] What would you say is the difference between the freelancer mindset and the entrepreneur mindset, having been on both kind of sides of the field?\n\n[00:35:51] What's the importance of building a personal brand as a freelancer? And how can someone build a personal brand for themselves?\n\n[00:37:50] Using Dunbar’s number to your advantage\n\n[00:40:10] How can we leverage networking events \n\n[00:42:47] Being a woman entrepreneur and freelancer\n\n[00:44:43] What's the one thing you want people to learn from your story?\n\n[00:45:39] The Random RoundSpecial Guest: Alison Grade.","content_html":"Living life on your own time and terms is a goal that many of us have. Alison Grade comes by the show to share her insight into how Freelancing can help you achieve this goal. She's the author of the Penguin best-selling book The Freelance Bible and shares a wealth of information with us to help get us started on the freelance journey.
\n\nQUOTES
\n\n"The key thing about being a freelancer is that... you are in charge of your destiny. You're not waiting for someone to say, please do this...But with that comes huge autonomy because you can work where you like, when you like." [00:06:57]
\n\n"I always start by asking what's in it for me to do this for nothing? So I would only do something for nothing if it was delivering value for me. " [00:21:03]
\n\n"The times when I've broken my own rule, it's always been a pain in the ass because they don't value me." [00:23:01]
\n\n"You've got to be self-motivated. You've got to just get out of bed and want to get on with it. If you're waiting for someone to tell you what to do you need to think about how do you change that. Because either it's really not suited to you or what you're looking at doing is just not motivating you. You know, you've got to you've got to have fire in the belly for go to want to do it." [00:31:32]
\n\nFIND ALISON ONLINE
\n\nWebsite: https://alisongrade.com/
\n\nLinkedIn: https://www.linkedin.com/in/alisongrade/
\n\nTwitter: https://twitter.com/alisongrade
\n\nSHOW NOTES
\n\n[00:01:31] Introduction for our guest
\n\n[00:04:04] What are some of the documentaries and feature films that you've worked on that perhaps our audience might have heard of?
\n\n[00:05:09] How COVID will affect the movie and theater industry
\n\n[00:06:45] What does being a freelancer mean?
\n\n[00:08:51] I-shaped versus T-shaped people
\n\n[00:10:56] The Three C’s analysis
\n\n[00:15:01] What can we do to make sure that we're pricing our services adequately?
\n\n[00:19:30] How to determine your baseline rate for freelancing
\n\n[00:20:52] Is there ever a situation where we should work for free?
\n\n[00:23:15] Doing free work to build your portfolio
\n\n[00:24:32] How can we make sure that we're getting the most out of our client meetings?
\n\n[00:26:24] How can we clearly identify the problem that our client is trying to solve
\n\n[00:28:33] So where do you see the future of freelancing headed in the next two to five years?
\n\n[00:30:03] How do you think technology will impact freelancers in the next two to five years?
\n\n[00:31:20] What do you think are some key traits that you think someone who wants to become a full-fledged entrepreneur should be cultivating within themselves?
\n\n[00:33:26] Is there a difference between freelancing and entrepreneurship, or can those terms be used a bit interchangeably?
\n\n[00:34:38] What would you say is the difference between the freelancer mindset and the entrepreneur mindset, having been on both kind of sides of the field?
\n\n[00:35:51] What's the importance of building a personal brand as a freelancer? And how can someone build a personal brand for themselves?
\n\n[00:37:50] Using Dunbar’s number to your advantage
\n\n[00:40:10] How can we leverage networking events
\n\n[00:42:47] Being a woman entrepreneur and freelancer
\n\n[00:44:43] What's the one thing you want people to learn from your story?
\n\n[00:45:39] The Random Round
Special Guest: Alison Grade.
","summary":"","date_published":"2020-09-14T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/36bb3932-812b-453c-8359-c37293732110.mp3","mime_type":"audio/mpeg","size_in_bytes":32372204,"duration_in_seconds":3430}]},{"id":"518f623d-64dd-4fbd-a989-e2ce88227fd6","title":"What is Your Why? | Mike Delgado","url":"https://theartistsofdatascience.fireside.fm/mike-delgado","content_text":"On this episode of The Artists of Data Science, we get a chance to hear from Mike Delgado, a social media strategist, speaker, community builder, and podcaster who serves as the director of social media at Experian over the last decade. When he's not doing awesome work at Experian, he's mentoring and teaching social media strategy courses at the University of California at Irvine.\n\nMike shares with us his journey into becoming a social media strategist from an English major and filmmaking background. He covers topics such as how to have more engagement on social media, the importance of compassion as a leader, and tips on finding your “why”. Mike’s passion for helping others is very evident in this episode, and his expertise and wisdom can help you find your purpose.\n\nWHAT YOU'LL LEARN\n\n[23:48] Biggest concerns of social media within the next two to five years\n\n[25:41] How can we be better citizens in our virtual community\n\n[29:39] Tips on finding your “why”\n\n[34:54] Qualities of a good leader\n\n[39:46] How we can boost our productivity and stay refreshed \n\nQUOTES\n\n[26:28] “...being part of a community means knowing when to be quiet…”\n\n[30:42] “...my calling at the deepest level is to help encourage and empower others in their work”\n\n[36:17] “I found that in my own failing, in my own mistakes, that I have grown the most.”\n\n[46:21] “the best way to help others is by taking care of yourself first”\n\nSHOW NOTES\n\n[00:01:52] Introduction for our guest\n\n[00:02:48] How did you first get into the social media space and what drew you to the field?\n\n[00:10:41] How to build a community\n\n[00:17:27] Building your brand on LinkedIn\n\n[00:18:35] Data science and social media\n\n[00:23:26] What do you think some of the biggest concerns are going to be for social media and society in the next two to five years?\n\n[00:25:16] How to be better virtual citizens \n\n[00:30:25] What is your why?\n\n[00:34:18] What makes a good leader and how you can cultivate those qualities\n\n[00:38:44] The hardest things to learn can’t be taught\n\n[00:39:33] Do you have any tips on how we can boost our productivity and stay refreshed during these work from home days?\n\n[00:41:32] How to maintain momentum in uncertain times\n\n[00:46:28] How we understand ourselves\n\n[00:48:20] What's the one thing you want people to learn from your story.\n\n[00:51:14] The Lightning RoundSpecial Guest: Mike Delgado.","content_html":"On this episode of The Artists of Data Science, we get a chance to hear from Mike Delgado, a social media strategist, speaker, community builder, and podcaster who serves as the director of social media at Experian over the last decade. When he's not doing awesome work at Experian, he's mentoring and teaching social media strategy courses at the University of California at Irvine.
\n\nMike shares with us his journey into becoming a social media strategist from an English major and filmmaking background. He covers topics such as how to have more engagement on social media, the importance of compassion as a leader, and tips on finding your “why”. Mike’s passion for helping others is very evident in this episode, and his expertise and wisdom can help you find your purpose.
\n\nWHAT YOU'LL LEARN
\n\n[23:48] Biggest concerns of social media within the next two to five years
\n\n[25:41] How can we be better citizens in our virtual community
\n\n[29:39] Tips on finding your “why”
\n\n[34:54] Qualities of a good leader
\n\n[39:46] How we can boost our productivity and stay refreshed
\n\nQUOTES
\n\n[26:28] “...being part of a community means knowing when to be quiet…”
\n\n[30:42] “...my calling at the deepest level is to help encourage and empower others in their work”
\n\n[36:17] “I found that in my own failing, in my own mistakes, that I have grown the most.”
\n\n[46:21] “the best way to help others is by taking care of yourself first”
\n\nSHOW NOTES
\n\n[00:01:52] Introduction for our guest
\n\n[00:02:48] How did you first get into the social media space and what drew you to the field?
\n\n[00:10:41] How to build a community
\n\n[00:17:27] Building your brand on LinkedIn
\n\n[00:18:35] Data science and social media
\n\n[00:23:26] What do you think some of the biggest concerns are going to be for social media and society in the next two to five years?
\n\n[00:25:16] How to be better virtual citizens
\n\n[00:30:25] What is your why?
\n\n[00:34:18] What makes a good leader and how you can cultivate those qualities
\n\n[00:38:44] The hardest things to learn can’t be taught
\n\n[00:39:33] Do you have any tips on how we can boost our productivity and stay refreshed during these work from home days?
\n\n[00:41:32] How to maintain momentum in uncertain times
\n\n[00:46:28] How we understand ourselves
\n\n[00:48:20] What's the one thing you want people to learn from your story.
\n\n[00:51:14] The Lightning Round
Special Guest: Mike Delgado.
","summary":"","date_published":"2020-09-10T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/518f623d-64dd-4fbd-a989-e2ce88227fd6.mp3","mime_type":"audio/mpeg","size_in_bytes":41047148,"duration_in_seconds":3581}]},{"id":"2b3e8b3c-1fe7-46db-b1a0-e3f9dae1d510","title":"Why You Have More Information Than You Think | Douglas W. Hubbard","url":"https://theartistsofdatascience.fireside.fm/douglas-w-hubbard","content_text":"On this episode of The Artists of Data Science, we get a chance to hear from Douglas Hubbard, a management consultant, speaker, and author in decision sciences. He's the inventor of the Applied Information Economics method and he's an internationally recognized expert in the field of measuring intangibles. He is also the author of many books, with his most recent one being “How to Measure Anything in Cybersecurity Risk”.\n\nDoug shares with us his journey into quantitative methodology, how to measure and quantify intangible things, and some of the misconceptions of statistics that are still being propagated. Doug’s expertise and knowledge in statistics is vast, and our listeners can gain a whole new perspective in measuring intangibles! \n\nWHAT YOU'LL LEARN\n\n[14:47] How data scientists can benefit from the methodologies of applied information economics\n[25:28] The Fermi decomposition\n[30:54] Three reasons why people think something can’t be measured\n[41:59] The concept of statistical significance\n[47:56] The difference between a Bayesian and frequentist\n\nQUOTES\n\n[21:18] “...measure with micrometer, cut with an axe.”\n\n[27:10] “...it's really easy to get lost in all the stuff you don't know”\n\n[43:11] “It's not just literacy you have to improve. It's not just that we have to learn new things about statistics. We have to unlearn misconceptions.”\n\n[43:52] “If you know almost nothing, almost anything will tell you something.’\n\nSHOW NOTES\n\n[00:01:36] Introduction for our guest today\n\n[00:02:59] Talk to us how you first got interested in measuring the intangibles?\n\n[00:05:14] What were some notable projects that you worked on during the early part of your career that helped you shape your philosophy of being able to measure anything?\n\n[00:09:20] What is applied information economics?\n\n[00:12:14] The importance of taking ideas from different domains and combining them in new days.\n\n[00:14:32] How do you see Data scientists benefiting from using the methodologies of applied information economics?\n\n[00:17:04] Where do you see the field of quantitative methodology headed in the next two to five years? \n\n[00:22:30] The difference between a decision models and predictive models \n\n[00:25:04] How to measure anything with Fermi decompositions\n\n[00:30:37] The three reasons people think something can’t be measured\n\n[00:38:16] Common misconceptions about statistics\n\n[00:41:52] Why is it so challenging for people to understand that concept of statistical significance and what it actually represents?\n\n[00:46:42] A purely philosophical interlude on Bayesian statistics\n\n[00:56:12] What’s the one thing you want people to learn from your story and from your work?\n\n[00:58:19] Jump into a quick lightning round. If you could meet any historical figure, who would it be?\n\n[00:58:38] What's the one thing you would say we truly cannot measure? \n\n[01:01:19] If you could have a billboard placed anywhere, what would you put on it?\n\n[01:01:25] What's the number one book, either fiction or nonfiction or even one of each that you would recommend our audience read, and what was your most impactful takeaway from it?\n\n[01:03:33] What is the best advice you have ever received?\n\n[01:04:42] Where can people find your books?\n\n[01:05:46] How can people connect with you? Where else can they find you online?Special Guest: Douglas W. Hubbard.","content_html":"On this episode of The Artists of Data Science, we get a chance to hear from Douglas Hubbard, a management consultant, speaker, and author in decision sciences. He's the inventor of the Applied Information Economics method and he's an internationally recognized expert in the field of measuring intangibles. He is also the author of many books, with his most recent one being “How to Measure Anything in Cybersecurity Risk”.
\n\nDoug shares with us his journey into quantitative methodology, how to measure and quantify intangible things, and some of the misconceptions of statistics that are still being propagated. Doug’s expertise and knowledge in statistics is vast, and our listeners can gain a whole new perspective in measuring intangibles!
\n\nWHAT YOU'LL LEARN
\n\n[14:47] How data scientists can benefit from the methodologies of applied information economics
\n[25:28] The Fermi decomposition
\n[30:54] Three reasons why people think something can’t be measured
\n[41:59] The concept of statistical significance
\n[47:56] The difference between a Bayesian and frequentist
QUOTES
\n\n[21:18] “...measure with micrometer, cut with an axe.”
\n\n[27:10] “...it's really easy to get lost in all the stuff you don't know”
\n\n[43:11] “It's not just literacy you have to improve. It's not just that we have to learn new things about statistics. We have to unlearn misconceptions.”
\n\n[43:52] “If you know almost nothing, almost anything will tell you something.’
\n\nSHOW NOTES
\n\n[00:01:36] Introduction for our guest today
\n\n[00:02:59] Talk to us how you first got interested in measuring the intangibles?
\n\n[00:05:14] What were some notable projects that you worked on during the early part of your career that helped you shape your philosophy of being able to measure anything?
\n\n[00:09:20] What is applied information economics?
\n\n[00:12:14] The importance of taking ideas from different domains and combining them in new days.
\n\n[00:14:32] How do you see Data scientists benefiting from using the methodologies of applied information economics?
\n\n[00:17:04] Where do you see the field of quantitative methodology headed in the next two to five years?
\n\n[00:22:30] The difference between a decision models and predictive models
\n\n[00:25:04] How to measure anything with Fermi decompositions
\n\n[00:30:37] The three reasons people think something can’t be measured
\n\n[00:38:16] Common misconceptions about statistics
\n\n[00:41:52] Why is it so challenging for people to understand that concept of statistical significance and what it actually represents?
\n\n[00:46:42] A purely philosophical interlude on Bayesian statistics
\n\n[00:56:12] What’s the one thing you want people to learn from your story and from your work?
\n\n[00:58:19] Jump into a quick lightning round. If you could meet any historical figure, who would it be?
\n\n[00:58:38] What's the one thing you would say we truly cannot measure?
\n\n[01:01:19] If you could have a billboard placed anywhere, what would you put on it?
\n\n[01:01:25] What's the number one book, either fiction or nonfiction or even one of each that you would recommend our audience read, and what was your most impactful takeaway from it?
\n\n[01:03:33] What is the best advice you have ever received?
\n\n[01:04:42] Where can people find your books?
\n\n[01:05:46] How can people connect with you? Where else can they find you online?
Special Guest: Douglas W. Hubbard.
","summary":"","date_published":"2020-09-07T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/2b3e8b3c-1fe7-46db-b1a0-e3f9dae1d510.mp3","mime_type":"audio/mpeg","size_in_bytes":39538234,"duration_in_seconds":4036}]},{"id":"fc10cda5-4a93-4de3-9510-0f7e0d71a52d","title":"Explaining Humans | Camilla Pang","url":"https://theartistsofdatascience.fireside.fm/camilla-pang","content_text":"On this episode of The Artists of Data Science, we get a chance to hear from Camilla Pang, a scientist specializing in translational bioinformatics. \n\nAt the age of eight, she was diagnosed with autism spectrum disorder and struggled to understand the world around her and the way people were. \n\nHer book, “Explaining Humans:What science can teach us about life, love, and relationships” is an original and incisive exploration of human nature and the strangeness of our social norms. \n\nCamilla shares with us her journey into science, and her mission to understand human behavior at a young age. She also discusses the potential impacts of machine learning and A.I within the next few years, and the importance of understanding the nuances in data scientists that create individuality. \n\nWHAT YOU'LL LEARN\n\n[7:18] Potential negative impacts of A.I\n\n[17:00] Learning to embrace errors\n\n[38:11] Getting over the perfectionist mindset\n\n[39:30] Important soft skills you need to cultivate \n\n[44:17] Advice for women in STEM\n\nQUOTES\n\n[6:59] “...before we get on to making the most of A.I we first need to make the most out of human minds.”\n\n[17:20] “an error in one context is a solution in the next”\n\n[47:10] Don’t judge yourself for thinking outside the box. Stay true to yourself and your vision. \n\n[55:23] “...just because you don't fit in a system, doesn't mean you weren't born to make a new one.”\n\nFIND CAMILLA ONLINE\n\nLinkedIn: https://www.linkedin.com/in/camilla-pang-8b177b69/\n\nInstagram: https://www.instagram.com/millie_moonface/\n\nTwitter: https://twitter.com/millzymai\n\nSHOW NOTES\n\n[00:01:32] Introduction for our guest\n\n[00:02:59] A large, open-ended question.\n\n[00:04:32] What you think the next big thing in machine learning is going to be in the next two to five years,\n\n[00:06:08] What do you think would be the biggest positive impact on society?\n\n[00:07:04] What do you think would be scariest applications of machine learning in the next two to five years?\n\n[00:07:51] What do you think separates the great Data scientists from the merely good ones?\n\n[00:09:47] Talk to us about the terms neurotypical and neurodiverse. Would you mind defining these terms for our audience?\n\n[00:11:29] What does it mean to think in boxes and what does it mean to think in trees?\n\n[00:14:59] Why are most people stuck in box thinking?\n\n[00:15:49] How to be a tree thinker\n\n[00:16:50] What can we do to start embracing errors in our own lives?\n\n[00:19:27] What do proteins have to do with personality and interpersonal relationships?\n\n[00:20:50] How could we use this understanding of proteins to be better colleagues and better teammates at work?\n\n[00:23:09] Never let your fear define your fate\n\n[00:25:16] Gradient descent in layman’s terms\n\n[00:26:47] How to use gradient descent to find our path to prioritize and identify our goals?\n\n[00:28:37] How can we use Bayes Theorem for empathy and managing the relationships that we have with ourselves?\n\n[00:31:02] What neural nets can teach us about ourselves\n\n[00:32:17] Is data science an art? Or is it a science?\n\n[00:33:30] How does the creative process manifest itself in Data science?\n\n[00:35:11] How to take better notes\n\n[00:37:26] How to stop being a perfectionist\n\n[00:39:10] Why soft skills are hard work\n\n[00:42:54] We’re both INFJ’s!\n\n[00:44:26] Advice for women in STEM\n\n[00:46:21] What can the Data community do to foster the inclusion of women in Data science and AI and STEM?\n\n[00:47:00] What's the one thing you want people to learn from this story?\n\n[00:48:37] The lightning roundSpecial Guest: Camilla Pang, PhD.","content_html":"On this episode of The Artists of Data Science, we get a chance to hear from Camilla Pang, a scientist specializing in translational bioinformatics.
\n\nAt the age of eight, she was diagnosed with autism spectrum disorder and struggled to understand the world around her and the way people were.
\n\nHer book, “Explaining Humans:What science can teach us about life, love, and relationships” is an original and incisive exploration of human nature and the strangeness of our social norms.
\n\nCamilla shares with us her journey into science, and her mission to understand human behavior at a young age. She also discusses the potential impacts of machine learning and A.I within the next few years, and the importance of understanding the nuances in data scientists that create individuality.
\n\nWHAT YOU'LL LEARN
\n\n[7:18] Potential negative impacts of A.I
\n\n[17:00] Learning to embrace errors
\n\n[38:11] Getting over the perfectionist mindset
\n\n[39:30] Important soft skills you need to cultivate
\n\n[44:17] Advice for women in STEM
\n\nQUOTES
\n\n[6:59] “...before we get on to making the most of A.I we first need to make the most out of human minds.”
\n\n[17:20] “an error in one context is a solution in the next”
\n\n[47:10] Don’t judge yourself for thinking outside the box. Stay true to yourself and your vision.
\n\n[55:23] “...just because you don't fit in a system, doesn't mean you weren't born to make a new one.”
\n\nFIND CAMILLA ONLINE
\n\nLinkedIn: https://www.linkedin.com/in/camilla-pang-8b177b69/
\n\nInstagram: https://www.instagram.com/millie_moonface/
\n\nTwitter: https://twitter.com/millzymai
\n\nSHOW NOTES
\n\n[00:01:32] Introduction for our guest
\n\n[00:02:59] A large, open-ended question.
\n\n[00:04:32] What you think the next big thing in machine learning is going to be in the next two to five years,
\n\n[00:06:08] What do you think would be the biggest positive impact on society?
\n\n[00:07:04] What do you think would be scariest applications of machine learning in the next two to five years?
\n\n[00:07:51] What do you think separates the great Data scientists from the merely good ones?
\n\n[00:09:47] Talk to us about the terms neurotypical and neurodiverse. Would you mind defining these terms for our audience?
\n\n[00:11:29] What does it mean to think in boxes and what does it mean to think in trees?
\n\n[00:14:59] Why are most people stuck in box thinking?
\n\n[00:15:49] How to be a tree thinker
\n\n[00:16:50] What can we do to start embracing errors in our own lives?
\n\n[00:19:27] What do proteins have to do with personality and interpersonal relationships?
\n\n[00:20:50] How could we use this understanding of proteins to be better colleagues and better teammates at work?
\n\n[00:23:09] Never let your fear define your fate
\n\n[00:25:16] Gradient descent in layman’s terms
\n\n[00:26:47] How to use gradient descent to find our path to prioritize and identify our goals?
\n\n[00:28:37] How can we use Bayes Theorem for empathy and managing the relationships that we have with ourselves?
\n\n[00:31:02] What neural nets can teach us about ourselves
\n\n[00:32:17] Is data science an art? Or is it a science?
\n\n[00:33:30] How does the creative process manifest itself in Data science?
\n\n[00:35:11] How to take better notes
\n\n[00:37:26] How to stop being a perfectionist
\n\n[00:39:10] Why soft skills are hard work
\n\n[00:42:54] We’re both INFJ’s!
\n\n[00:44:26] Advice for women in STEM
\n\n[00:46:21] What can the Data community do to foster the inclusion of women in Data science and AI and STEM?
\n\n[00:47:00] What's the one thing you want people to learn from this story?
\n\n[00:48:37] The lightning round
Special Guest: Camilla Pang, PhD.
","summary":"","date_published":"2020-09-03T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/fc10cda5-4a93-4de3-9510-0f7e0d71a52d.mp3","mime_type":"audio/mpeg","size_in_bytes":34992515,"duration_in_seconds":3580}]},{"id":"450b0fb8-434f-4514-a8d3-82f6d54a1d70","title":"The Many Models Mindset | Scott E. Page","url":"https://theartistsofdatascience.fireside.fm/scott-e-page","content_text":"On this episode of The Artists of Data Science, we get a chance to hear from Scott Page, a professor who studies complex systems and collective intelligence teams and political and economic institutions. He's known for his research on and modeling of diversity and complexity in the social sciences with a particular interest in the roles that diversity plays in complex systems. His book, “The Model Thinker”, stresses the application of ensembles of models to make sense of complex phenomena.\n\nScott shares with us his predictions into the future of machine learning, the importance of using a simple model, and how diversity impacts productivity. This episode is packed with amazing content that all data scientists and machine learning practitioners can apply in their lives. It was an absolute pleasure chatting with Scott!\n\nWHAT YOU'LL LEARN\n\n[12:41] Scariest applications of machine learning we might see \n\n[24:56] What is a model, and why must they be simple?\n\n[33:30] Many model thinking and it’s advantages\n\n[47:07] How diversity impacts productivity\n\n[49:46] How creativity impacts success, and how to be more creative\n\nQUOTES\n\n[6:31] “...you have to separate achievement from purpose.”\n\n[35:45] “...if you really want to understand a complex phenomena, you've got to look at it with lots of lenses…”\n\n[45:02] “...what you really want...is people who are acquiring different ways of thinking and understanding different tools, because then the whole is going to be so much more than the sum of the parts.”\n\n[46:36] “Creativity is the union of sets. Getting at the truth is the intersection of sets.”\n\nSHOW NOTES\n\n[00:01:15] Introduction for our guest\n\n[00:02:45] What drew you to the field of modeling in general and specifically game theory and complexity?\n\n[00:03:49] So what were some of the challenges you faced while you're paving your own lane in the field?\n\n[00:05:34] Separate achievement from purpose\n\n[00:06:53] The synergy of ideas\n\n[00:10:24] The biggest positive of machine learning on society in the next two to five years. \n\n[00:12:35] The scariest applications of machine learning in the next two to five years?\n\n[00:14:00] The online echo chamber\n\n[00:15:12] Big data versus thick data\n\n[00:17:05] Is thick data like longitudinal data?\n\n[00:19:23] As practitioners of data science and machine learning, what do you think will be some of our biggest areas of concern?\n\n[00:21:34] The “Scott Page Canned Beets” argument\n\n[00:24:49] What is a model and why must they be simple?\n\n[00:26:10] What are the three classes of models?\n\n[00:26:50] What are the seven uses of models, aka the REDCAPE?\n\n[00:29:00] The wisdom hierarchy\n\n[00:31:14] The importance of assumptions while constructing a model\n\n[00:33:20] Many model thinking vs single model thinking\n\n[00:35:53] The difficulties of modelling human behavior\n\n[00:39:02] Identity diversity versus cognitive diversity\n\n[00:42:42] Cognitive diversity and mental models\n\n[00:44:43] Cognitive diversity for knowledge workers\n\n[00:45:14] Diversity and creativity\n\n[00:47:04] In what ways does diversity make systems more productive? \n\n[00:48:28] Is Data science machine learning to be an art or purely a hard science? \n\n[00:49:31] Success and creativity\n\n[00:51:32] What's the one thing you want people to learn from your story?\n\n[00:53:41] The lightning roundSpecial Guest: Scott E. Page.","content_html":"On this episode of The Artists of Data Science, we get a chance to hear from Scott Page, a professor who studies complex systems and collective intelligence teams and political and economic institutions. He's known for his research on and modeling of diversity and complexity in the social sciences with a particular interest in the roles that diversity plays in complex systems. His book, “The Model Thinker”, stresses the application of ensembles of models to make sense of complex phenomena.
\n\nScott shares with us his predictions into the future of machine learning, the importance of using a simple model, and how diversity impacts productivity. This episode is packed with amazing content that all data scientists and machine learning practitioners can apply in their lives. It was an absolute pleasure chatting with Scott!
\n\nWHAT YOU'LL LEARN
\n\n[12:41] Scariest applications of machine learning we might see
\n\n[24:56] What is a model, and why must they be simple?
\n\n[33:30] Many model thinking and it’s advantages
\n\n[47:07] How diversity impacts productivity
\n\n[49:46] How creativity impacts success, and how to be more creative
\n\nQUOTES
\n\n[6:31] “...you have to separate achievement from purpose.”
\n\n[35:45] “...if you really want to understand a complex phenomena, you've got to look at it with lots of lenses…”
\n\n[45:02] “...what you really want...is people who are acquiring different ways of thinking and understanding different tools, because then the whole is going to be so much more than the sum of the parts.”
\n\n[46:36] “Creativity is the union of sets. Getting at the truth is the intersection of sets.”
\n\nSHOW NOTES
\n\n[00:01:15] Introduction for our guest
\n\n[00:02:45] What drew you to the field of modeling in general and specifically game theory and complexity?
\n\n[00:03:49] So what were some of the challenges you faced while you're paving your own lane in the field?
\n\n[00:05:34] Separate achievement from purpose
\n\n[00:06:53] The synergy of ideas
\n\n[00:10:24] The biggest positive of machine learning on society in the next two to five years.
\n\n[00:12:35] The scariest applications of machine learning in the next two to five years?
\n\n[00:14:00] The online echo chamber
\n\n[00:15:12] Big data versus thick data
\n\n[00:17:05] Is thick data like longitudinal data?
\n\n[00:19:23] As practitioners of data science and machine learning, what do you think will be some of our biggest areas of concern?
\n\n[00:21:34] The “Scott Page Canned Beets” argument
\n\n[00:24:49] What is a model and why must they be simple?
\n\n[00:26:10] What are the three classes of models?
\n\n[00:26:50] What are the seven uses of models, aka the REDCAPE?
\n\n[00:29:00] The wisdom hierarchy
\n\n[00:31:14] The importance of assumptions while constructing a model
\n\n[00:33:20] Many model thinking vs single model thinking
\n\n[00:35:53] The difficulties of modelling human behavior
\n\n[00:39:02] Identity diversity versus cognitive diversity
\n\n[00:42:42] Cognitive diversity and mental models
\n\n[00:44:43] Cognitive diversity for knowledge workers
\n\n[00:45:14] Diversity and creativity
\n\n[00:47:04] In what ways does diversity make systems more productive?
\n\n[00:48:28] Is Data science machine learning to be an art or purely a hard science?
\n\n[00:49:31] Success and creativity
\n\n[00:51:32] What's the one thing you want people to learn from your story?
\n\n[00:53:41] The lightning round
Special Guest: Scott E. Page.
","summary":"","date_published":"2020-08-31T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/450b0fb8-434f-4514-a8d3-82f6d54a1d70.mp3","mime_type":"audio/mpeg","size_in_bytes":38673023,"duration_in_seconds":3741}]},{"id":"08129589-0784-417d-97e6-29d0fd9ddaa6","title":"Naked Data Science | Charles Wheelan","url":"https://theartistsofdatascience.fireside.fm/charles-wheelan-phd","content_text":"On this episode of The Artists of Data Science, we get a chance to hear from Charles Wheelan, a professor, journalist, speaker and author, who holds a PHD in public policy from the University Chicago. He's currently a senior lecturer and policy fellow at the Rockefeller Center at Dartmouth College, and the author of Naked Economics, a book that is an accessible and entertaining introduction to economics for the layperson.\n\nCharles shares with us his journey into becoming a prolific author, and why he decided to write Naked Economics, Naked Statistics, and so on. He also warns the use of big data and the implications it can have if used improperly. This episode is packed with insights into money, statistics, and some of the important problems the world is currently facing. \n\nWHAT YOU'LL LEARN\n\n[4:25] Charles’s tips on learning a subject effectively \n\n[12:41] What is money, and why does it matter? \n\n[21:40] How statistics can be used to make solve problems\n\n[26:55] Why humans are so bad at appreciating and conceptualizing probabilities\n\n[33:02] Important soft skills that technically oriented people need \n\nQUOTES\n\n[11:56] “Big Data is...a powerful weapon...it really can be put to great effect. Used improperly, you can do some enormous damage.”\n\n[33:07] “...even if you are very technically oriented, you have got to have an awareness of sociology, psychology, great literature and the like.”\n\n[36:23] “...if your data reflects some underlying problem, then any model you build from that data will just embed it more firmly in cement.”\n\n[48:15] …”stop thinking about what you're doing and look around the world and see what's missing”\n\nFIND CHARLES ONLINE\n\nLinkedIn: https://www.linkedin.com/in/charles-wheelan-a6220911/\n\nWebsite: http://www.nakedeconomics.com/\n\nTwitter: https://twitter.com/CharlesWheelan\n\nSHOW NOTES\n[00:01:19] Introduction for our guest\n\n[00:02:45] How did you become so interested in statistics?\n\n[00:04:16] Was there a lot of self study involved in learning statistics?\n\n[00:05:06] How he wrote Naked Statistics\n\n[00:06:51] What is economics?\n\n[00:09:19] Does big data impact how economics works?\n\n[00:11:21] Does big data change how the invisible hand works?\n\n[00:12:35] What is money and why does it matter?\n\n[00:16:43] Money in a world of contactless payments\n\n[00:18:18] The impact of digital currencies on society\n\n[00:20:15] Money and intersubjective reality\n\n[00:21:22] How to use statistics to make business work better\n\n[00:23:12] Which form of bias should we be most wary of?\n\n[00:24:40] How will COVID affect the election\n\n[00:26:49] Why are humans so bad at appreciating conceptualizing probabilities?\n\n[00:29:26] Why is it important that we cultivate an intuition for what probabilities represent?\n\n[00:30:39] Why we shouldn't buy the extended warranty\n\n[00:32:38] What's going to separate them from the rest of the world, the rest the competition.\n\n[00:32:54] What soft skills do you need to be successful?\n\n[00:37:19] Charles Wheelan predicted COVID in his book The Rationing\n\n[00:37:37] Draw parallels between the fiction you wrote and the reality that we're experiencing today\n\n[00:39:03] How he came up with the story for The Rationing\n\n[00:41:07] Which aspect of human nature do you think from your fiction has shown itself to become a reality with our current situation?\n\n[00:43:18] What's the one thing you want people to learn from this story?\n\n[00:44:35] The lightning roundSpecial Guest: Charles Wheelan, PhD.","content_html":"On this episode of The Artists of Data Science, we get a chance to hear from Charles Wheelan, a professor, journalist, speaker and author, who holds a PHD in public policy from the University Chicago. He's currently a senior lecturer and policy fellow at the Rockefeller Center at Dartmouth College, and the author of Naked Economics, a book that is an accessible and entertaining introduction to economics for the layperson.
\n\nCharles shares with us his journey into becoming a prolific author, and why he decided to write Naked Economics, Naked Statistics, and so on. He also warns the use of big data and the implications it can have if used improperly. This episode is packed with insights into money, statistics, and some of the important problems the world is currently facing.
\n\nWHAT YOU'LL LEARN
\n\n[4:25] Charles’s tips on learning a subject effectively
\n\n[12:41] What is money, and why does it matter?
\n\n[21:40] How statistics can be used to make solve problems
\n\n[26:55] Why humans are so bad at appreciating and conceptualizing probabilities
\n\n[33:02] Important soft skills that technically oriented people need
\n\nQUOTES
\n\n[11:56] “Big Data is...a powerful weapon...it really can be put to great effect. Used improperly, you can do some enormous damage.”
\n\n[33:07] “...even if you are very technically oriented, you have got to have an awareness of sociology, psychology, great literature and the like.”
\n\n[36:23] “...if your data reflects some underlying problem, then any model you build from that data will just embed it more firmly in cement.”
\n\n[48:15] …”stop thinking about what you're doing and look around the world and see what's missing”
\n\nFIND CHARLES ONLINE
\n\nLinkedIn: https://www.linkedin.com/in/charles-wheelan-a6220911/
\n\nWebsite: http://www.nakedeconomics.com/
\n\nTwitter: https://twitter.com/CharlesWheelan
\n\nSHOW NOTES
\n[00:01:19] Introduction for our guest
[00:02:45] How did you become so interested in statistics?
\n\n[00:04:16] Was there a lot of self study involved in learning statistics?
\n\n[00:05:06] How he wrote Naked Statistics
\n\n[00:06:51] What is economics?
\n\n[00:09:19] Does big data impact how economics works?
\n\n[00:11:21] Does big data change how the invisible hand works?
\n\n[00:12:35] What is money and why does it matter?
\n\n[00:16:43] Money in a world of contactless payments
\n\n[00:18:18] The impact of digital currencies on society
\n\n[00:20:15] Money and intersubjective reality
\n\n[00:21:22] How to use statistics to make business work better
\n\n[00:23:12] Which form of bias should we be most wary of?
\n\n[00:24:40] How will COVID affect the election
\n\n[00:26:49] Why are humans so bad at appreciating conceptualizing probabilities?
\n\n[00:29:26] Why is it important that we cultivate an intuition for what probabilities represent?
\n\n[00:30:39] Why we shouldn't buy the extended warranty
\n\n[00:32:38] What's going to separate them from the rest of the world, the rest the competition.
\n\n[00:32:54] What soft skills do you need to be successful?
\n\n[00:37:19] Charles Wheelan predicted COVID in his book The Rationing
\n\n[00:37:37] Draw parallels between the fiction you wrote and the reality that we're experiencing today
\n\n[00:39:03] How he came up with the story for The Rationing
\n\n[00:41:07] Which aspect of human nature do you think from your fiction has shown itself to become a reality with our current situation?
\n\n[00:43:18] What's the one thing you want people to learn from this story?
\n\n[00:44:35] The lightning round
Special Guest: Charles Wheelan, PhD.
","summary":"We get an opportunity to talk economics, statistics, and more with New York Times Best Selling author Dr. Charles Wheelan! ","date_published":"2020-08-27T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/08129589-0784-417d-97e6-29d0fd9ddaa6.mp3","mime_type":"audio/mpeg","size_in_bytes":33369397,"duration_in_seconds":3598}]},{"id":"4838dfaa-808d-40ca-b86b-dcdc4da4b070","title":"The Contemporary Practice of ML SUCKS! | Carl Osipov","url":"https://theartistsofdatascience.fireside.fm/carl-osipov","content_text":"On this episode of The Artists of Data Science, we get a chance to hear from Carl Osipov, who has nearly two decades of information technology experience spanning roles such as program manager at Google, an IT executive at IBM, and as an adviser to Fortune 500 companies. Today, he's here to talk about his book, “Serverless Machine Learning In Action”, which is targeted at teams and individuals who are interested in building machine learning system implementations efficiently at scale.\n\nCarl shares with us his take on the future impacts of machine learning, the creative process in feature engineering, and important soft skills that data scientists need to develop. Carl’s expertise and advice will resonate with beginners and senior data scientists alike. It was a great pleasure speaking with him!\n\nWHAT YOU'LL LEARN\n\n[5:01] Hype in machine learning and how it’s changed \n\n[8:58] The potential negative impacts of machine learning \n\n[38:21] Is machine learning an art or science?\n\n[51:47] Important soft skills you need to succeed \n\n[54:23] Tips on communicating with executives\n\nQUOTES\n\n[12:00] “I think what will make data scientists of tomorrow successful is going to be more about the understanding of human culture.”\n\n[58:03] “The most important lesson is to be persistent and continue focusing on that one successful outcome. You only need to be successful once, so don't worry about any of those individual failures.”\n\n[58:50] “Whenever you collaborate with someone and you're willing to learn from them, you're going to come away as a person who really grows as an individual…”\n\nSHOW NOTES\n\n[00:01:33] Introduction for our guest today\n\n[00:03:03] What drew you to this field? What were some of the challenges you faced breaking into the field?\n\n[00:04:46] How much more hyped has machine learning become since you first kind of broke into this?\n\n[00:05:59] Where do you see now the field of machine learning headed in the next two to five years?\n\n[00:07:41] What do you see being the biggest positive impact coming from machine learning in the next two to five years?\n\n[00:08:52] What do you think would be the scariest application of machine learning in the next two to five years?\n\n[00:10:55] What are some things that we should keep on top of our mind as areas of concern so that we can kind of mitigate the risk of these scary applications?\n\n[00:11:45] What do you think will separate the great Data scientists from just the good ones?\n\n[00:13:48] What is serverless machine learning and how is it different from regular old fashioned machine learning?\n\n[00:17:10] So what is the difference between machine learning code and machine learning platform?\n\n[00:19:14] What is it about the contemporary practice of machine learning that tends to just suck our productivity from the practitioner?\n\n[00:21:24] At what point then does it make sense for us to start using serverless machine learning? \n\n[00:23:05] The difference between row-oriented and column-oriented storage.\n\n[00:27:21] A hypothetical scenario where serverless machine learning would an ideal use case.\n\n[00:28:52] What tips you can share with our audience so that we can be more thoughtful with our feature engineering.\n\n[00:31:55] What are some tips that you can share with our audience so that we can be more thoughtful in our hyperparameter tuning?\n\n[00:34:17] What do we do once a model is put into production?\n\n[00:38:07] Is data science an art? Or is it purely a science?\n\n[00:39:51] The creative process in data science\n\n[00:43:19] The democratization of machine learning\n\n[00:45:21] What would you say was the biggest lesson you learned about democratization of A.I. while you're over at Google?\n\n[00:46:16] We discuss the many patents Carl has published\n\n[00:48:53] Which of your publications, your patents do you think are most applicable to our current times?\n\n[00:51:24] What soft-skills do you need to be successful?\n\n[00:53:49] How to communicate with executives\n\n[00:55:54] How to develop your product sense and business acumen\n\n[00:57:10] Why you shouldn’t be discouraged by these insane job descriptions\n\n[00:58:16] What’s the one thing you want to people to learn from your story?\n\n[00:59:03] Where can people find your book?\n\n[00:59:44] What's your data science superpower?\n\n[00:59:59] If AI could answer any question for you, what would you ask?\n\n[01:00:05] What do you believe that other people think is crazy?\n\n[01:00:21] If you could have a billboard anywhere. What would you put on it?\n\n[01:00:31] What is an academic topic outside of Data science that you think every data scientist should spend some time studying and researching on?\n\n[01:00:48] What would be the number one book? Fiction, nonfiction, or maybe one of each that you would recommend our audience read. And what was your most impactful takeaway from it?\n\n[01:01:21] If we can get a magic telephone that allowed you to contact 18 year old Carl, what would you tell him?\n\n[01:01:39] What's the best advice you have ever received? \n\n[01:01:43] What motivates you?\n\n[01:01:46] What song do you currently have on repeat?\n\n[01:01:56] How can people connect with you and what can they find you online?Special Guest: Carl Osipov.","content_html":"On this episode of The Artists of Data Science, we get a chance to hear from Carl Osipov, who has nearly two decades of information technology experience spanning roles such as program manager at Google, an IT executive at IBM, and as an adviser to Fortune 500 companies. Today, he's here to talk about his book, “Serverless Machine Learning In Action”, which is targeted at teams and individuals who are interested in building machine learning system implementations efficiently at scale.
\n\nCarl shares with us his take on the future impacts of machine learning, the creative process in feature engineering, and important soft skills that data scientists need to develop. Carl’s expertise and advice will resonate with beginners and senior data scientists alike. It was a great pleasure speaking with him!
\n\nWHAT YOU'LL LEARN
\n\n[5:01] Hype in machine learning and how it’s changed
\n\n[8:58] The potential negative impacts of machine learning
\n\n[38:21] Is machine learning an art or science?
\n\n[51:47] Important soft skills you need to succeed
\n\n[54:23] Tips on communicating with executives
\n\nQUOTES
\n\n[12:00] “I think what will make data scientists of tomorrow successful is going to be more about the understanding of human culture.”
\n\n[58:03] “The most important lesson is to be persistent and continue focusing on that one successful outcome. You only need to be successful once, so don't worry about any of those individual failures.”
\n\n[58:50] “Whenever you collaborate with someone and you're willing to learn from them, you're going to come away as a person who really grows as an individual…”
\n\nSHOW NOTES
\n\n[00:01:33] Introduction for our guest today
\n\n[00:03:03] What drew you to this field? What were some of the challenges you faced breaking into the field?
\n\n[00:04:46] How much more hyped has machine learning become since you first kind of broke into this?
\n\n[00:05:59] Where do you see now the field of machine learning headed in the next two to five years?
\n\n[00:07:41] What do you see being the biggest positive impact coming from machine learning in the next two to five years?
\n\n[00:08:52] What do you think would be the scariest application of machine learning in the next two to five years?
\n\n[00:10:55] What are some things that we should keep on top of our mind as areas of concern so that we can kind of mitigate the risk of these scary applications?
\n\n[00:11:45] What do you think will separate the great Data scientists from just the good ones?
\n\n[00:13:48] What is serverless machine learning and how is it different from regular old fashioned machine learning?
\n\n[00:17:10] So what is the difference between machine learning code and machine learning platform?
\n\n[00:19:14] What is it about the contemporary practice of machine learning that tends to just suck our productivity from the practitioner?
\n\n[00:21:24] At what point then does it make sense for us to start using serverless machine learning?
\n\n[00:23:05] The difference between row-oriented and column-oriented storage.
\n\n[00:27:21] A hypothetical scenario where serverless machine learning would an ideal use case.
\n\n[00:28:52] What tips you can share with our audience so that we can be more thoughtful with our feature engineering.
\n\n[00:31:55] What are some tips that you can share with our audience so that we can be more thoughtful in our hyperparameter tuning?
\n\n[00:34:17] What do we do once a model is put into production?
\n\n[00:38:07] Is data science an art? Or is it purely a science?
\n\n[00:39:51] The creative process in data science
\n\n[00:43:19] The democratization of machine learning
\n\n[00:45:21] What would you say was the biggest lesson you learned about democratization of A.I. while you're over at Google?
\n\n[00:46:16] We discuss the many patents Carl has published
\n\n[00:48:53] Which of your publications, your patents do you think are most applicable to our current times?
\n\n[00:51:24] What soft-skills do you need to be successful?
\n\n[00:53:49] How to communicate with executives
\n\n[00:55:54] How to develop your product sense and business acumen
\n\n[00:57:10] Why you shouldn’t be discouraged by these insane job descriptions
\n\n[00:58:16] What’s the one thing you want to people to learn from your story?
\n\n[00:59:03] Where can people find your book?
\n\n[00:59:44] What's your data science superpower?
\n\n[00:59:59] If AI could answer any question for you, what would you ask?
\n\n[01:00:05] What do you believe that other people think is crazy?
\n\n[01:00:21] If you could have a billboard anywhere. What would you put on it?
\n\n[01:00:31] What is an academic topic outside of Data science that you think every data scientist should spend some time studying and researching on?
\n\n[01:00:48] What would be the number one book? Fiction, nonfiction, or maybe one of each that you would recommend our audience read. And what was your most impactful takeaway from it?
\n\n[01:01:21] If we can get a magic telephone that allowed you to contact 18 year old Carl, what would you tell him?
\n\n[01:01:39] What's the best advice you have ever received?
\n\n[01:01:43] What motivates you?
\n\n[01:01:46] What song do you currently have on repeat?
\n\n[01:01:56] How can people connect with you and what can they find you online?
Special Guest: Carl Osipov.
","summary":"","date_published":"2020-08-24T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/4838dfaa-808d-40ca-b86b-dcdc4da4b070.mp3","mime_type":"audio/mpeg","size_in_bytes":40620647,"duration_in_seconds":3775}]},{"id":"e2688eb3-eea3-4902-9d24-5722174236df","title":"How to Become a Chief Data Scientist | T. Scott Clendaniel","url":"https://theartistsofdatascience.fireside.fm/t-scott-clendaniel","content_text":"On this episode of The Artists of Data Science, we get a chance to hear from T. Scott Clendaniel, a leader in the data science space with over three decades of experience serving in various roles in business, analytics and artificial intelligence. Currently, he's a chief data scientist of the Strategic Artificial Intelligence Lab, where he's aiming to create cutting edge artificial intelligence that can be made accessible to all. He gives insight into the future of A.I, how to be an effective leader, and how to use storytelling in data science.\n\nScott shares with us his incredible career journey and the insights he has gathered from it. This episode is packed with advice, wisdom, and tips for every data scientist to take something from. It was a great honor interviewing Scott!\n\nWHAT YOU'LL LEARN\n\n[7:57] What is an A.I. winter? \n\n[10:54] Where the field of data science is headed in the next few years?\n\n[13:58] Tips on being an effective leader\n\n[20:39] The underrated skill of storytelling, and how to cultivate it\n\n[32:43] Tips for people that want to break into data science\n\nQUOTES\n\n[16:01] “If you're the first data scientist in an organization...make sure that you focus on a crawl, walk, run approach.”\n\n[17:50] “Simplicity is ridiculously underrated…people do not support what they don't understand. Instead, they fear what they don't understand.”\n\n[35:03] “Find your why and make sure it's the right why and use that to propel you…”\n\nSHOW NOTES\n\n[00:01:35] Introduction for our guest today\n\n[00:03:33] What drew you to the field and some of the challenges you faced while you're trying to break into and create your own lane in Data science?\n\n[00:05:00] How much more hyped has I become since he first broke into the field?\n\n[00:07:39] A brief history of the AI winters we've experienced and why we're on the verge of the next winter\n\n[00:10:54] Where do you see the field of data science, machine learning, artificial intelligence headed in the next two to five years?\n\n[00:12:27] In this vision of the future. What do you think is going to separate the great Data scientists from just the good ones?\n\n[00:13:42] What's it mean for you to be a good leader in Data science. And how can an individual contributor embody the characteristics of a good leader without necessarily having the title?\n\n[00:15:27] For someone who's, let's say, the first data scientist in the organization and they're kind of responsible for building up to Data Science practice, what are some of the challenges that you would see them facing and how do you think that they could overcome those challenges?\n\n[00:18:48] What would you say the hero's journey looks like for a Data scientist or anyone in a data related role?\n\n[00:19:31] The importance of story-telling in data science\n\n[00:22:27] Does the way you tell a story differ if you're talking to your manager versus maybe talking to a roomful of executives? Do you have any tips for Data scientists?\n\n[00:25:32] So what are some questions we could ask ourselves when we're starting a project that can really help us clarify exactly what the problem is?\n\n[00:27:35] There is a hidden Data science message in the movie Dr. Strange\n\n[00:28:47] How do you think a Data scientists could develop and cultivate a business acumen or a product sense for themselves?\n\n[00:30:56] The multiplicity of algorithims and the importance of feature engineering\n\n[00:32:25] Can you share some tips or words of encouragement for our listeners who's got like a couple of decades, maybe 10 to 20 years of non Data related experience under their belt and they're now trying to break into Data science? What challenges do you foresee them facing and how can they overcome these challenges?\n\n[00:35:20] What advice or insight can you share with people who are breaking into the field and they look at these job postings and some of them want the abilities of an entire team wrapped up into one person?\n\n[00:39:28] Do you have any suggestions for finance or fintech Data science projects that an aspiring Data scientists could tackle?\n\n[00:42:15] What are some things that we need to be cognizant of and monitor and track once the model is deployed, both from the Data scientists perspective and the business perspective?\n\n[00:44:22] What advice do you have for Data scientists who have who feel like they don't need to learn anymore? What would you have to say today, scientists in that mindset?\n\n[00:46:51] What's the one thing you want people to learn from your story?\n\n[00:48:58] So what are the two five letter words that really grind your gears and why?\n\n[00:49:06] So what is an academic topic or just an area of research or interest outside of Data science that you think every Data scientist should spend more time researching on?\n\n[00:49:27] What is your favorite question to ask during an interview?\n\n[00:51:00] What's the number one book you'd recommend our audience read and your most impactful take away from it?\n\n[00:52:08] If we could somehow get a magic telephone that allowed us to contact 20 year old T. Scott, what would you tell him?\n\n[00:52:42] What is the best advice you have ever received?\n\n[00:53:09] What motivates you?\n\n[00:53:35] What song do you have on Repeat right now?\n\n[00:53:44] How could people connect with you? Where can they find you?Special Guest: T. Scott Clendaniel.","content_html":"On this episode of The Artists of Data Science, we get a chance to hear from T. Scott Clendaniel, a leader in the data science space with over three decades of experience serving in various roles in business, analytics and artificial intelligence. Currently, he's a chief data scientist of the Strategic Artificial Intelligence Lab, where he's aiming to create cutting edge artificial intelligence that can be made accessible to all. He gives insight into the future of A.I, how to be an effective leader, and how to use storytelling in data science.
\n\nScott shares with us his incredible career journey and the insights he has gathered from it. This episode is packed with advice, wisdom, and tips for every data scientist to take something from. It was a great honor interviewing Scott!
\n\nWHAT YOU'LL LEARN
\n\n[7:57] What is an A.I. winter?
\n\n[10:54] Where the field of data science is headed in the next few years?
\n\n[13:58] Tips on being an effective leader
\n\n[20:39] The underrated skill of storytelling, and how to cultivate it
\n\n[32:43] Tips for people that want to break into data science
\n\nQUOTES
\n\n[16:01] “If you're the first data scientist in an organization...make sure that you focus on a crawl, walk, run approach.”
\n\n[17:50] “Simplicity is ridiculously underrated…people do not support what they don't understand. Instead, they fear what they don't understand.”
\n\n[35:03] “Find your why and make sure it's the right why and use that to propel you…”
\n\nSHOW NOTES
\n\n[00:01:35] Introduction for our guest today
\n\n[00:03:33] What drew you to the field and some of the challenges you faced while you're trying to break into and create your own lane in Data science?
\n\n[00:05:00] How much more hyped has I become since he first broke into the field?
\n\n[00:07:39] A brief history of the AI winters we've experienced and why we're on the verge of the next winter
\n\n[00:10:54] Where do you see the field of data science, machine learning, artificial intelligence headed in the next two to five years?
\n\n[00:12:27] In this vision of the future. What do you think is going to separate the great Data scientists from just the good ones?
\n\n[00:13:42] What's it mean for you to be a good leader in Data science. And how can an individual contributor embody the characteristics of a good leader without necessarily having the title?
\n\n[00:15:27] For someone who's, let's say, the first data scientist in the organization and they're kind of responsible for building up to Data Science practice, what are some of the challenges that you would see them facing and how do you think that they could overcome those challenges?
\n\n[00:18:48] What would you say the hero's journey looks like for a Data scientist or anyone in a data related role?
\n\n[00:19:31] The importance of story-telling in data science
\n\n[00:22:27] Does the way you tell a story differ if you're talking to your manager versus maybe talking to a roomful of executives? Do you have any tips for Data scientists?
\n\n[00:25:32] So what are some questions we could ask ourselves when we're starting a project that can really help us clarify exactly what the problem is?
\n\n[00:27:35] There is a hidden Data science message in the movie Dr. Strange
\n\n[00:28:47] How do you think a Data scientists could develop and cultivate a business acumen or a product sense for themselves?
\n\n[00:30:56] The multiplicity of algorithims and the importance of feature engineering
\n\n[00:32:25] Can you share some tips or words of encouragement for our listeners who's got like a couple of decades, maybe 10 to 20 years of non Data related experience under their belt and they're now trying to break into Data science? What challenges do you foresee them facing and how can they overcome these challenges?
\n\n[00:35:20] What advice or insight can you share with people who are breaking into the field and they look at these job postings and some of them want the abilities of an entire team wrapped up into one person?
\n\n[00:39:28] Do you have any suggestions for finance or fintech Data science projects that an aspiring Data scientists could tackle?
\n\n[00:42:15] What are some things that we need to be cognizant of and monitor and track once the model is deployed, both from the Data scientists perspective and the business perspective?
\n\n[00:44:22] What advice do you have for Data scientists who have who feel like they don't need to learn anymore? What would you have to say today, scientists in that mindset?
\n\n[00:46:51] What's the one thing you want people to learn from your story?
\n\n[00:48:58] So what are the two five letter words that really grind your gears and why?
\n\n[00:49:06] So what is an academic topic or just an area of research or interest outside of Data science that you think every Data scientist should spend more time researching on?
\n\n[00:49:27] What is your favorite question to ask during an interview?
\n\n[00:51:00] What's the number one book you'd recommend our audience read and your most impactful take away from it?
\n\n[00:52:08] If we could somehow get a magic telephone that allowed us to contact 20 year old T. Scott, what would you tell him?
\n\n[00:52:42] What is the best advice you have ever received?
\n\n[00:53:09] What motivates you?
\n\n[00:53:35] What song do you have on Repeat right now?
\n\n[00:53:44] How could people connect with you? Where can they find you?
Special Guest: T. Scott Clendaniel.
","summary":"We speak with the always entertaining and informative T. Scott Clendaniel","date_published":"2020-08-20T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/e2688eb3-eea3-4902-9d24-5722174236df.mp3","mime_type":"audio/mpeg","size_in_bytes":29065992,"duration_in_seconds":3296}]},{"id":"bc401ad7-23d8-47d6-b09b-bc6c27ccceb0","title":"Overcoming Imposter Syndrome | Paul McLachlan, PhD","url":"https://theartistsofdatascience.fireside.fm/paul-mclachlan-phd","content_text":"On this episode of The Artists of Data Science, we get a chance to hear from Paul McLachlan, a data scientist who has over a decade of experience applying his knowledge and expertise to academia, corporate businesses, and entrepreneurial endeavours. \n\nHis contributions and expertise have led to numerous startups and nonprofits inviting him to serve as an advisor. He gives insight into how what sparked his interest into the data science field, his tips for beginners in data science, and how he stays motivated.\n\nPaul shares with us his powerful journey from being a high school dropout to getting his PhD in computational social science and becoming the A.I. research leader for the Consumer and Industry Lab at Ericsson Research. This episode is packed with advice, wisdom, and tips that will change your mindset.\n\nWHAT YOU WILL LEARN\n\n[21:37] How A.I. can help fight COVID-19\n[27:15] Extended reality vs. virtual reality\n[32:11] Tips for breaking into data science\n[35:29] Important soft skills for data scientist\n[44:22] Staying motivated in difficult times\n\nQUOTES\n[19:05] \"Data science is really a collective endeavour… even the most skilled and successful data scientist is going to have to be able to successfully work with technical stakeholders, non-technical stakeholders…\"\n\n[34:51] \"…Start from a position of humility…that that can go much further for data scientists than always trying to be the smartest technical person in a conversation…\"\n\n[45:29] \"Having fun and staying connected and staying entertained is actually part of your job responsibilities rather than something that can be set aside.\"\n\nSHOW NOTES\n[00:01:40] Introduction for our guest today\n\n[00:03:38] What sparked your interest in the field of Data Science? Where did you start and how did you get to where you are today?\n\n[00:05:50] How to not be afraid of math and overcome imposter syndrome\n\n[00:07:42] Where do you see the field of Data science machine learning and A.I. headed in the next two to five years?\n\n[00:09:38] What do you think will be the biggest area of concern for the application of A.I. in the next, say, two to five years?\n\n[00:11:22] What do you think will separate the great Data scientists from the good ones?\n\n[00:12:57] Ericcson's involvement with the White House Office of Science and Technology COVID-19 open research dataset challenge using information retrieval and NLP\n\n[00:13:24] What is information retrieval?\n\n[00:14:02] What is Natural Language Processing?\n\n00:14:40] How information retrieval and Natural Language Processing played a role in the innovative solutions that Ericsson data scientists developed for the challenge.\n\n[00:19:31] What the resulting product looked like\n\n[00:20:52] Interesting findings that came from the challenge\n\n[00:24:30] Congratulations on your new role. AI Research Leader for the consumer and industry lab. So can you tell us a little bit about how the consumer and industry lab fits into Ericsson?\n\n[00:26:56] What XR and VR are and share with us what aspects of XR and VR are most interesting to you.\n\n[00:31:45] How to build a culture of data science\n\n[00:35:13] What do you look for in a data scientists beside those those technical skills?\n\n[00:37:47] How to gain industry experience if you don't have any\n\n[00:39:52] How to communicate with executives as a data scientist\n\n[00:42:10] Thought leadership in data science\n\n[00:44:06] Tips to stay motivated when you're feeling down in your learning journey\n\n[00:47:03] What's the one thing you want people to learn from your story?\n\n[00:48:40] The lightning round Special Guest: Paul McLachlan, PhD.","content_html":"On this episode of The Artists of Data Science, we get a chance to hear from Paul McLachlan, a data scientist who has over a decade of experience applying his knowledge and expertise to academia, corporate businesses, and entrepreneurial endeavours.
\n\nHis contributions and expertise have led to numerous startups and nonprofits inviting him to serve as an advisor. He gives insight into how what sparked his interest into the data science field, his tips for beginners in data science, and how he stays motivated.
\n\nPaul shares with us his powerful journey from being a high school dropout to getting his PhD in computational social science and becoming the A.I. research leader for the Consumer and Industry Lab at Ericsson Research. This episode is packed with advice, wisdom, and tips that will change your mindset.
\n\nWHAT YOU WILL LEARN
\n\n[21:37] How A.I. can help fight COVID-19
\n[27:15] Extended reality vs. virtual reality
\n[32:11] Tips for breaking into data science
\n[35:29] Important soft skills for data scientist
\n[44:22] Staying motivated in difficult times
QUOTES
\n[19:05] "Data science is really a collective endeavour… even the most skilled and successful data scientist is going to have to be able to successfully work with technical stakeholders, non-technical stakeholders…"
[34:51] "…Start from a position of humility…that that can go much further for data scientists than always trying to be the smartest technical person in a conversation…"
\n\n[45:29] "Having fun and staying connected and staying entertained is actually part of your job responsibilities rather than something that can be set aside."
\n\nSHOW NOTES
\n[00:01:40] Introduction for our guest today
[00:03:38] What sparked your interest in the field of Data Science? Where did you start and how did you get to where you are today?
\n\n[00:05:50] How to not be afraid of math and overcome imposter syndrome
\n\n[00:07:42] Where do you see the field of Data science machine learning and A.I. headed in the next two to five years?
\n\n[00:09:38] What do you think will be the biggest area of concern for the application of A.I. in the next, say, two to five years?
\n\n[00:11:22] What do you think will separate the great Data scientists from the good ones?
\n\n[00:12:57] Ericcson's involvement with the White House Office of Science and Technology COVID-19 open research dataset challenge using information retrieval and NLP
\n\n[00:13:24] What is information retrieval?
\n\n[00:14:02] What is Natural Language Processing?
\n\n00:14:40] How information retrieval and Natural Language Processing played a role in the innovative solutions that Ericsson data scientists developed for the challenge.
\n\n[00:19:31] What the resulting product looked like
\n\n[00:20:52] Interesting findings that came from the challenge
\n\n[00:24:30] Congratulations on your new role. AI Research Leader for the consumer and industry lab. So can you tell us a little bit about how the consumer and industry lab fits into Ericsson?
\n\n[00:26:56] What XR and VR are and share with us what aspects of XR and VR are most interesting to you.
\n\n[00:31:45] How to build a culture of data science
\n\n[00:35:13] What do you look for in a data scientists beside those those technical skills?
\n\n[00:37:47] How to gain industry experience if you don't have any
\n\n[00:39:52] How to communicate with executives as a data scientist
\n\n[00:42:10] Thought leadership in data science
\n\n[00:44:06] Tips to stay motivated when you're feeling down in your learning journey
\n\n[00:47:03] What's the one thing you want people to learn from your story?
\n\n[00:48:40] The lightning round
Special Guest: Paul McLachlan, PhD.
","summary":"On this episode we speak with Artificial Intelligence Research Leader at Ericcson - Dr. Paul McLachlan. We talk about how he overcame challenges in his academic journey, battled imposter syndrome, and became a leader in AI space.","date_published":"2020-08-17T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/bc401ad7-23d8-47d6-b09b-bc6c27ccceb0.mp3","mime_type":"audio/mpeg","size_in_bytes":32284958,"duration_in_seconds":3486}]},{"id":"6e9e9321-1fbc-48e2-82c0-0d0f7e24dab9","title":"Physics and the Art of Data Science | Santona Tuli, PhD","url":"https://theartistsofdatascience.fireside.fm/santona-tuli-phd","content_text":"On this episode of The Artists of Data Science, we get a chance to hear from Santona Tuli, a physicist and data scientist who has a PhD in physics specializing in nuclear science and quantum chromodynamics. \n\nShe currently leads a team of five doctoral and postdoctoral physicists studying a new plasma phase of matter and the elusive nuclear effects in high energy proton and nucleus collisions at the Large Hadron Collider at CERN in Geneva, Switzerland.\n\nSantona shares with us her journey into data science as a physicist, and her perspective on the future of the field. She also discusses the differences between data science and decision science, tips to break into the field, and advice for women in STEM. \n\nIt was an absolute delight hearing Santona’s advice, and I believe her unique perspectives can help all data scientists! \n\nWHAT YOU'LL LEARN\n[4:46] Where the field of data science is headed\n\n[32:42] Is data science an art or science? \n\n[49:07] Tips for breaking into data science\n\n[57:55] How to get over the perfectionist mindset and feeling like a failure \n\n[1:02:07] Diversity and inclusion of minorities in STEM\n\nQUOTES\n[34:51] “...just being able to step outside and think of alternative approaches, stepping outside the predefined paths. To me, that's how the creative part of my brain is really engaged when I'm doing Data science.”\n\n[39:38] “...the audience should be able to look up at this screen and see themselves reflected in it, being able to understand that the physics that's going on...physics is very much within their reach. Science is very much within their reach.”\n\n[52:40] “...separate or distinguish what the end goal is and the steps that you need to take in order to get there”\n\n[55:21] “get over this idea that it has to be perfect before [you] push it out...What's the worst that can happen? Maybe someone criticizes in some way...But it might turn out that this criticism that you're receiving on it is actually going to help you iterate on that project and make it better.”\n\nWHERE TO FIND SANTONA\n\nLinkedIn: https://www.linkedin.com/in/santona-tuli/\n\nSHOW NOTES\n\n[00:01:21] Introduction for our guest today\n\n[00:02:40] The path into data science\n\n[00:03:20] What the heck is quantum chromodynamics?\n\n[00:03:54] Data science and the study of nuclear forces\n\n[00:04:49] The future of data science\n\n[00:08:17] Data science and empathy\n\n[00:09:27] How to be a great data scientist\n\n[00:10:48] What is CERN?\n\n[00:13:13] What is this Y particle?\n\n[00:15:15] The data science work flow and particle physics\n\n[00:20:25] Data reduction and data bottlenecks\n\n[00:23:43] Selection cuts and rules based clustering\n\n[00:29:43] The importance of feature engineering\n\n[00:32:31] How do you view data science? Do you view it as an art or a science?\n\n[00:34:17] How does the creative process come to life in Data science?\n\n[00:36:39] Santona talks about the IMAX movie that she stars in\n\n[00:40:43] The difference between interpretable and explainable machine learning.\n\n[00:44:22] Decision science and data science\n\n[00:48:49] Words of encouragement for people learning new things\n\n[00:51:04] What does it mean to think like a product manager?\n\n[00:54:14] Break free of the perfectionist mindset\n\n[00:57:00] How to deal with feedback and criticism\n\n[00:58:31] What are some soft skills that you think Data scientists are missing?\n\n[01:01:29] Advice and words of encouragement for the women in our audience who are breaking into tech or currently in tech.\n\n[01:05:48] Santona talks about the impact she hopes to have on young women in STEM\n\n[01:09:08] What can men do, in particular in the Data community, to help foster the inclusion of women in STEM, in tech and Data?\n\n[01:11:28] What's the one thing you want people to learn from your story,\n\n[01:11:57] What's your data science superpower.\n\n[01:12:02] What would you say is the most fundamental truth of physics that all human beings should understand?\n\n[01:12:19] What do you think is the most mysterious aspect of our universe?\n\n[01:12:43] What is an academic topic outside of Data science that you think every data scientist should spend some time researching or studying on.\n\n[01:12:53] What's the number one book? Fiction, nonfiction? Or if you want to pick one of each that you would recommend our audience read. And what was your most impactful takeaway from it?\n\n[01:14:02] If we can somehow get a magical telephone that allowed you to contact 18 year old Santona, what would you tell her?\n\n[01:15:09] What song do you have on repeat.\n\n[01:15:28] How do people connect with you? Where can they find you?Special Guest: Santona Tuli, PhD.","content_html":"On this episode of The Artists of Data Science, we get a chance to hear from Santona Tuli, a physicist and data scientist who has a PhD in physics specializing in nuclear science and quantum chromodynamics.
\n\nShe currently leads a team of five doctoral and postdoctoral physicists studying a new plasma phase of matter and the elusive nuclear effects in high energy proton and nucleus collisions at the Large Hadron Collider at CERN in Geneva, Switzerland.
\n\nSantona shares with us her journey into data science as a physicist, and her perspective on the future of the field. She also discusses the differences between data science and decision science, tips to break into the field, and advice for women in STEM.
\n\nIt was an absolute delight hearing Santona’s advice, and I believe her unique perspectives can help all data scientists!
\n\nWHAT YOU'LL LEARN
\n[4:46] Where the field of data science is headed
[32:42] Is data science an art or science?
\n\n[49:07] Tips for breaking into data science
\n\n[57:55] How to get over the perfectionist mindset and feeling like a failure
\n\n[1:02:07] Diversity and inclusion of minorities in STEM
\n\nQUOTES
\n[34:51] “...just being able to step outside and think of alternative approaches, stepping outside the predefined paths. To me, that's how the creative part of my brain is really engaged when I'm doing Data science.”
[39:38] “...the audience should be able to look up at this screen and see themselves reflected in it, being able to understand that the physics that's going on...physics is very much within their reach. Science is very much within their reach.”
\n\n[52:40] “...separate or distinguish what the end goal is and the steps that you need to take in order to get there”
\n\n[55:21] “get over this idea that it has to be perfect before [you] push it out...What's the worst that can happen? Maybe someone criticizes in some way...But it might turn out that this criticism that you're receiving on it is actually going to help you iterate on that project and make it better.”
\n\nWHERE TO FIND SANTONA
\n\nLinkedIn: https://www.linkedin.com/in/santona-tuli/
\n\nSHOW NOTES
\n\n[00:01:21] Introduction for our guest today
\n\n[00:02:40] The path into data science
\n\n[00:03:20] What the heck is quantum chromodynamics?
\n\n[00:03:54] Data science and the study of nuclear forces
\n\n[00:04:49] The future of data science
\n\n[00:08:17] Data science and empathy
\n\n[00:09:27] How to be a great data scientist
\n\n[00:10:48] What is CERN?
\n\n[00:13:13] What is this Y particle?
\n\n[00:15:15] The data science work flow and particle physics
\n\n[00:20:25] Data reduction and data bottlenecks
\n\n[00:23:43] Selection cuts and rules based clustering
\n\n[00:29:43] The importance of feature engineering
\n\n[00:32:31] How do you view data science? Do you view it as an art or a science?
\n\n[00:34:17] How does the creative process come to life in Data science?
\n\n[00:36:39] Santona talks about the IMAX movie that she stars in
\n\n[00:40:43] The difference between interpretable and explainable machine learning.
\n\n[00:44:22] Decision science and data science
\n\n[00:48:49] Words of encouragement for people learning new things
\n\n[00:51:04] What does it mean to think like a product manager?
\n\n[00:54:14] Break free of the perfectionist mindset
\n\n[00:57:00] How to deal with feedback and criticism
\n\n[00:58:31] What are some soft skills that you think Data scientists are missing?
\n\n[01:01:29] Advice and words of encouragement for the women in our audience who are breaking into tech or currently in tech.
\n\n[01:05:48] Santona talks about the impact she hopes to have on young women in STEM
\n\n[01:09:08] What can men do, in particular in the Data community, to help foster the inclusion of women in STEM, in tech and Data?
\n\n[01:11:28] What's the one thing you want people to learn from your story,
\n\n[01:11:57] What's your data science superpower.
\n\n[01:12:02] What would you say is the most fundamental truth of physics that all human beings should understand?
\n\n[01:12:19] What do you think is the most mysterious aspect of our universe?
\n\n[01:12:43] What is an academic topic outside of Data science that you think every data scientist should spend some time researching or studying on.
\n\n[01:12:53] What's the number one book? Fiction, nonfiction? Or if you want to pick one of each that you would recommend our audience read. And what was your most impactful takeaway from it?
\n\n[01:14:02] If we can somehow get a magical telephone that allowed you to contact 18 year old Santona, what would you tell her?
\n\n[01:15:09] What song do you have on repeat.
\n\n[01:15:28] How do people connect with you? Where can they find you?
Special Guest: Santona Tuli, PhD.
","summary":"","date_published":"2020-08-13T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/6e9e9321-1fbc-48e2-82c0-0d0f7e24dab9.mp3","mime_type":"audio/mpeg","size_in_bytes":47888559,"duration_in_seconds":4588}]},{"id":"e33510d7-d354-4308-9a50-e3309a1605be","title":"Data Science Double Bam | Joshua Starmer","url":"https://theartistsofdatascience.fireside.fm/joshua-starmer-phd","content_text":"On this episode of The Artists of Data Science, we get a chance to hear from Josh Starmer a data scientist who has helped empower learners from all over the globe by breaking down complicated statistics and machine learning topics into small bite sized pieces that are easy to understand.\n\nYou may know Joshua from his youtube channel StatQuest, where he's beloved by his audience of over 320,000 subscribers and 15 million viewers.\n\nJoshua shares with us his powerful journey from being a cellist and music composer to getting his PhD in computational biology and then creating StatQuest.\n\nThis episode is packed with advice, wisdom, and tips for developing a creative process and facing your fears. It was a great honor interviewing Joshua!\n\nWHAT YOU'LL LEARN\n\n[9:05] How music has helped Joshua become more creative\n\n[17:19] Inspiration for StatQuest\n\n[24:00] The most challenging part of creating content\n\n[28:02] The most misunderstood concept from statistics and machine learning\n\n[36:38] How Joshua approaches his creative endeavours\n\nQUOTES\n\n[9:38] \"I pick up my guitar, my ukulele, and I start playing, and my head just completely clears.\"\n\n[19:52] \"what I really want people to take home is that anyone can understand these things [statistics]. Ninety nine times out of 100, the only thing between them and understanding is fancy terminology and fancy notation\"\n\n[23:31] \"It's probably a good thing that I'm a little nervous…because it pushes me just a little harder to make sure that what I'm talking about is correct\"\n\n[33:16] \"…if you want to educate someone…you have to relate with them and you have to see the material from their perspective.\"\n\nFIND JOSHUA ONLINE\n\nLinkedIn: https://www.linkedin.com/in/joshua-starmer-95a554130/\n\nYouTube: https://www.youtube.com/user/joshstarmer\n\nWebsite: https://statquest.org/\n\nSHOW NOTES\n\n[00:01:40] Introduction for our guest\n\n[00:03:13] How Joshua got into statistics\n\n[00:04:12] Where do you see the field of Data science headed in the next two to five years?\n\n[00:05:12] What do you think is gonna separate the great Data scientists from the really good ones?\n\n[00:06:22] Talk to us a bit about what music theory is, what a music theorist does.\n\n[00:08:59] Do you think having a deep understanding of math has helped you be more creative as a musician or vice versa?\n\n[00:11:22] What are some of the commercials and shows that feature your music?\n\n[00:15:32] Joshua describes his process for creating music\n\n[00:17:12] The inspiration of StatQuest\n\n[00:19:27] The StatQuest mission\n\n[00:20:40] Overcoming the resistance when it comes to creating and publishing content\n\n[00:23:53] What's the most challenging part for you when it comes to creating content for the channel?\n\n[00:25:15] What's your personal favorite video from the archives?\n\n[00:26:16] The absolute must watch video from StatQuest\n\n[00:27:53] The most misunderstood statistical concept\n\n[00:30:23] Why you don't need to memorize forumals\n\n[00:32:37] Can you recommend a good book for learning statistics?\n\n[00:34:27] The art and science of data science\n\n[00:36:25] Creativity and data science\n\n[00:38:05] What would you say are the similarities and differences in the creative process for, let's say, writing a research publication, composing music or creating youtube video?\n\n[00:39:38] What's the one thing you want people to learn from your story?\n\n[00:40:47] The lightning round. Special Guest: Joshua Starmer, PhD.","content_html":"On this episode of The Artists of Data Science, we get a chance to hear from Josh Starmer a data scientist who has helped empower learners from all over the globe by breaking down complicated statistics and machine learning topics into small bite sized pieces that are easy to understand.
\n\nYou may know Joshua from his youtube channel StatQuest, where he's beloved by his audience of over 320,000 subscribers and 15 million viewers.
\n\nJoshua shares with us his powerful journey from being a cellist and music composer to getting his PhD in computational biology and then creating StatQuest.
\n\nThis episode is packed with advice, wisdom, and tips for developing a creative process and facing your fears. It was a great honor interviewing Joshua!
\n\nWHAT YOU'LL LEARN
\n\n[9:05] How music has helped Joshua become more creative
\n\n[17:19] Inspiration for StatQuest
\n\n[24:00] The most challenging part of creating content
\n\n[28:02] The most misunderstood concept from statistics and machine learning
\n\n[36:38] How Joshua approaches his creative endeavours
\n\nQUOTES
\n\n[9:38] "I pick up my guitar, my ukulele, and I start playing, and my head just completely clears."
\n\n[19:52] "what I really want people to take home is that anyone can understand these things [statistics]. Ninety nine times out of 100, the only thing between them and understanding is fancy terminology and fancy notation"
\n\n[23:31] "It's probably a good thing that I'm a little nervous…because it pushes me just a little harder to make sure that what I'm talking about is correct"
\n\n[33:16] "…if you want to educate someone…you have to relate with them and you have to see the material from their perspective."
\n\nFIND JOSHUA ONLINE
\n\nLinkedIn: https://www.linkedin.com/in/joshua-starmer-95a554130/
\n\nYouTube: https://www.youtube.com/user/joshstarmer
\n\nWebsite: https://statquest.org/
\n\nSHOW NOTES
\n\n[00:01:40] Introduction for our guest
\n\n[00:03:13] How Joshua got into statistics
\n\n[00:04:12] Where do you see the field of Data science headed in the next two to five years?
\n\n[00:05:12] What do you think is gonna separate the great Data scientists from the really good ones?
\n\n[00:06:22] Talk to us a bit about what music theory is, what a music theorist does.
\n\n[00:08:59] Do you think having a deep understanding of math has helped you be more creative as a musician or vice versa?
\n\n[00:11:22] What are some of the commercials and shows that feature your music?
\n\n[00:15:32] Joshua describes his process for creating music
\n\n[00:17:12] The inspiration of StatQuest
\n\n[00:19:27] The StatQuest mission
\n\n[00:20:40] Overcoming the resistance when it comes to creating and publishing content
\n\n[00:23:53] What's the most challenging part for you when it comes to creating content for the channel?
\n\n[00:25:15] What's your personal favorite video from the archives?
\n\n[00:26:16] The absolute must watch video from StatQuest
\n\n[00:27:53] The most misunderstood statistical concept
\n\n[00:30:23] Why you don't need to memorize forumals
\n\n[00:32:37] Can you recommend a good book for learning statistics?
\n\n[00:34:27] The art and science of data science
\n\n[00:36:25] Creativity and data science
\n\n[00:38:05] What would you say are the similarities and differences in the creative process for, let's say, writing a research publication, composing music or creating youtube video?
\n\n[00:39:38] What's the one thing you want people to learn from your story?
\n\n[00:40:47] The lightning round.
Special Guest: Joshua Starmer, PhD.
","summary":"Today we get an opportunity to speak with the man behind StatQuest - Dr. Joshua Starmer!\r\n\r\nWe learn about his journey into statistics, his creative process, and what it's like creating a StatsQuest video!","date_published":"2020-08-10T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/e33510d7-d354-4308-9a50-e3309a1605be.mp3","mime_type":"audio/mpeg","size_in_bytes":31293497,"duration_in_seconds":3294}]},{"id":"5f721fac-a23e-483b-9916-95f910b56a14","title":"We're All Soldiers in Cyberwarfare | Chase Cunningham, PhD","url":"https://theartistsofdatascience.fireside.fm/chase-cunningham-phd","content_text":"On this episode of The Artists of Data Science, we get a chance to hear from Chase Cunningham, a retired Navy chief cryptologist with nearly two decades of experience in cyber, forensic and analytic operations. He holds both PHD and Masters in Computer Science, and has been named one of Security magazine's most influential people in security for 2019.\n\nChase shares with us the definition of cybersecurity and cyberwarfare, how cyberspace has evolved over the past decade, and the dangers of operating within this space. Chase’s knowledge within cybersecurity will help data scientists identify ways for them to build models that have better real world outcomes and give them insights into a field that impacts our work.\n\nWHAT YOU'LL LEARN\n\n[4:15] What is cyberwarfare and cybersecurity?\n\n[5:33] How does cybersecurity impact data science? \n\n[8:19] The truth about hackers \n\n[16:22] Autonomous vehicles and cybersecurity concerns \n\n[26:20] Ways for data scientists to prevent biases within their models\n\nQUOTES\n\nFIND CHASE ONLINE\n\nLinkedIn: https://www.linkedin.com/in/dr-chase-cunningham-54b26243/\n\nTwitter: https://twitter.com/CynjaChaseC\n\nSHOW NOTES\n[00:01:30] Introduction for our guest today\n\n[00:02:37] Talk to us a bit about your professional journey, how you first heard of cyber security, cyber warfare, and kind of what drew you into that field.\n\n[00:04:06] Can you define what cyber warfare and cyber security are?\n\n[00:05:19] Cyber security and data science\n\n[00:06:01] Cybersecurity, data science, and machine learning\n\n[00:06:52] What are some of the biggest concerns in cyber warfare that we'll face both kind of at individual user level and at the organizational level over the next two to five years?\n\n[00:07:56] Hollywood hackers aren't real like hackers\n\n[00:09:05] How hacking has evolved overtime\n\n[00:10:02] How to practice for cyberwarefare\n\n[00:11:03] How can machine learning help detect or prevent these hacking incidents from occurring?\n\n[00:11:29] Cybersecurity projects\n\n[00:13:01] The Cyber Shot Heard around the world. \n\n[00:14:04] What you mean by kinetic outcomes?\n\n[00:14:33] Modern cybersecurity and kinetic outcomes\n\n[00:15:02] Perimeter based security mode\n\n[00:15:42] Alternative to a perimeter based security\n\n[00:16:09] What does cyber security have to do with autonomous vehicles?\n\n[00:16:50] Cyber security attacks on autonomous vehicles\n\n[00:18:14] How cyber security, social media, and A.I can be used for bad\n\n[00:19:15] How to not be tricked by deep fakes\n\n[00:20:38] Weaponizing biometrics\n\n[00:21:26] Cyber warfare campaigns\n\n[00:22:26] Societal impacts of deep fakes, machine learning, A.I. and cloud computing?\n\n[00:24:18] What the history of warfare can teach us about cyberwarfare\n\n[00:25:04] What happens, when Data and A.I. studies go awry?\n\n[00:26:05] How to prevent bias in machine learning systems\n\n[00:27:01] What do you think would be the equivalent of the nuclear bomb for cyber warfare, cyber security?\n\n[00:27:38] You've got six patents that are credited to you. Which one is your favorite one?\n\n[00:29:05] Why should we kill the password?\n\n[00:29:38] What would be the alternative to passwords?\n\n[00:30:07] What's the one thing you want people to learn from your story?\n\n[00:30:39] The lightning roundSpecial Guest: Chase Cunningham, PhD.","content_html":"On this episode of The Artists of Data Science, we get a chance to hear from Chase Cunningham, a retired Navy chief cryptologist with nearly two decades of experience in cyber, forensic and analytic operations. He holds both PHD and Masters in Computer Science, and has been named one of Security magazine's most influential people in security for 2019.
\n\nChase shares with us the definition of cybersecurity and cyberwarfare, how cyberspace has evolved over the past decade, and the dangers of operating within this space. Chase’s knowledge within cybersecurity will help data scientists identify ways for them to build models that have better real world outcomes and give them insights into a field that impacts our work.
\n\nWHAT YOU'LL LEARN
\n\n[4:15] What is cyberwarfare and cybersecurity?
\n\n[5:33] How does cybersecurity impact data science?
\n\n[8:19] The truth about hackers
\n\n[16:22] Autonomous vehicles and cybersecurity concerns
\n\n[26:20] Ways for data scientists to prevent biases within their models
\n\nQUOTES
\n\nFIND CHASE ONLINE
\n\nLinkedIn: https://www.linkedin.com/in/dr-chase-cunningham-54b26243/
\n\nTwitter: https://twitter.com/CynjaChaseC
\n\nSHOW NOTES
\n[00:01:30] Introduction for our guest today
[00:02:37] Talk to us a bit about your professional journey, how you first heard of cyber security, cyber warfare, and kind of what drew you into that field.
\n\n[00:04:06] Can you define what cyber warfare and cyber security are?
\n\n[00:05:19] Cyber security and data science
\n\n[00:06:01] Cybersecurity, data science, and machine learning
\n\n[00:06:52] What are some of the biggest concerns in cyber warfare that we'll face both kind of at individual user level and at the organizational level over the next two to five years?
\n\n[00:07:56] Hollywood hackers aren't real like hackers
\n\n[00:09:05] How hacking has evolved overtime
\n\n[00:10:02] How to practice for cyberwarefare
\n\n[00:11:03] How can machine learning help detect or prevent these hacking incidents from occurring?
\n\n[00:11:29] Cybersecurity projects
\n\n[00:13:01] The Cyber Shot Heard around the world.
\n\n[00:14:04] What you mean by kinetic outcomes?
\n\n[00:14:33] Modern cybersecurity and kinetic outcomes
\n\n[00:15:02] Perimeter based security mode
\n\n[00:15:42] Alternative to a perimeter based security
\n\n[00:16:09] What does cyber security have to do with autonomous vehicles?
\n\n[00:16:50] Cyber security attacks on autonomous vehicles
\n\n[00:18:14] How cyber security, social media, and A.I can be used for bad
\n\n[00:19:15] How to not be tricked by deep fakes
\n\n[00:20:38] Weaponizing biometrics
\n\n[00:21:26] Cyber warfare campaigns
\n\n[00:22:26] Societal impacts of deep fakes, machine learning, A.I. and cloud computing?
\n\n[00:24:18] What the history of warfare can teach us about cyberwarfare
\n\n[00:25:04] What happens, when Data and A.I. studies go awry?
\n\n[00:26:05] How to prevent bias in machine learning systems
\n\n[00:27:01] What do you think would be the equivalent of the nuclear bomb for cyber warfare, cyber security?
\n\n[00:27:38] You've got six patents that are credited to you. Which one is your favorite one?
\n\n[00:29:05] Why should we kill the password?
\n\n[00:29:38] What would be the alternative to passwords?
\n\n[00:30:07] What's the one thing you want people to learn from your story?
\n\n[00:30:39] The lightning round
Special Guest: Chase Cunningham, PhD.
","summary":"","date_published":"2020-08-06T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/5f721fac-a23e-483b-9916-95f910b56a14.mp3","mime_type":"audio/mpeg","size_in_bytes":19151178,"duration_in_seconds":2045}]},{"id":"42416a57-8ecb-4e66-a77c-625df7ff5315","title":"Flash Statistics | Marco Andreoni","url":"https://theartistsofdatascience.fireside.fm/marco-andreoni","content_text":"On this episode of The Artists of Data Science, we get a chance to hear from Marco Andreoni, a statistician and data scientist who has a master's degree in mathematics and machine learning, as well as a master's degree in mathematics and cryptography.\n\nHe is the lead data scientist at Quantyca, where he covers every part of the data lifecycle from ingestion, storage, analytics, web applications, cloud storage and beyond.\n\nMarco shares with us his passion for teaching other statistics in a more meaningful way. This led him to create Flash statistics, a way to make statistics more accessible to people. Marco brings an interesting perspective into sharing knowledge in a creative way, that all of our listeners should develop to be competitive!\n\nWHAT YOU'LL LEARN\n\n[5:59] Relationship between cryptography and data science\n\n[23:57] What happens when you deploy a model to production\n\n[27:11] The importance of version controlling models\n\n[28:47] The importance of version controlling data\n\n[30:33] Evaluation metrics for post production\n\n[32:00] The importance of creativity\n\n[36:00] Tips on communicating effectively\n\nQUOTES\n\n[21:03] \"You don't need to memorize every single equation…But you must know the underlying idea.\"\n\n[31:23] \"Only if you measure something, you can control something\"\n\n[35:00] \"Focus on the process, the result takes care of itself\"\n\nFIND MARCO ONLINE\n\nLinkedIn: https://www.linkedin.com/in/marcoandreoni1/\n\nWebsite: https://www.flashstatistics.com/\n\nSHOW NOTES\n\n[00:01:24] Introduction for our guest\n\n[00:02:43] Talk to us about your journey, how you first got interested in statistics, machine learning, data science? What drew you to the field?\n\n[00:04:10] Can you give us an overview of what cryptography is? \n\n[00:05:52] How do you see machine learning and cryptography kind of inter playing in the near future?\n\n[00:07:52] GDPR and data science\n\n[00:08:53] Talk to us about the genesis of flash statistics? What was your inspiration for creating it?\n\n[00:09:35] The mission of flash statistics\n\n[00:10:23] Did you feel any type of internal hesitation or a fear with creating the content? And if you did, how did you overcome it?\n\n[00:12:19] The challenge of creating content\n\n[00:13:21] Do you have a personal favorite graphic from the archives?\n\n[00:13:57] Correlation and causation explained via the story of the Stork.\n\n[00:16:20] The one flash statistics painting you need to check out\n\n[00:17:21] What would you say is the most misunderstood concept from statistics and machine learning?\n\n[00:17:51] Would you mind clarifying or demystifying that concept for us?\n\n[00:20:35] Do you think it's important to learn all the formula and equations even though we have advanced software that doesn't work?\n\n[00:21:15] Do you have any tips or any good ways for somebody to learn the underlying idea behind what the software is doing?\n\n[00:22:27] Do you consider Data science and machine learning to be an art or purely hard science? And why?\n\n[00:23:57] What happens when you deploy a model to production\n\n[00:27:11] The importance of version controlling models\n\n[00:28:47] The importance of version controlling data\n\n[00:30:33] Evaluation metrics for post production\n\n[00:31:46] How to be creative\n\n[00:35:57] How to effectively communicate\n\n[00:38:22] The creative process in data science and the artistic process\n\n[00:39:24] What's the one thing you want people to learn from your story?\n\n[00:40:12] The lightning roundSpecial Guest: Marco Andreoni.","content_html":"On this episode of The Artists of Data Science, we get a chance to hear from Marco Andreoni, a statistician and data scientist who has a master's degree in mathematics and machine learning, as well as a master's degree in mathematics and cryptography.
\n\nHe is the lead data scientist at Quantyca, where he covers every part of the data lifecycle from ingestion, storage, analytics, web applications, cloud storage and beyond.
\n\nMarco shares with us his passion for teaching other statistics in a more meaningful way. This led him to create Flash statistics, a way to make statistics more accessible to people. Marco brings an interesting perspective into sharing knowledge in a creative way, that all of our listeners should develop to be competitive!
\n\nWHAT YOU'LL LEARN
\n\n[5:59] Relationship between cryptography and data science
\n\n[23:57] What happens when you deploy a model to production
\n\n[27:11] The importance of version controlling models
\n\n[28:47] The importance of version controlling data
\n\n[30:33] Evaluation metrics for post production
\n\n[32:00] The importance of creativity
\n\n[36:00] Tips on communicating effectively
\n\nQUOTES
\n\n[21:03] "You don't need to memorize every single equation…But you must know the underlying idea."
\n\n[31:23] "Only if you measure something, you can control something"
\n\n[35:00] "Focus on the process, the result takes care of itself"
\n\nFIND MARCO ONLINE
\n\nLinkedIn: https://www.linkedin.com/in/marcoandreoni1/
\n\nWebsite: https://www.flashstatistics.com/
\n\nSHOW NOTES
\n\n[00:01:24] Introduction for our guest
\n\n[00:02:43] Talk to us about your journey, how you first got interested in statistics, machine learning, data science? What drew you to the field?
\n\n[00:04:10] Can you give us an overview of what cryptography is?
\n\n[00:05:52] How do you see machine learning and cryptography kind of inter playing in the near future?
\n\n[00:07:52] GDPR and data science
\n\n[00:08:53] Talk to us about the genesis of flash statistics? What was your inspiration for creating it?
\n\n[00:09:35] The mission of flash statistics
\n\n[00:10:23] Did you feel any type of internal hesitation or a fear with creating the content? And if you did, how did you overcome it?
\n\n[00:12:19] The challenge of creating content
\n\n[00:13:21] Do you have a personal favorite graphic from the archives?
\n\n[00:13:57] Correlation and causation explained via the story of the Stork.
\n\n[00:16:20] The one flash statistics painting you need to check out
\n\n[00:17:21] What would you say is the most misunderstood concept from statistics and machine learning?
\n\n[00:17:51] Would you mind clarifying or demystifying that concept for us?
\n\n[00:20:35] Do you think it's important to learn all the formula and equations even though we have advanced software that doesn't work?
\n\n[00:21:15] Do you have any tips or any good ways for somebody to learn the underlying idea behind what the software is doing?
\n\n[00:22:27] Do you consider Data science and machine learning to be an art or purely hard science? And why?
\n\n[00:23:57] What happens when you deploy a model to production
\n\n[00:27:11] The importance of version controlling models
\n\n[00:28:47] The importance of version controlling data
\n\n[00:30:33] Evaluation metrics for post production
\n\n[00:31:46] How to be creative
\n\n[00:35:57] How to effectively communicate
\n\n[00:38:22] The creative process in data science and the artistic process
\n\n[00:39:24] What's the one thing you want people to learn from your story?
\n\n[00:40:12] The lightning round
Special Guest: Marco Andreoni.
","summary":"On this episode we speak with Marco Andreoni, an Italian data scientist who is also a painter! He's well-known for his work with Flash Statistics, a series of animated infographics covering a wide range of statistics concepts. \r\n\r\nWe talk about his journey into data science, his work with flash staistics, and discuss some things that you should be aware of when you're working in the industry as a data scientist.\r\n","date_published":"2020-08-03T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/42416a57-8ecb-4e66-a77c-625df7ff5315.mp3","mime_type":"audio/mpeg","size_in_bytes":25808381,"duration_in_seconds":2897}]},{"id":"de45bd13-1e41-4b21-8297-833b3c470c1c","title":"The Infinite Retina | Irena Cronin","url":"https://theartistsofdatascience.fireside.fm/irena-cronin","content_text":"On this episode of The Artists of Data Science, we get a chance to hear from Irena Cronin, the co-author of \"The Infinite Retina\".\n\nShe currently serves as CEO of Infinite Retina, an organization which provides research and business strategy to help companies succeed in spatial computing. She gives insight into what sparked her interest into spatial computing, how she sees spatial computing influencing our world, and the potential data problems that will result from more spatial computing technology.\n\nIrena shares with us what led her from leaving her career as an equity research analyst on Wall Street to working with AR/VR and other spatial computing tech. This episode is packed with interesting insights in our future, and I believe anyone listening will have something to ponder on!\n\nWHAT YOU WILL LEARN\n\n[4:09] Spatial computing is all the technology associated with bringing a 3D realm to it's users.\n\n[8:15] Concerns of spatial computing\n\n[17:20]The four technical paradigm shifts\n\n[28:56] Spatial computing and autonomous vehicles shaping our future\n\nQUOTES\n\n[16:59] \"Technology…it's always been a tool for us. But even more so with spatial computing.\"\n\n[43:12] \"I'd say the most important thing you can ever do is to be extremely persistent, no matter what\"\n\n[44:42] \"I think it's extremely important to have professors and the students in a class, …take time to listen to everyone who wants to speak… and not let anyone monopolize that precious time.\"\n\nFIND IRENA ONLINE\n\nInstagram: https://www.instagram.com/infiniteretina/\n\nTwitter: https://twitter.com/IrenaCronin\n\nLinkedIn: https://www.linkedin.com/in/irenacronin/\n\nSHOW NOTES\n[00:01:33] Introduction for our guest today\n\n[00:02:52] How did you get to where you are today? \n\n[00:04:02] What is spatial computing, and how is it different from regular computing?\n\n[00:04:53] In what ways is spatial computing already a part of our daily lives?\n\n[00:06:47] Where is spatial computing technology headed in the next two to five years?\n\n[00:08:06] What do you think are some of the biggest concerns that society will face due to spatial computing technology in the next two to five years?\n\n[00:10:51] What is the prime directive?\n\n[00:13:04] How does spatial computing play into meeting that prime directive?\n\n[00:14:35] How will spatial computing change what it means to be human?\n\n[00:17:07] What is the fourth paradigm?\n\n[00:20:08] What's the intersection between spatial computing and artificial intelligence look like? \n\n[00:21:29] Voice first technology, spatial computing, and the prime directive.\n\n[00:24:42] Can AI create a government for itself? \n\n[00:28:36] How will spatial computing and autonomous vehicles help shape cities of the future?\n\n[00:31:02] Can you talk to us a bit about what Data bubbles are and what they have to do with the cities of the future.\n\n[00:33:24] Concerns that local municipalities are having with the use of this spatial computing technology.\n\n[00:39:39] How spatial computing will change the way we attend live events in a COVID world\n\n[00:42:55] Advice for women who are in STEM fields\n\n[00:44:07] How can we foster the inclusion of women in Data science, in AI, and in STEM?\n\n[00:46:08] What's the one thing you want people to learn from your story?\n\n[00:46:38] The lightning roundSpecial Guest: Irena Cronin.","content_html":"On this episode of The Artists of Data Science, we get a chance to hear from Irena Cronin, the co-author of "The Infinite Retina".
\n\nShe currently serves as CEO of Infinite Retina, an organization which provides research and business strategy to help companies succeed in spatial computing. She gives insight into what sparked her interest into spatial computing, how she sees spatial computing influencing our world, and the potential data problems that will result from more spatial computing technology.
\n\nIrena shares with us what led her from leaving her career as an equity research analyst on Wall Street to working with AR/VR and other spatial computing tech. This episode is packed with interesting insights in our future, and I believe anyone listening will have something to ponder on!
\n\nWHAT YOU WILL LEARN
\n\n[4:09] Spatial computing is all the technology associated with bringing a 3D realm to it's users.
\n\n[8:15] Concerns of spatial computing
\n\n[17:20]The four technical paradigm shifts
\n\n[28:56] Spatial computing and autonomous vehicles shaping our future
\n\nQUOTES
\n\n[16:59] "Technology…it's always been a tool for us. But even more so with spatial computing."
\n\n[43:12] "I'd say the most important thing you can ever do is to be extremely persistent, no matter what"
\n\n[44:42] "I think it's extremely important to have professors and the students in a class, …take time to listen to everyone who wants to speak… and not let anyone monopolize that precious time."
\n\nFIND IRENA ONLINE
\n\nInstagram: https://www.instagram.com/infiniteretina/
\n\nTwitter: https://twitter.com/IrenaCronin
\n\nLinkedIn: https://www.linkedin.com/in/irenacronin/
\n\nSHOW NOTES
\n[00:01:33] Introduction for our guest today
[00:02:52] How did you get to where you are today?
\n\n[00:04:02] What is spatial computing, and how is it different from regular computing?
\n\n[00:04:53] In what ways is spatial computing already a part of our daily lives?
\n\n[00:06:47] Where is spatial computing technology headed in the next two to five years?
\n\n[00:08:06] What do you think are some of the biggest concerns that society will face due to spatial computing technology in the next two to five years?
\n\n[00:10:51] What is the prime directive?
\n\n[00:13:04] How does spatial computing play into meeting that prime directive?
\n\n[00:14:35] How will spatial computing change what it means to be human?
\n\n[00:17:07] What is the fourth paradigm?
\n\n[00:20:08] What's the intersection between spatial computing and artificial intelligence look like?
\n\n[00:21:29] Voice first technology, spatial computing, and the prime directive.
\n\n[00:24:42] Can AI create a government for itself?
\n\n[00:28:36] How will spatial computing and autonomous vehicles help shape cities of the future?
\n\n[00:31:02] Can you talk to us a bit about what Data bubbles are and what they have to do with the cities of the future.
\n\n[00:33:24] Concerns that local municipalities are having with the use of this spatial computing technology.
\n\n[00:39:39] How spatial computing will change the way we attend live events in a COVID world
\n\n[00:42:55] Advice for women who are in STEM fields
\n\n[00:44:07] How can we foster the inclusion of women in Data science, in AI, and in STEM?
\n\n[00:46:08] What's the one thing you want people to learn from your story?
\n\n[00:46:38] The lightning round
Special Guest: Irena Cronin.
","summary":"On this episode we speak to Irena Cronin, author of the book The Infinite Retina and CEO of the company with the same name. We talk about spatial computing, AR/VR/XR and it's intersection with machine learning and AI, we also discuss what the future will look like with this new technology. She also shares some awesome advice and tips for women who are breaking into STEM fields.","date_published":"2020-07-30T09:30:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/de45bd13-1e41-4b21-8297-833b3c470c1c.mp3","mime_type":"audio/mpeg","size_in_bytes":32896231,"duration_in_seconds":3135}]},{"id":"3657554c-14ca-4626-be30-65e4d1781434","title":"Start From The Bottom | Carlos Mercado","url":"https://theartistsofdatascience.fireside.fm/carlos-mercado","content_text":"On this episode of The Artists of Data Science, we get a chance to hear from Carlos Mercado, a\ndata scientist, economist and urban studies enthusiast. \n\nThroughout his career, he's had a diverse range of experience, including time as a freight broker, a year long stint teaching English in Korea and working as a data science freelancer. He's currently a senior data scientist at a global consulting firm.\n\nCarlos shares with us his journey into data science, the importance of building your brand, and\ntips for those who want to break into the field. Carlos is an example of someone who has\nworked hard to learn the fundamentals, and his story shows that it is possible to break into data\nscience!\n\nWHAT YOU'LL LEARN\n\n[5:16] Where is the field heading?\n\n[10:23] Carlos’s background in economics, and how it relates to data science\n\n[23:52] Lessons regarding how to get the job you want\n\n[30:39] How to use reframing and paradoxes for your mindset\n\n[45:24] Advice on building a resume for data science\n\n[51:40] Building your personal brand\n\nQUOTES\n\n[23:12] “...without the history, you’re not going to have context.”\n\n[25:51] “...your resume is a sales document. So if you don't include it in your sale, they're not going to know to buy.”\n\n[29:33} “...the most important part of data science, besides knowing math, is being able to communicate to business people and making sure that they understand...”\n\nFIND CARLOS ONLINE\n\nLinkedIn: https://www.linkedin.com/in/crmercado/\n\nSHOW NOTES\n\n[00:01:36] Introduction of our guest\n\n[00:02:52] Let's talk about how you first heard of Data science and what drew you to the field.\n\n[00:05:12] Where do you see the field headed in the next two to five years?\n\n[00:06:42] How to be a great data scientist\n\n[00:08:31] Natural language process and voice data\n\n[00:10:15] What is economics and why data scientists should care\n\n[00:11:12] Economics and big data\n\n[00:14:11] Bitcoin and Data Science\n\n[00:17:24] What you need to know about GIS, Urban Economics, and Data Science\n\n[00:22:26] Do you have any other resources or articles that are kind of covering that topic that our readers can go check out if they want to learn more?\n\n[00:23:24] Lessons learned in the data science job search process\n\n[00:26:58] What you've learned about Data science working for a psychiatrist at a nonprofit school.\n\n[00:30:20] Reframe and Paradox\n\n[00:34:36] What it's like working as a consulting data scientist\n\n[00:39:09] How does this differ from working in a regular organization?\n\n[00:40:34] Phoenix project and Unicorn Project\n\n[00:41:04] Freelancing as a data scientist\n\n[00:45:15] How to make a good data science resume\n\n[00:49:57] How to make a good data science project\n\n[00:51:33] How to build your data science brand\n\n[00:53:05] The qualities that Carlos looks for in a data scientist \n\n[00:54:06] What's the one thing you want people to learn from your story?\n\n[00:54:49] The lightning roundSpecial Guest: Carlos Mercado.","content_html":"On this episode of The Artists of Data Science, we get a chance to hear from Carlos Mercado, a
\ndata scientist, economist and urban studies enthusiast.
Throughout his career, he's had a diverse range of experience, including time as a freight broker, a year long stint teaching English in Korea and working as a data science freelancer. He's currently a senior data scientist at a global consulting firm.
\n\nCarlos shares with us his journey into data science, the importance of building your brand, and
\ntips for those who want to break into the field. Carlos is an example of someone who has
\nworked hard to learn the fundamentals, and his story shows that it is possible to break into data
\nscience!
WHAT YOU'LL LEARN
\n\n[5:16] Where is the field heading?
\n\n[10:23] Carlos’s background in economics, and how it relates to data science
\n\n[23:52] Lessons regarding how to get the job you want
\n\n[30:39] How to use reframing and paradoxes for your mindset
\n\n[45:24] Advice on building a resume for data science
\n\n[51:40] Building your personal brand
\n\nQUOTES
\n\n[23:12] “...without the history, you’re not going to have context.”
\n\n[25:51] “...your resume is a sales document. So if you don't include it in your sale, they're not going to know to buy.”
\n\n[29:33} “...the most important part of data science, besides knowing math, is being able to communicate to business people and making sure that they understand...”
\n\nFIND CARLOS ONLINE
\n\nLinkedIn: https://www.linkedin.com/in/crmercado/
\n\nSHOW NOTES
\n\n[00:01:36] Introduction of our guest
\n\n[00:02:52] Let's talk about how you first heard of Data science and what drew you to the field.
\n\n[00:05:12] Where do you see the field headed in the next two to five years?
\n\n[00:06:42] How to be a great data scientist
\n\n[00:08:31] Natural language process and voice data
\n\n[00:10:15] What is economics and why data scientists should care
\n\n[00:11:12] Economics and big data
\n\n[00:14:11] Bitcoin and Data Science
\n\n[00:17:24] What you need to know about GIS, Urban Economics, and Data Science
\n\n[00:22:26] Do you have any other resources or articles that are kind of covering that topic that our readers can go check out if they want to learn more?
\n\n[00:23:24] Lessons learned in the data science job search process
\n\n[00:26:58] What you've learned about Data science working for a psychiatrist at a nonprofit school.
\n\n[00:30:20] Reframe and Paradox
\n\n[00:34:36] What it's like working as a consulting data scientist
\n\n[00:39:09] How does this differ from working in a regular organization?
\n\n[00:40:34] Phoenix project and Unicorn Project
\n\n[00:41:04] Freelancing as a data scientist
\n\n[00:45:15] How to make a good data science resume
\n\n[00:49:57] How to make a good data science project
\n\n[00:51:33] How to build your data science brand
\n\n[00:53:05] The qualities that Carlos looks for in a data scientist
\n\n[00:54:06] What's the one thing you want people to learn from your story?
\n\n[00:54:49] The lightning round
Special Guest: Carlos Mercado.
","summary":"","date_published":"2020-07-27T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/3657554c-14ca-4626-be30-65e4d1781434.mp3","mime_type":"audio/mpeg","size_in_bytes":33287494,"duration_in_seconds":3576}]},{"id":"edd69324-19a0-40a2-93aa-0d0c81dacf27","title":"AI Through The Ages | Djamila Amimer, PhD","url":"https://theartistsofdatascience.fireside.fm/djamila-amimer-phd","content_text":"On this episode of The Artists of Data Science, we get a chance to hear from Djimila Amimer, an experienced business leader and entrepreneur with a broad range of experience across multiple domains.\n\nShe is the CEO and founder of Mindsenses Global, a management consultancy specializing in artificial intelligence with a mission to help businesses and organizations apply A.I. and unlock its full potential.\n\nDjimila shares with us her journey into the field of A.I, and some of the concerns she sees the field facing within the next few years, along with the applications of A.I. expanding to other businesses and organizations. \n\nShe also highlights important soft skills everyone should develop, and advice for women in tech. \n\nThis episode is packed with tips from an expert in A.I.!\n\nWHAT YOU'LL LEARN\n\n[8:32] Biggest concerns for data scientists within the next few years\n\n[16:56] Ethical concerns that data scientists should understand with general A.I\n\n[21:24] How A.I. can help in the fight against COVID-19 \n\n[27:10] Djimila’s work with Mindsenses Global\n\n[32:42] Advice on how to become an entrepreneur \n\nQUOTES\n\n[33:17] “...your journey is going to be lonely. So you have to have a lot of resilience to be able to sustain yourself and you grow your business…”\n\n[34:02] “I believe that if you want to do it, if you really, really want to and you believe in it...you will succeed no matter what…”\n\n[35:28] “…you have to be able to adapt to a changing environment.”\n\nFIND DJAMILA ONLINE\n\nLinkedIn: https://www.linkedin.com/in/dr-djamila-amimer-142662137/\n\nTwitter: https://twitter.com/mind_senses\n\nWebsite: https://mindsenses.co.uk/\n\nSHOW NOTES\n\n[00:01:21] Introduction for our guest today\n\n[00:03:15] Talk to us a bit about how you got involved with the field of artificial intelligence, what drew you to the field? \n\n[00:03:48] Where do you see the field of A.I. headed to the next two to five years? What do you think is going to be the next wave of A.I.?\n\n[00:05:09] A historical tour through the three waves of A.I.\n\n[00:07:07] What do you think separates the great Data scientists from the good ones?\n\n[00:08:26] What do you think are going to be some of the biggest concerns that a Data scientist will face in the next two to five years?\n\n[00:10:13] Narrow AI, General AI, and the future of AI\n\n[00:16:43] The ethical concerns Data scientists will face as AI evolves\n\n[00:21:19] How can AI be used to help us fight this Covid-19 pandemic?\n\n[00:24:57] Do you think that we could use AI and machine learning to identify or at least predict the next pandemic?\n\n[00:25:30] Which one of your research works do you think is most relevant to our current times and can you maybe make the connection for us?\n\n[00:27:02] A deep diver into the work that Dr. Amimer does at Mind Sense Global\n\n[00:32:28] Tips for anyone who is thinking of becoming an entrepreneur \n\n[00:33:44] How to cultivate an entrepreneurial mindset\n\n[00:35:32] Data science entrepreneurship opportunities in the COVID world\n\n[00:38:05] The soft skills you need to standout\n\n[00:41:13] How can a student with nothing but a laptop and an Internet connection to use AI for good?\n\n[00:44:22] Advice for women in STEM\n\n[00:46:11] What can the Data community do to foster the inclusion of women in STEM?\n\n[00:48:27] What's the one thing you want people to learn from your story?\n\n[00:49:12] The lightning roundSpecial Guest: Djamila Amimer, PhD.","content_html":"On this episode of The Artists of Data Science, we get a chance to hear from Djimila Amimer, an experienced business leader and entrepreneur with a broad range of experience across multiple domains.
\n\nShe is the CEO and founder of Mindsenses Global, a management consultancy specializing in artificial intelligence with a mission to help businesses and organizations apply A.I. and unlock its full potential.
\n\nDjimila shares with us her journey into the field of A.I, and some of the concerns she sees the field facing within the next few years, along with the applications of A.I. expanding to other businesses and organizations.
\n\nShe also highlights important soft skills everyone should develop, and advice for women in tech.
\n\nThis episode is packed with tips from an expert in A.I.!
\n\nWHAT YOU'LL LEARN
\n\n[8:32] Biggest concerns for data scientists within the next few years
\n\n[16:56] Ethical concerns that data scientists should understand with general A.I
\n\n[21:24] How A.I. can help in the fight against COVID-19
\n\n[27:10] Djimila’s work with Mindsenses Global
\n\n[32:42] Advice on how to become an entrepreneur
\n\nQUOTES
\n\n[33:17] “...your journey is going to be lonely. So you have to have a lot of resilience to be able to sustain yourself and you grow your business…”
\n\n[34:02] “I believe that if you want to do it, if you really, really want to and you believe in it...you will succeed no matter what…”
\n\n[35:28] “…you have to be able to adapt to a changing environment.”
\n\nFIND DJAMILA ONLINE
\n\nLinkedIn: https://www.linkedin.com/in/dr-djamila-amimer-142662137/
\n\nTwitter: https://twitter.com/mind_senses
\n\nWebsite: https://mindsenses.co.uk/
\n\nSHOW NOTES
\n\n[00:01:21] Introduction for our guest today
\n\n[00:03:15] Talk to us a bit about how you got involved with the field of artificial intelligence, what drew you to the field?
\n\n[00:03:48] Where do you see the field of A.I. headed to the next two to five years? What do you think is going to be the next wave of A.I.?
\n\n[00:05:09] A historical tour through the three waves of A.I.
\n\n[00:07:07] What do you think separates the great Data scientists from the good ones?
\n\n[00:08:26] What do you think are going to be some of the biggest concerns that a Data scientist will face in the next two to five years?
\n\n[00:10:13] Narrow AI, General AI, and the future of AI
\n\n[00:16:43] The ethical concerns Data scientists will face as AI evolves
\n\n[00:21:19] How can AI be used to help us fight this Covid-19 pandemic?
\n\n[00:24:57] Do you think that we could use AI and machine learning to identify or at least predict the next pandemic?
\n\n[00:25:30] Which one of your research works do you think is most relevant to our current times and can you maybe make the connection for us?
\n\n[00:27:02] A deep diver into the work that Dr. Amimer does at Mind Sense Global
\n\n[00:32:28] Tips for anyone who is thinking of becoming an entrepreneur
\n\n[00:33:44] How to cultivate an entrepreneurial mindset
\n\n[00:35:32] Data science entrepreneurship opportunities in the COVID world
\n\n[00:38:05] The soft skills you need to standout
\n\n[00:41:13] How can a student with nothing but a laptop and an Internet connection to use AI for good?
\n\n[00:44:22] Advice for women in STEM
\n\n[00:46:11] What can the Data community do to foster the inclusion of women in STEM?
\n\n[00:48:27] What's the one thing you want people to learn from your story?
\n\n[00:49:12] The lightning round
Special Guest: Djamila Amimer, PhD.
","summary":"","date_published":"2020-07-20T09:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/edd69324-19a0-40a2-93aa-0d0c81dacf27.mp3","mime_type":"audio/mpeg","size_in_bytes":39104602,"duration_in_seconds":3330}]},{"id":"bf63feb3-7761-43f2-afac-0c3217acbff7","title":"You ARE Good Enough | Lisa Shiller","url":"https://theartistsofdatascience.fireside.fm/lisa-shiller","content_text":"On this episode of The Artists of Data Science, we get a chance to hear from Lisa Shiller, a mathematician and data scientist who loves dancing, cooking and adventure. She's most passionate about using her skills to make a positive impact, improve people's well-being, create sustainable abundance and decrease our carbon footprint by spreading awareness of sustainability.\n\nLisa shares with us her work at FoodMaestro, the importance of sustainability, interesting findings from her COVID-19 related project, and advice for women in tech. Lisa provides great advice for data scientists on how to impact the culture of their organizations and the importance of being authentic. It was a great pleasure interviewing her!\n\nWHAT YOU'LL LEARN\n\n[6:01] What is sustainability?\n\n[19:52] Lisa’s COVID-19 project in Mexico\n\n[28:19] Challenges in cultivating a data science culture in an organization\n\n[32:41] Important soft skills every data scientist needs\n\n[38:51] Advice for women in tech\n\nQUOTES\n\n[8:38] “...it's all about taking the data that we have, interpreting it and allowing just like everyday people to have access to information to make smarter, healthier decisions.”\n\n[31:22] “I think it's important to... work with other people that are also who they are authentically.”\n\n[36:57] “I don't know everything right now, but I will figure it out. And that's totally OK.”\n\nFIND LISA ONLINE\n\nLinkedIn: https://www.linkedin.com/in/lisa-shiller-a7471551/\n\nInstagram: https://www.instagram.com/lisashiller/\n\nTwitter: https://twitter.com/lisa_shiller\n\nFacebook: https://www.facebook.com/lshiller\n\nWebsite: https://www.lisashiller.com/\n\nSHOW NOTES\n\n[00:01:44] Introduction for our guest\n\n[00:02:58] Lisa’s path into Data science. What sparked her interest? Where did she start? And how did she get to where she is today?\n\n[00:04:08] Talk to us about the work you're doing at FoodMaestro. How are you applying data science to help deliver a better food experience?\n\n[00:05:48] What sustainability means in terms of the work Lisa does\n\n[00:07:15] How will Data science will impact clinical health, wellness, and sustainability even in the next two to five years?\n\n[00:08:48] In what ways do you feel we can leverage data science to help reduce our carbon footprint and promote sustainability?\n\n[00:09:45] In what ways do you think Data science will have a big impact or at least the biggest positive impact on people's food choices in the next two to five years?\n\n[00:12:06] Lisa talks to us about the project she worked on, where she used math and data science to predict COVID-19 in the state of Guanajuato, Mexico.\n\n[00:14:09] Lisa explains what the SEIR model from epidemiology is\n\n[00:15:37] Lisa talks to us about the importance of having good or strong assumptions when undertaking a project?\n\n[00:19:44] Lisa shares what she found to be the most interesting or important finding that she got from this project?\n\n[00:21:54] Lisa defines what herd immunity is\n\n[00:22:54] How do you view data science? Do you view it as an art or as a science?\n\n[00:24:08] How does the creative process manifests itself in mathematics and Data science?\n\n[00:25:28] What do you think are the essentials to lay the foundation on which to build a data science team in your organization?\n\n[00:28:02] Tips for the first data scientist in the organization.\n\n[00:29:45] What is it that you look for in a Data science candidate?\n\n[00:32:14] What are some of these soft skills that candidates are missing that are really in a separate from their competition?\n\n[00:34:30] How to communicate with non-technical audiences\n\n[00:35:32] How to communicate when you don’t know the answer\n\n[00:38:33] Words of encouragement for our women in the audience who are breaking in to or currently in tech.\n\n[00:40:44] Can you talk to us about how you grappled with imposter syndrome and how you overcame that?\n\n[00:43:03] What can the Data community as a whole do to foster inclusion of women in Data science and AI? \n\n[00:44:52] What's the one thing you want people to learn from your story?\n\n[00:45:39] The lightning roundSpecial Guest: Lisa Shiller.","content_html":"On this episode of The Artists of Data Science, we get a chance to hear from Lisa Shiller, a mathematician and data scientist who loves dancing, cooking and adventure. She's most passionate about using her skills to make a positive impact, improve people's well-being, create sustainable abundance and decrease our carbon footprint by spreading awareness of sustainability.
\n\nLisa shares with us her work at FoodMaestro, the importance of sustainability, interesting findings from her COVID-19 related project, and advice for women in tech. Lisa provides great advice for data scientists on how to impact the culture of their organizations and the importance of being authentic. It was a great pleasure interviewing her!
\n\nWHAT YOU'LL LEARN
\n\n[6:01] What is sustainability?
\n\n[19:52] Lisa’s COVID-19 project in Mexico
\n\n[28:19] Challenges in cultivating a data science culture in an organization
\n\n[32:41] Important soft skills every data scientist needs
\n\n[38:51] Advice for women in tech
\n\nQUOTES
\n\n[8:38] “...it's all about taking the data that we have, interpreting it and allowing just like everyday people to have access to information to make smarter, healthier decisions.”
\n\n[31:22] “I think it's important to... work with other people that are also who they are authentically.”
\n\n[36:57] “I don't know everything right now, but I will figure it out. And that's totally OK.”
\n\nFIND LISA ONLINE
\n\nLinkedIn: https://www.linkedin.com/in/lisa-shiller-a7471551/
\n\nInstagram: https://www.instagram.com/lisashiller/
\n\nTwitter: https://twitter.com/lisa_shiller
\n\nFacebook: https://www.facebook.com/lshiller
\n\nWebsite: https://www.lisashiller.com/
\n\nSHOW NOTES
\n\n[00:01:44] Introduction for our guest
\n\n[00:02:58] Lisa’s path into Data science. What sparked her interest? Where did she start? And how did she get to where she is today?
\n\n[00:04:08] Talk to us about the work you're doing at FoodMaestro. How are you applying data science to help deliver a better food experience?
\n\n[00:05:48] What sustainability means in terms of the work Lisa does
\n\n[00:07:15] How will Data science will impact clinical health, wellness, and sustainability even in the next two to five years?
\n\n[00:08:48] In what ways do you feel we can leverage data science to help reduce our carbon footprint and promote sustainability?
\n\n[00:09:45] In what ways do you think Data science will have a big impact or at least the biggest positive impact on people's food choices in the next two to five years?
\n\n[00:12:06] Lisa talks to us about the project she worked on, where she used math and data science to predict COVID-19 in the state of Guanajuato, Mexico.
\n\n[00:14:09] Lisa explains what the SEIR model from epidemiology is
\n\n[00:15:37] Lisa talks to us about the importance of having good or strong assumptions when undertaking a project?
\n\n[00:19:44] Lisa shares what she found to be the most interesting or important finding that she got from this project?
\n\n[00:21:54] Lisa defines what herd immunity is
\n\n[00:22:54] How do you view data science? Do you view it as an art or as a science?
\n\n[00:24:08] How does the creative process manifests itself in mathematics and Data science?
\n\n[00:25:28] What do you think are the essentials to lay the foundation on which to build a data science team in your organization?
\n\n[00:28:02] Tips for the first data scientist in the organization.
\n\n[00:29:45] What is it that you look for in a Data science candidate?
\n\n[00:32:14] What are some of these soft skills that candidates are missing that are really in a separate from their competition?
\n\n[00:34:30] How to communicate with non-technical audiences
\n\n[00:35:32] How to communicate when you don’t know the answer
\n\n[00:38:33] Words of encouragement for our women in the audience who are breaking in to or currently in tech.
\n\n[00:40:44] Can you talk to us about how you grappled with imposter syndrome and how you overcame that?
\n\n[00:43:03] What can the Data community as a whole do to foster inclusion of women in Data science and AI?
\n\n[00:44:52] What's the one thing you want people to learn from your story?
\n\n[00:45:39] The lightning round
Special Guest: Lisa Shiller.
","summary":"On this episode we speak with Lisa Shiller! She's a data scientist based out of Toronto and currently living in Mexico City. We speak about her background in epidemiology (and a deep dive into various epidemiological models), how she got into data science, what it's like to be the first data scientist in an organization, navigating bro culture in tech, and how to promote gal culture in our field!","date_published":"2020-07-13T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/bf63feb3-7761-43f2-afac-0c3217acbff7.mp3","mime_type":"audio/mpeg","size_in_bytes":27372942,"duration_in_seconds":3177}]},{"id":"b3244254-6ad2-4883-adda-8455748a7c29","title":"Pick The Right Voices To Listen To | Brenda Hali","url":"https://theartistsofdatascience.fireside.fm/brenda-hali","content_text":"On this episode of The Artists of Data Science, we get a chance to hear from Brenda Hali, a marketing guru turned data scientist who is passionate about using data to understand causation and to promote company growth. She gives insight into how she broke into the data science field, how marketing and data science are related in some ways, and the struggles she faced when breaking into tech. \n\nBrenda shares with us her transition from marketing into data science, along with the importance of having the representation of women and other minorities in the tech industry. This episode really shows why diversity and inclusion in tech is so important, and how we can all play a role to help others break into the field.\n\nWHAT YOU'LL LEARN\n\n[6:56] What marketers can learn from data scientists \n\n[11:07] Steps to take when beginning a new project\n\n[17:33] How to communicate effectively with your team in the post-COVID world\n\n[20:56] Advice for women and minorities that want to enter into data science \n\nQUOTES\n\n[15:02] “...you need to have communication with your team, and that communication needs to be in one place”\n\n[15:47] “...experiment fast and let things go…”\n\n[23:52] “Be careful with who you listen to, and be careful when those voices are close to you.”\n\nFIND BRENDA ONLINE\n\nLinkedIn: https://www.linkedin.com/in/brenda-hali\n\nInstagram: https://www.instagram.com/datanauti/\n\nTwitter: https://twitter.com/brendahali\n\nMedium: https://medium.com/@brendahalih\n\nSHOW NOTES\n\n[00:01:31] Introduction for our guest today\n\n[00:02:19] Let's talk a little bit about how you first heard of data science and what drew you to the field.\n\n[00:06:16] As someone who is a marketer turned data scientist, what would you say that the data scientist and the marketer can learn from each other?\n\n[00:08:46] How do you see data science impacting marketing and what could the data scientists and the marketer do to best serve each other in this vision of the future that you have?\n\n[00:10:49] What are some of the first things that you do when taking on a new project? And what are some of the steps you take to kind of keep you on track while going through and navigating the ambiguity of a data science project?\n\n[00:12:51] You wrote on a \"Starting Guide to Excel at Teamwork.\" I was wondering if you could talk to us a bit about the importance of teamwork for data scientists. Do you mind sharing the key points from that post with our audience?\n\n[00:17:17] How do you think teamwork will change or be affected in this post-Covid world? What can we do to start being better team members when we're actually not going to be for a while at least some people aren't going to be in the same room, in the same office as their colleagues.\n\n[00:20:38] Do you have any advice or words of encouragement for the women in our audience who are breaking into tech or who are currently in the tech space.\n\n[00:24:11] What can the Data community do to foster the inclusion of women in Data science and A.I?\n\n[00:29:37] What's the one thing you want people to learn from your story?\n\n[00:31:39] How universities, probably will change their business model.\n\n[00:32:27] What is your Data science superpower?\n\n[00:33:03] What's an academic topic outside of Data science that you think Data scientists should spend some time researching on?\n\n[00:33:13] What is the number one book, fiction, non-fiction or both that you would recommend our audience read. And what was your most impactful takeaway from it?\n\n[00:34:09] What's the biggest blunder of bias you've seen or heard of with an algorithm?\n\n[00:34:55] If we can somehow get a magic telephone that allowed you to contact 20 year old Brenda, what would you tell her?\n\n[00:35:43] What's the best advice you have ever received?\n\n[00:36:23] What motivates you?\n\n[00:38:08] What song do you have on repeat?\n\n[00:38:21] How can people connect with you? Where can they find you?Special Guest: Brenda Hali.","content_html":"On this episode of The Artists of Data Science, we get a chance to hear from Brenda Hali, a marketing guru turned data scientist who is passionate about using data to understand causation and to promote company growth. She gives insight into how she broke into the data science field, how marketing and data science are related in some ways, and the struggles she faced when breaking into tech.
\n\nBrenda shares with us her transition from marketing into data science, along with the importance of having the representation of women and other minorities in the tech industry. This episode really shows why diversity and inclusion in tech is so important, and how we can all play a role to help others break into the field.
\n\nWHAT YOU'LL LEARN
\n\n[6:56] What marketers can learn from data scientists
\n\n[11:07] Steps to take when beginning a new project
\n\n[17:33] How to communicate effectively with your team in the post-COVID world
\n\n[20:56] Advice for women and minorities that want to enter into data science
\n\nQUOTES
\n\n[15:02] “...you need to have communication with your team, and that communication needs to be in one place”
\n\n[15:47] “...experiment fast and let things go…”
\n\n[23:52] “Be careful with who you listen to, and be careful when those voices are close to you.”
\n\nFIND BRENDA ONLINE
\n\nLinkedIn: https://www.linkedin.com/in/brenda-hali
\n\nInstagram: https://www.instagram.com/datanauti/
\n\nTwitter: https://twitter.com/brendahali
\n\nMedium: https://medium.com/@brendahalih
\n\nSHOW NOTES
\n\n[00:01:31] Introduction for our guest today
\n\n[00:02:19] Let's talk a little bit about how you first heard of data science and what drew you to the field.
\n\n[00:06:16] As someone who is a marketer turned data scientist, what would you say that the data scientist and the marketer can learn from each other?
\n\n[00:08:46] How do you see data science impacting marketing and what could the data scientists and the marketer do to best serve each other in this vision of the future that you have?
\n\n[00:10:49] What are some of the first things that you do when taking on a new project? And what are some of the steps you take to kind of keep you on track while going through and navigating the ambiguity of a data science project?
\n\n[00:12:51] You wrote on a "Starting Guide to Excel at Teamwork." I was wondering if you could talk to us a bit about the importance of teamwork for data scientists. Do you mind sharing the key points from that post with our audience?
\n\n[00:17:17] How do you think teamwork will change or be affected in this post-Covid world? What can we do to start being better team members when we're actually not going to be for a while at least some people aren't going to be in the same room, in the same office as their colleagues.
\n\n[00:20:38] Do you have any advice or words of encouragement for the women in our audience who are breaking into tech or who are currently in the tech space.
\n\n[00:24:11] What can the Data community do to foster the inclusion of women in Data science and A.I?
\n\n[00:29:37] What's the one thing you want people to learn from your story?
\n\n[00:31:39] How universities, probably will change their business model.
\n\n[00:32:27] What is your Data science superpower?
\n\n[00:33:03] What's an academic topic outside of Data science that you think Data scientists should spend some time researching on?
\n\n[00:33:13] What is the number one book, fiction, non-fiction or both that you would recommend our audience read. And what was your most impactful takeaway from it?
\n\n[00:34:09] What's the biggest blunder of bias you've seen or heard of with an algorithm?
\n\n[00:34:55] If we can somehow get a magic telephone that allowed you to contact 20 year old Brenda, what would you tell her?
\n\n[00:35:43] What's the best advice you have ever received?
\n\n[00:36:23] What motivates you?
\n\n[00:38:08] What song do you have on repeat?
\n\n[00:38:21] How can people connect with you? Where can they find you?
Special Guest: Brenda Hali.
","summary":"On this episode we speak with Brenda Hali and get her perspectice on the intersection of marketing and data science, what marketers and data scientists can learn from each other, and where the future of the field is headed. We also talk about a couple of her blog posts, what it means to be a good team mate, how to handle ambiguity with data science projects and what it's like being a woman in tech. ","date_published":"2020-07-06T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/b3244254-6ad2-4883-adda-8455748a7c29.mp3","mime_type":"audio/mpeg","size_in_bytes":22130564,"duration_in_seconds":2367}]},{"id":"b1709aa5-418e-4b76-98e4-5d72c2dee577","title":"Why We Should Be More Like Winnie The Pooh | Khuyen Tran","url":"https://theartistsofdatascience.fireside.fm/khuyen-tran","content_text":"On this episode of The Artists of Data Science, we get a chance to hear from Khuyen Tran, a student of data science that is currently in pursuit of breaking into the field. She gives insight into how she prioritizes her tasks every day and strategies she uses to take notes and read books.\n\nThis episode gives our listeners a fresh perspective on how to approach the data science field, and some very interesting soft skills that you can implement to step up your game! Khuyen is definitely someone I believe will bring lots of value into the data science field.\n\nWHAT YOU WILL LEARN\n\n[3:23] Ways to boost your efficiency and learning rate\n\n[9:34] What inspired Khuyen to begin writing her posts on data science\n\n[11:42] How to initiate projects in data science\n\n[26:43] Reading books the right way\n\nQUOTES\n\n[4:43] “…maximize important tasks over the urgent but not important tasks…”\n\n[11:25] “…the best way to learn anything is not from taking notes, but from… using it.”\n\n[24:15] “…learn to love whatever you are doing and you will start to do it really well.”\n\nFIND KHUYEN ONLINE\nLinkedIn: https://www.linkedin.com/in/khuyen-tran-1401/\nMedium: https://medium.com/@khuyentran1476\nTwitter: https://twitter.com/KhuyenTran16\nWebsite: http://mathdatasimplified.com/\n\nSHOW NOTES\n[00:01:17] Introduction for our guest\n\n[00:02:28] How did you get interested in Data science and machine learning. What kind of drew you to the field?\n\n[00:03:02] Khuyen talks to us about her struggle to dedicate time for Data science, and shares some of the struggles and strategies that she's used to enable yourself to boost your learning rate and accomplish more.\n\n[00:04:11] Khuyen talks about how she uses the Eisenhower decision matrix to manage her priorities\n\n[00:06:11] How to manage the distactions that could derail you while you're studying\n\n[00:07:17] How to cultivate the right mindset for studying\n\n[00:09:23] Khuyen talks to us about some of the projects she's done and how posting her work on Towards Data Science has helped her\n\n[00:10:55] Khuyen shares her tips for taking notes while studying\n\n[00:11:32] How to come up with ideas for your projects\n\n[00:12:46] How do you find the right Data? How do you organize your thoughts? How do you structure your project? How do you overcome these challenges?\n\n[00:13:51] Tips for networking with experts in the field\n\n[00:14:41] Some tips on how to identfy and use the right resources\n\n[00:16:49] What's your data and analysis discovery process like?\n\n[00:18:18] How to answer questions you don't know the answer to during an interview\n\n[00:21:51] What's the one thing you want people to learn from your story?\n\n[00:22:16] The lightning roundSpecial Guest: Khuyen Tran.","content_html":"On this episode of The Artists of Data Science, we get a chance to hear from Khuyen Tran, a student of data science that is currently in pursuit of breaking into the field. She gives insight into how she prioritizes her tasks every day and strategies she uses to take notes and read books.
\n\nThis episode gives our listeners a fresh perspective on how to approach the data science field, and some very interesting soft skills that you can implement to step up your game! Khuyen is definitely someone I believe will bring lots of value into the data science field.
\n\nWHAT YOU WILL LEARN
\n\n[3:23] Ways to boost your efficiency and learning rate
\n\n[9:34] What inspired Khuyen to begin writing her posts on data science
\n\n[11:42] How to initiate projects in data science
\n\n[26:43] Reading books the right way
\n\nQUOTES
\n\n[4:43] “…maximize important tasks over the urgent but not important tasks…”
\n\n[11:25] “…the best way to learn anything is not from taking notes, but from… using it.”
\n\n[24:15] “…learn to love whatever you are doing and you will start to do it really well.”
\n\nFIND KHUYEN ONLINE
\nLinkedIn: https://www.linkedin.com/in/khuyen-tran-1401/
\nMedium: https://medium.com/@khuyentran1476
\nTwitter: https://twitter.com/KhuyenTran16
\nWebsite: http://mathdatasimplified.com/
SHOW NOTES
\n[00:01:17] Introduction for our guest
[00:02:28] How did you get interested in Data science and machine learning. What kind of drew you to the field?
\n\n[00:03:02] Khuyen talks to us about her struggle to dedicate time for Data science, and shares some of the struggles and strategies that she's used to enable yourself to boost your learning rate and accomplish more.
\n\n[00:04:11] Khuyen talks about how she uses the Eisenhower decision matrix to manage her priorities
\n\n[00:06:11] How to manage the distactions that could derail you while you're studying
\n\n[00:07:17] How to cultivate the right mindset for studying
\n\n[00:09:23] Khuyen talks to us about some of the projects she's done and how posting her work on Towards Data Science has helped her
\n\n[00:10:55] Khuyen shares her tips for taking notes while studying
\n\n[00:11:32] How to come up with ideas for your projects
\n\n[00:12:46] How do you find the right Data? How do you organize your thoughts? How do you structure your project? How do you overcome these challenges?
\n\n[00:13:51] Tips for networking with experts in the field
\n\n[00:14:41] Some tips on how to identfy and use the right resources
\n\n[00:16:49] What's your data and analysis discovery process like?
\n\n[00:18:18] How to answer questions you don't know the answer to during an interview
\n\n[00:21:51] What's the one thing you want people to learn from your story?
\n\n[00:22:16] The lightning round
Special Guest: Khuyen Tran.
","summary":"We talk to an up-and-coming data science, Khuyen Tran! She shares some excellent tips on learning and managing time that all data scientists can learn from!","date_published":"2020-06-29T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/b1709aa5-418e-4b76-98e4-5d72c2dee577.mp3","mime_type":"audio/mpeg","size_in_bytes":16801744,"duration_in_seconds":1866}]},{"id":"ac47c745-d1f1-4e2d-8492-197f989520be","title":"Take a Leap of Faith | Alistair Croll","url":"https://theartistsofdatascience.fireside.fm/alistair-croll","content_text":"On this episode of The Artists of Data Science, we get a chance to hear from Alistair Croll, a well-established entrepreneur, analyst, and author. \n\nHe's the author of Lean Analytics, when we co-wrote with Benjamin Yoskovitz. He's also one of the founders of Coradiant, Year One Labs, and the Strata confersence.\n\nHe shares some excellent tips one how to ask the right questions when working with data, essentials of business communication, and the need to be obsessed as an entrepreneur.\n\nWHAT YOU WILL LEARN:\n\n[28:28] How to be an intrepreneur \n\n[13:39] Incorporate philosophy with data\n\n[19:11] Why you need to be obsessed as an entrepreneur \n\nQUOTES\n\n[14:22] …”as an early stage company, your focus is your biggest currency.”\n[22:10] …”crises have a way of accelerating the inevitable.” \n[46:04] “...you got to first seek to engage and entertain and then you have the ability to inform people.”\n[51:38] …”find a way to capture attention that you can turn into profitable demand better than the competition.”\n\nWHERE TO FIND ALISTAIR ONLINE:\n\nTwitter:https://twitter.com/acroll\nLinkedIn:https://www.linkedin.com/in/alistaircroll/\nWebsite: http://solveforinteresting.com/\n\nSHOW NOTES\n[00:01:37] Introduction for our guest today\n\n[00:03:20] Alistair talks about his early work with Coradiant\n\n[00:05:47] What do you think the next two to five years is going to look like for businesses leveraging data and analytics?\n\n[00:07:55] Why A.I. will need a therapist\n\n[00:08:26] In this new vision of the future then what's really going to separate, like the great data scientists from just the merely good ones?\n\n[00:11:03] The importance of privacy and GDPR for data scientists\n\n[00:13:56] The concept of \"one metric that matters\" and how that's going to manifest in terms ofmeasuring privacy \n\n[00:15:00] Why Zoom DOES NOT deserve to be the videoconferencing platform in the world\n\n[00:17:30] Do you have any advice or tips for anyone who's been toying with the idea of entrepreneurship?\n\n[00:19:22] Why we need to instill leaps of faith in people who want to be founders\n\n[00:21:06] In terms of data science, entrepreneurship in this COVID/post-COVID area. What do you see as some problems with tackling that in enterprising data? Scientists can can identify and then get into an opportunity?\n\n[00:22:29]So you've been writing a lot about innovation at Tilt to the Windmill. How should be incumbents think about innovation?\n\n[00:23:02]A deep dive into various models of innovation\n\n[00:26:38] An excellent and thorough discussion on intrapreneur \n\n[00:30:37] Some great advice for one man data science teams who are on an intrapreneurial journey\n\n[00:33:50] The stages of growth intrapreneur developing data products within their organization will face and how to overcome challenges in those stages\n\n[00:36:50] We get into music science and its intersection with data science\n\n[00:43:13] What's your go to music?\n\n[00:43:54] The important soft-skills that a data scientist needs for success\n\n[00:47:11] What are some key takeaways from your book - Propose, Prepare, Present - that you think a data scientist should apply when communicating with non-technical audiences?\n\n[00:49:27] Let's talk a bit about being evil. You say start-ups should be more evil, that sounds terrible. What are you thinking? What are you trying to communicate with that?\n\n[00:53:23] What's the one thing you want people to learn from your story?\n\n[00:54:24] What's it mean to solve for interesting?\n\n[00:56:00] Jumping into a quick lightning round: What would be the number one book, other fiction or non-fiction or both that you'd recommend our audience read and your most impactful takeaway from it?\n\n[00:57:19] If we could somehow get a magical telephone that allowed you to contact 18 year old Allistair. What would you tell him?\n\n[00:58:45] What it means to cultivate a personality\n\n[01:00:07] What's something you've done at one of your ventures that's been just evil enough?\n\n[01:02:57] What's the best advice you've ever received?\n\n[01:04:58] What motivates you?\n\n[01:06:10]So what song do you currently have on repeat?\n\n[01:06:34] How could people connect with you? Where can they find you?Special Guest: Alistair Croll.","content_html":"On this episode of The Artists of Data Science, we get a chance to hear from Alistair Croll, a well-established entrepreneur, analyst, and author.
\n\nHe's the author of Lean Analytics, when we co-wrote with Benjamin Yoskovitz. He's also one of the founders of Coradiant, Year One Labs, and the Strata confersence.
\n\nHe shares some excellent tips one how to ask the right questions when working with data, essentials of business communication, and the need to be obsessed as an entrepreneur.
\n\nWHAT YOU WILL LEARN:
\n\n[28:28] How to be an intrepreneur
\n\n[13:39] Incorporate philosophy with data
\n\n[19:11] Why you need to be obsessed as an entrepreneur
\n\nQUOTES
\n\n[14:22] …”as an early stage company, your focus is your biggest currency.”
\n[22:10] …”crises have a way of accelerating the inevitable.”
\n[46:04] “...you got to first seek to engage and entertain and then you have the ability to inform people.”
\n[51:38] …”find a way to capture attention that you can turn into profitable demand better than the competition.”
WHERE TO FIND ALISTAIR ONLINE:
\n\nTwitter:https://twitter.com/acroll
\nLinkedIn:https://www.linkedin.com/in/alistaircroll/
\nWebsite: http://solveforinteresting.com/
SHOW NOTES
\n[00:01:37] Introduction for our guest today
[00:03:20] Alistair talks about his early work with Coradiant
\n\n[00:05:47] What do you think the next two to five years is going to look like for businesses leveraging data and analytics?
\n\n[00:07:55] Why A.I. will need a therapist
\n\n[00:08:26] In this new vision of the future then what's really going to separate, like the great data scientists from just the merely good ones?
\n\n[00:11:03] The importance of privacy and GDPR for data scientists
\n\n[00:13:56] The concept of "one metric that matters" and how that's going to manifest in terms ofmeasuring privacy
\n\n[00:15:00] Why Zoom DOES NOT deserve to be the videoconferencing platform in the world
\n\n[00:17:30] Do you have any advice or tips for anyone who's been toying with the idea of entrepreneurship?
\n\n[00:19:22] Why we need to instill leaps of faith in people who want to be founders
\n\n[00:21:06] In terms of data science, entrepreneurship in this COVID/post-COVID area. What do you see as some problems with tackling that in enterprising data? Scientists can can identify and then get into an opportunity?
\n\n[00:22:29]So you've been writing a lot about innovation at Tilt to the Windmill. How should be incumbents think about innovation?
\n\n[00:23:02]A deep dive into various models of innovation
\n\n[00:26:38] An excellent and thorough discussion on intrapreneur
\n\n[00:30:37] Some great advice for one man data science teams who are on an intrapreneurial journey
\n\n[00:33:50] The stages of growth intrapreneur developing data products within their organization will face and how to overcome challenges in those stages
\n\n[00:36:50] We get into music science and its intersection with data science
\n\n[00:43:13] What's your go to music?
\n\n[00:43:54] The important soft-skills that a data scientist needs for success
\n\n[00:47:11] What are some key takeaways from your book - Propose, Prepare, Present - that you think a data scientist should apply when communicating with non-technical audiences?
\n\n[00:49:27] Let's talk a bit about being evil. You say start-ups should be more evil, that sounds terrible. What are you thinking? What are you trying to communicate with that?
\n\n[00:53:23] What's the one thing you want people to learn from your story?
\n\n[00:54:24] What's it mean to solve for interesting?
\n\n[00:56:00] Jumping into a quick lightning round: What would be the number one book, other fiction or non-fiction or both that you'd recommend our audience read and your most impactful takeaway from it?
\n\n[00:57:19] If we could somehow get a magical telephone that allowed you to contact 18 year old Allistair. What would you tell him?
\n\n[00:58:45] What it means to cultivate a personality
\n\n[01:00:07] What's something you've done at one of your ventures that's been just evil enough?
\n\n[01:02:57] What's the best advice you've ever received?
\n\n[01:04:58] What motivates you?
\n\n[01:06:10]So what song do you currently have on repeat?
\n\n[01:06:34] How could people connect with you? Where can they find you?
Special Guest: Alistair Croll.
","summary":"On this episode we get the honour of hearing from Alistair Croll! He's the co-author of the best-selling book Lean Analytics, as well as several other books. He's also a serial entrepreneur who has had success in a number of ventures and stops by the show to talk about how he got into the data world, what the landscape of data science will look like in the near future and shares his insights into the qualities that an entrepreneur or intrapreneur needs to cultivate to be successful.\r\n\r\nJoin the FREE open Slack mastermind community where I'll answer questions and keep you posted on bi-weekly office hours: https://bit.ly/artistsofdatascience ","date_published":"2020-06-22T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/ac47c745-d1f1-4e2d-8492-197f989520be.mp3","mime_type":"audio/mpeg","size_in_bytes":41145338,"duration_in_seconds":4074}]},{"id":"9a83bc07-d052-4353-bfb9-f550689eaca6","title":"How to Use Your Unique Gift and Perspective | Deborah Berebichez, PhD","url":"https://theartistsofdatascience.fireside.fm/debbie-berebichez-phd","content_text":"On this episode of The Artists of Data Science, we get a chance to hear from Deborah Berebichez, a physicist, data scientist, and TV host. Her passion for learning and teaching has led her to become a voice for women and minorities in STEM. \n\nShe gives insight into how she broke into the data science field, how to cultivate the right mindset to succeed, and the importance of diversity and inclusion in tech.\n\nDeborah shares with us how she grew up in a conservative environment, and the obstacles that she had to overcome to become the first Mexican woman to graduate with a physics PhD from Stanford University. \n\nWHAT YOU WILL LEARN\n\n[17:11] What value Deborah believes data science will bring within the next few years\n\n[20:43] Deborah's role model for being curious and inquisitive\n\n[27:42] Actionable tips for cultivating the habit of critical thinking\n\n[40:07] Advice on how to be the hero when you feel like a failure\n\n[51:47] Advice for women that want to break into tech\n\nQUOTES\n[19:57] \"…I think the most amazing things that are going to happen [due to data science] are giving transparency to industries and to communities of people that otherwise in the past have remained quite invisible\"\n\n[24:19] \"I am a very strong supporter of making people learn and educat[ing] others in the basics of science so that we can become empowered citizens and know more about the world.\"\n\n[24:50] \"…Critical thinking to me is about questioning authority…[it] allows us to to gain the proficiency in being able to discard lies from the truth.\"\n\n[28:12] \"…Make sure that you recognized the biases that you have about the world and what you want to be truth so that you don't blind yourself to the actual results of a data analysis\"\n\n[40:59] \"…The people who end up succeeding in life are not the ones for whom things come easily. They are the ones for for whom obstacles are just something to transcend and the ones that get up every time that they experience a failure in their lives and they keep going.\"\n\nFIND DEBORAH BEREBICHEZ ONLINE\n\nLinkedIn: https://www.linkedin.com/in/berebichez/\n\nYouTube: https://www.youtube.com/channel/UCT58Xn45TFrXGIEqRcT_yTg\n\nInstagram: https://www.instagram.com/debbiebere/\n\nTwitter: https://twitter.com/debbiebere\n\nSHOW NOTES\n[00:03:44] The path into data science\n\n[00:07:59] Dr. Berebichez talks about how she got involved with Metis and the work she's doing there.\n\n[00:09:36] What data science will look like in 2-5 years\n\n[00:11:05] The need for different skillsets in data science, from translators to engineers.\n\n[00:12:12] How to be a great data scientist\n\n[00:14:30] What do you think would be the scariest application or the scariest abuse or misuse of data science machine learning in the next two to five years?\n\n[00:16:46] What ways do you think Data science will have the biggest positive impact on society in the next two to five years?\n\n[00:20:34] Dr. Berebichez talks about a historical figure that means a lot to her: Tycho Brahe\n\n[00:24:38] Critical thinking and the data scientist\n\n[00:27:33] Actionable tips to become a better critical thinker\n\n[00:29:33] Why are humans so bad at appreciating or conceptualizing probabilities?\n\n[00:31:09] Why is it important that we cultivate this intuition for what probability represents?\n\n[00:33:53] Is data science an art or science?\n\n[00:36:16] How does the creative process tend to manifest itself in Data science?\n\n[00:38:00] For people out there who are trying to break into data science and maybe they feel like they don't belong or they don't know enough or they aren't smart enough. Do you have any words of encouragement for them?\n\n[00:39:54] So in those moments where we feel like we're failing or failures, we want to give up because it's hard upskilling and learning so much to get into Data science. What can we do to feel like a hero?\n\n[00:41:48] Breaking into data science when you're coming from a non-technical background\n\n[00:44:06] What would you say would be like the biggest myth that people tend to hold in their heads about breaking into Data science? And would you mind debunking that for us?\n\n[00:45:49] The story of Rupesh\n\n[00:49:59] The importance of progress over perfection\n\n[00:51:32] Debbie shares her experience being a woman in tech and provides the women in our audience some advice and encouragement.\n\n[00:53:30] What could the Data community and men in the Data community do to foster inclusion of women in Data science and AI?\n\n[00:55:39] What's the one thing you want people to learn from your story?\n\n[00:56:24] The lightning roundSpecial Guest: Deborah Berebichez, PhD.","content_html":"On this episode of The Artists of Data Science, we get a chance to hear from Deborah Berebichez, a physicist, data scientist, and TV host. Her passion for learning and teaching has led her to become a voice for women and minorities in STEM.
\n\nShe gives insight into how she broke into the data science field, how to cultivate the right mindset to succeed, and the importance of diversity and inclusion in tech.
\n\nDeborah shares with us how she grew up in a conservative environment, and the obstacles that she had to overcome to become the first Mexican woman to graduate with a physics PhD from Stanford University.
\n\nWHAT YOU WILL LEARN
\n\n[17:11] What value Deborah believes data science will bring within the next few years
\n\n[20:43] Deborah's role model for being curious and inquisitive
\n\n[27:42] Actionable tips for cultivating the habit of critical thinking
\n\n[40:07] Advice on how to be the hero when you feel like a failure
\n\n[51:47] Advice for women that want to break into tech
\n\nQUOTES
\n[19:57] "…I think the most amazing things that are going to happen [due to data science] are giving transparency to industries and to communities of people that otherwise in the past have remained quite invisible"
[24:19] "I am a very strong supporter of making people learn and educat[ing] others in the basics of science so that we can become empowered citizens and know more about the world."
\n\n[24:50] "…Critical thinking to me is about questioning authority…[it] allows us to to gain the proficiency in being able to discard lies from the truth."
\n\n[28:12] "…Make sure that you recognized the biases that you have about the world and what you want to be truth so that you don't blind yourself to the actual results of a data analysis"
\n\n[40:59] "…The people who end up succeeding in life are not the ones for whom things come easily. They are the ones for for whom obstacles are just something to transcend and the ones that get up every time that they experience a failure in their lives and they keep going."
\n\nFIND DEBORAH BEREBICHEZ ONLINE
\n\nLinkedIn: https://www.linkedin.com/in/berebichez/
\n\nYouTube: https://www.youtube.com/channel/UCT58Xn45TFrXGIEqRcT_yTg
\n\nInstagram: https://www.instagram.com/debbiebere/
\n\nTwitter: https://twitter.com/debbiebere
\n\nSHOW NOTES
\n[00:03:44] The path into data science
[00:07:59] Dr. Berebichez talks about how she got involved with Metis and the work she's doing there.
\n\n[00:09:36] What data science will look like in 2-5 years
\n\n[00:11:05] The need for different skillsets in data science, from translators to engineers.
\n\n[00:12:12] How to be a great data scientist
\n\n[00:14:30] What do you think would be the scariest application or the scariest abuse or misuse of data science machine learning in the next two to five years?
\n\n[00:16:46] What ways do you think Data science will have the biggest positive impact on society in the next two to five years?
\n\n[00:20:34] Dr. Berebichez talks about a historical figure that means a lot to her: Tycho Brahe
\n\n[00:24:38] Critical thinking and the data scientist
\n\n[00:27:33] Actionable tips to become a better critical thinker
\n\n[00:29:33] Why are humans so bad at appreciating or conceptualizing probabilities?
\n\n[00:31:09] Why is it important that we cultivate this intuition for what probability represents?
\n\n[00:33:53] Is data science an art or science?
\n\n[00:36:16] How does the creative process tend to manifest itself in Data science?
\n\n[00:38:00] For people out there who are trying to break into data science and maybe they feel like they don't belong or they don't know enough or they aren't smart enough. Do you have any words of encouragement for them?
\n\n[00:39:54] So in those moments where we feel like we're failing or failures, we want to give up because it's hard upskilling and learning so much to get into Data science. What can we do to feel like a hero?
\n\n[00:41:48] Breaking into data science when you're coming from a non-technical background
\n\n[00:44:06] What would you say would be like the biggest myth that people tend to hold in their heads about breaking into Data science? And would you mind debunking that for us?
\n\n[00:45:49] The story of Rupesh
\n\n[00:49:59] The importance of progress over perfection
\n\n[00:51:32] Debbie shares her experience being a woman in tech and provides the women in our audience some advice and encouragement.
\n\n[00:53:30] What could the Data community and men in the Data community do to foster inclusion of women in Data science and AI?
\n\n[00:55:39] What's the one thing you want people to learn from your story?
\n\n[00:56:24] The lightning round
Special Guest: Deborah Berebichez, PhD.
","summary":"On this episode we speak with Dr. Deborah Berebichez! Chief Data Scientist at Metis and host of Outrageous Acts of Science and Humanly Impossible. We talk about her path into data science, the struggles she faced in her career, and ","date_published":"2020-06-15T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/9a83bc07-d052-4353-bfb9-f550689eaca6.mp3","mime_type":"audio/mpeg","size_in_bytes":36893788,"duration_in_seconds":4010}]},{"id":"84692f2a-baa6-42ed-b475-f7d41e5a572f","title":"Don't Be Afraid To Build Your Brand | Srivatsan Srinivasan","url":"https://theartistsofdatascience.fireside.fm/srivatsan-srinivasan","content_text":"On this episode of The Artists of Data Science, we get a chance to hear from Srivatsan Srinivasan, a data scientist who has nearly two decades of applying his intense passion for building data driven products.\n\nHe's a strong leader who effectively motivates, mentors, and directs others, and has served as a trusted advisor to senior level executives. He gives insight into how he broke into the data science field, the importance of focusing on business outcomes,, and some important soft skills.\n\nSrivatsan shares with us his tips on how to navigate crazy job descriptions, as well as his methods for communicating with executives. This episode contains actionable advice from someone who has been working with data since the beginning!\n\nWHAT YOU WILL LEARN\n\n[10:26] What it means to be a good leader in data science\n\n[11:45] How to productionize a model\n\n[15:01] Concept Drift\n\n[17:54] How to navigate difficult job descriptions\n\n[20:33] Tips on communicating with executives\n\nQUOTES\n\n[9:09] \"I think more and more data scientists today are technology focused. They need to use technology to just solve a problem…they should focus more on business outcomes.\"\n\n[10:26] \"…a good leader in data science…should be ready to embrace failure\"\n\n[12:21] \"…start with modularizing your code, see where are your common functions that you can use\"\n\nFIND SRIVATSAN ONLINE\n\nLinkedIn: https://www.linkedin.com/in/srivatsan-srinivasan-b8131b/\n\nYouTube: https://www.youtube.com/c/AIEngineeringLife\n\nSHOW NOTES\n\n[00:01:17] Introduction of our guest today\n\n[00:02:58] Let's talk a little bit about how you first heard of data science and what drew you to the field and maybe touch on some of the challenges you faced while breaking into the field.\n\n[00:05:13] You've been so generous with your knowledge and sharing your knowledge, creating some really well crafted content for LinkedIn and YouTube. And I'm wondering what's the inspiration behind that?\n\n[00:06:35] Where do you see the field headed in the next two to five years?\n\n[00:08:41] In this vision of the future, what's going to separate the great data scientists from the ones that are just merely good?\n\n[00:10:08] What does it mean to be a good leader in data science? And how can an individual contributor embody the characteristics of a good leader without necessarily having the title?\n\n[00:11:30] What are some challenges that a a notebook data scientist can face when it comes time to productionize a model? And do you have any tips for how to overcome those hurdles?\n\n[00:12:43] Some actionable tips that you can use today for moving outside of notebooks\n\n[00:13:32] What are some things that we should be keeping track of once we have deployed our model into production?\n\n[00:14:44] A discussion of concept drift and data drift\n\n[00:17:08] Do you have any advice or insight for people that are breaking into the field and they see these job postings that look like they want the abilities of an entire team rolled up into one person and then they they just become scared of applying. Do you have any tips or advice for them?\n\n[00:19:12] What are some soft skills that candidates are missing that are really going to separate them from their competition?\n\n[00:20:23] And do you have any tips for a data scientist who might find themselves having to present to a non-technical audience or perhaps a room full of executives?\n\n[00:21:16] What's the one thing you want people to learn from your story?\n\n[00:22:03] The lightning roundSpecial Guest: Srivatsan Srinivasan.","content_html":"On this episode of The Artists of Data Science, we get a chance to hear from Srivatsan Srinivasan, a data scientist who has nearly two decades of applying his intense passion for building data driven products.
\n\nHe's a strong leader who effectively motivates, mentors, and directs others, and has served as a trusted advisor to senior level executives. He gives insight into how he broke into the data science field, the importance of focusing on business outcomes,, and some important soft skills.
\n\nSrivatsan shares with us his tips on how to navigate crazy job descriptions, as well as his methods for communicating with executives. This episode contains actionable advice from someone who has been working with data since the beginning!
\n\nWHAT YOU WILL LEARN
\n\n[10:26] What it means to be a good leader in data science
\n\n[11:45] How to productionize a model
\n\n[15:01] Concept Drift
\n\n[17:54] How to navigate difficult job descriptions
\n\n[20:33] Tips on communicating with executives
\n\nQUOTES
\n\n[9:09] "I think more and more data scientists today are technology focused. They need to use technology to just solve a problem…they should focus more on business outcomes."
\n\n[10:26] "…a good leader in data science…should be ready to embrace failure"
\n\n[12:21] "…start with modularizing your code, see where are your common functions that you can use"
\n\nFIND SRIVATSAN ONLINE
\n\nLinkedIn: https://www.linkedin.com/in/srivatsan-srinivasan-b8131b/
\n\nYouTube: https://www.youtube.com/c/AIEngineeringLife
\n\nSHOW NOTES
\n\n[00:01:17] Introduction of our guest today
\n\n[00:02:58] Let's talk a little bit about how you first heard of data science and what drew you to the field and maybe touch on some of the challenges you faced while breaking into the field.
\n\n[00:05:13] You've been so generous with your knowledge and sharing your knowledge, creating some really well crafted content for LinkedIn and YouTube. And I'm wondering what's the inspiration behind that?
\n\n[00:06:35] Where do you see the field headed in the next two to five years?
\n\n[00:08:41] In this vision of the future, what's going to separate the great data scientists from the ones that are just merely good?
\n\n[00:10:08] What does it mean to be a good leader in data science? And how can an individual contributor embody the characteristics of a good leader without necessarily having the title?
\n\n[00:11:30] What are some challenges that a a notebook data scientist can face when it comes time to productionize a model? And do you have any tips for how to overcome those hurdles?
\n\n[00:12:43] Some actionable tips that you can use today for moving outside of notebooks
\n\n[00:13:32] What are some things that we should be keeping track of once we have deployed our model into production?
\n\n[00:14:44] A discussion of concept drift and data drift
\n\n[00:17:08] Do you have any advice or insight for people that are breaking into the field and they see these job postings that look like they want the abilities of an entire team rolled up into one person and then they they just become scared of applying. Do you have any tips or advice for them?
\n\n[00:19:12] What are some soft skills that candidates are missing that are really going to separate them from their competition?
\n\n[00:20:23] And do you have any tips for a data scientist who might find themselves having to present to a non-technical audience or perhaps a room full of executives?
\n\n[00:21:16] What's the one thing you want people to learn from your story?
\n\n[00:22:03] The lightning round
Special Guest: Srivatsan Srinivasan.
","summary":"In this episode we speak with an AI influencer and content creator - Srivatsan Srinivasan! We talk about the journey he took into data science, some of the struggles he faced along the way, and he shares some great wisdom and tips for data scientists! -----\r\nJust a heads up - the audio quality of this episode is sub-par due to network issues on my end. The transcript was manually done by me, so you can always refer to that if parts are unclear. Thanks for your flexibility! Apologies on the audio quality for this episode - I did my best to fix them. If you can get past some of the issues, you will learn a lot from this man!\r\nFollow the show on Instagram @theartistsofdatascience, on Twitter @ArtistsOfData, on Facebook @TheArtistsOfDataScience, and on LinkedIn too!\r\n\r\n","date_published":"2020-06-08T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/84692f2a-baa6-42ed-b475-f7d41e5a572f.mp3","mime_type":"audio/mpeg","size_in_bytes":15957410,"duration_in_seconds":1598}]},{"id":"bb49fb5c-7e3d-4e18-b78a-0a2ccc1bac86","title":"The Monsters in Your Head | Brandon Quach, PhD","url":"https://theartistsofdatascience.fireside.fm/brandon-quach","content_text":"On this episode of The Artists of Data Science, we get a chance to hear from Brandon Quach, a data scientist who has a PhD in bioengineering, and has worked on threat analysis for security and business ecosystems. He's currently a principal data scientist and manager, leading the charge to modernize the customer experience by applying machine learning to customer support.\n\nBrandon shares his perspective on how data scientists should approach problems, the importance of passing on knowledge, how to be a leader in the data workspace, and the appropriate mindset to develop when faced with difficult problems. Speaking with him was an honor, and this episode has something for everyone to take away from.\n\nWHAT YOU'LL LEARN\n\n[11:50] Brandon discusses automation and whether or not we will be able to automate human judgement\n\n[18:01] What qualities do you need to become an intrapreneur in your organization\n\n[22:19] A unique way to approach leadership in your organization\n\n[30:08] Why great thinkers abhor being told what to do \n\n[37:37] How important is agile and scrum methodology in data science\n\n[46:13] The mindset you need to accept the monsters in your life\n\nQUOTES\n\n[22:37] “...trust, to me, comes from your ability to not be scared of the results that come out of your work or anything that you do.”\n\n[27:25] …”If I received good advice and….good guidance, then I feel it's sort of my job, my duty, to pass it on to the next generation”\n\n[30:08] “Great thinkers like to figure things out and come to a point that they believe in the solution.”\n\n[35:33] “I want people to look back long after I've gone and say...that decision that was made early on that nobody had appreciated...turned out to be really critical down the road…”\n\n[53:33] “...successful data scientists can think through any kind of problem surrounding data science, not just the core problem.”\n\n[57:05] “You should learn how to think through code. How can you learn how to think through code?. Well, either you have a built in imagination... and/or you have gone through a lot of iterations of code and you can understand the process...”\n\nFIND BRANDON ONLINE\n\nTwitter: https://twitter.com/databrandon\n\nLinkedin: https://www.linkedin.com/in/bquach/\n\nWebsite: https://databrandon.com/\n\nSHOW NOTES\n\n[00:01:16] The introduction for our guest today\n\n[00:03:52] Brandon's journey from academia to industry\n\n[00:06:13] What were some of the the struggles and challenges he faced during your journey?\n\n[00:09:48] Things are never as simple as they seem in data science\n\n[00:11:41] The future of data science\n\n[00:12:06] The automation of data science workflows\n\n[00:13:58] The automation of human judgment and human creativity in problem solving\n\n[00:15:45] What separates the great data scientists from the good ones\n\n[00:17:01] Why a lot of data scientists tend to have PhDs\n\n[00:18:01] What is an intrapreneur?\n\n[00:21:56] A leadership philosophy for data science\n\n[00:27:40] Great advice for data scientists new to the career\n\n[00:29:34] Why you should never tell a data scientist what to do\n\n[00:32:25] Autonomy and mastery lead to purpose for data scientists\n\n[00:33:42] The mindset of future judgement\n\n[00:37:25] Agile and scrum in data science\n\n[00:42:34] Grit, Mindset, and Drive for data scientists\n\n[00:43:55] Dealing with data science stakeholders and handling machine learning setbacks\n\n[00:47:25] Imposter syndrome in data science\n\n[00:50:31] Soft skills for data scientists\n\n[00:51:56] Brandon talks about some interesting interview questions he asks to assess a candidates thought process\n\n[00:54:54] How to deepen your intuition and knowledge of data science\n\n[00:58:08] What's the one thing you want people to learn from your story?\n\n[00:58:56] The lightning roundSpecial Guest: Brandon Quach, PhD.","content_html":"On this episode of The Artists of Data Science, we get a chance to hear from Brandon Quach, a data scientist who has a PhD in bioengineering, and has worked on threat analysis for security and business ecosystems. He's currently a principal data scientist and manager, leading the charge to modernize the customer experience by applying machine learning to customer support.
\n\nBrandon shares his perspective on how data scientists should approach problems, the importance of passing on knowledge, how to be a leader in the data workspace, and the appropriate mindset to develop when faced with difficult problems. Speaking with him was an honor, and this episode has something for everyone to take away from.
\n\nWHAT YOU'LL LEARN
\n\n[11:50] Brandon discusses automation and whether or not we will be able to automate human judgement
\n\n[18:01] What qualities do you need to become an intrapreneur in your organization
\n\n[22:19] A unique way to approach leadership in your organization
\n\n[30:08] Why great thinkers abhor being told what to do
\n\n[37:37] How important is agile and scrum methodology in data science
\n\n[46:13] The mindset you need to accept the monsters in your life
\n\nQUOTES
\n\n[22:37] “...trust, to me, comes from your ability to not be scared of the results that come out of your work or anything that you do.”
\n\n[27:25] …”If I received good advice and….good guidance, then I feel it's sort of my job, my duty, to pass it on to the next generation”
\n\n[30:08] “Great thinkers like to figure things out and come to a point that they believe in the solution.”
\n\n[35:33] “I want people to look back long after I've gone and say...that decision that was made early on that nobody had appreciated...turned out to be really critical down the road…”
\n\n[53:33] “...successful data scientists can think through any kind of problem surrounding data science, not just the core problem.”
\n\n[57:05] “You should learn how to think through code. How can you learn how to think through code?. Well, either you have a built in imagination... and/or you have gone through a lot of iterations of code and you can understand the process...”
\n\nFIND BRANDON ONLINE
\n\nTwitter: https://twitter.com/databrandon
\n\nLinkedin: https://www.linkedin.com/in/bquach/
\n\nWebsite: https://databrandon.com/
\n\nSHOW NOTES
\n\n[00:01:16] The introduction for our guest today
\n\n[00:03:52] Brandon's journey from academia to industry
\n\n[00:06:13] What were some of the the struggles and challenges he faced during your journey?
\n\n[00:09:48] Things are never as simple as they seem in data science
\n\n[00:11:41] The future of data science
\n\n[00:12:06] The automation of data science workflows
\n\n[00:13:58] The automation of human judgment and human creativity in problem solving
\n\n[00:15:45] What separates the great data scientists from the good ones
\n\n[00:17:01] Why a lot of data scientists tend to have PhDs
\n\n[00:18:01] What is an intrapreneur?
\n\n[00:21:56] A leadership philosophy for data science
\n\n[00:27:40] Great advice for data scientists new to the career
\n\n[00:29:34] Why you should never tell a data scientist what to do
\n\n[00:32:25] Autonomy and mastery lead to purpose for data scientists
\n\n[00:33:42] The mindset of future judgement
\n\n[00:37:25] Agile and scrum in data science
\n\n[00:42:34] Grit, Mindset, and Drive for data scientists
\n\n[00:43:55] Dealing with data science stakeholders and handling machine learning setbacks
\n\n[00:47:25] Imposter syndrome in data science
\n\n[00:50:31] Soft skills for data scientists
\n\n[00:51:56] Brandon talks about some interesting interview questions he asks to assess a candidates thought process
\n\n[00:54:54] How to deepen your intuition and knowledge of data science
\n\n[00:58:08] What's the one thing you want people to learn from your story?
\n\n[00:58:56] The lightning round
Special Guest: Brandon Quach, PhD.
","summary":"On this episode we talk to Dr. Brandon Quach and he shares with us his leadership philosophy, why great thinkers (like data scientists) should hate being told what to do, the mindset of future judgement, and how to deal with the monsters in our head so that we can achieve our full potential.\r\n\r\nFollow the show on Instagram @theartistsofdatascience, on Twitter @ArtistsOfData, on Facebook @TheArtistsOfDataScience, and on LinkedIn too!\r\n\r\n","date_published":"2020-06-01T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/bb49fb5c-7e3d-4e18-b78a-0a2ccc1bac86.mp3","mime_type":"audio/mpeg","size_in_bytes":34600034,"duration_in_seconds":4196}]},{"id":"027027ac-4901-4a24-96d5-68a2c00dfa48","title":"Skepticism is NOT a Denial Activity | Kyle Polich","url":"https://theartistsofdatascience.fireside.fm/kyle-polich","content_text":"On this episode of The Artists of Data Science, we get a chance to hear from Kyle Polich, a computer scientist turned data skeptic. He has a wide array of interests and skills in A.I, machine learning, and statistics. \n\nThese skills have made him a sought after consultant in the data science field. He is also the host of the very popular data podcast, Data Skeptic, which discusses topics related to data science all from the perspective of applying critical thinking and the scientific method to evaluate the veracity of claims and efficacy of approaches.\n\nIn this episode, Kyle defines what a data skeptic is, and also goes on to give advice on how to communicate effectively with leaders and executives as a data scientist. Kyle brings a very unique perspective related to all things data, along with actionable advice!\n\nWHAT YOU WILL LEARN\n\n[00:11:49] Probabilistic data structures\n\n[00:15:19] How probabilitistic data structures will change the future\n\n[18:55] Is data science more of an art or science?\n\n[23:36] Advice for data scientists trapped in a perfectionist mindset\n\n[30:43] Important soft skills that you need to succeed\n\n[39:40] How to communicate your ideas with executives\n\nQUOTES\n\n[11:43] \"…greatness is achieved by a commitment to your craft and pursuing it.\"\n\n[16:42] \"The greatest trick the devil ever pulled was convincing the world he didn't exist. That's what good data science does to me.\"\n\n[24:42] …\"being able to fall down but get up fast is important.\"\n\nFIND KYLE ONLINE\nLinkedIn:https://www.linkedin.com/in/kyle-polich-5047193/\n\nTwitter:https://twitter.com/DataSkeptic\n\nPodcast:https://dataskeptic.com/\n\nSHOW NOTES\n\n[00:03:01] How Kyle got into data science\n\n[00:05:20] What the heck is a data skeptic?\n\n[00:07:47] What do you think the next big thing in data science is going to be the next, say, two to five years.\n\n[00:11:04] How to be a great data scientist\n\n[00:11:49] Kyle gives us a primer on probabilistic data structures\n\n[00:15:19] How do you see probabilistic data structures impacting society in the next two to five years?\n\n[00:17:19] Data skeptic mission\n\n[00:18:39] Kyle answers the question - how do you view data science? Do you think it's more of the art or more science?\n\n[00:21:09] We talk about principles and methodologies as it related to art and science\n\n[00:21:52] Kyle shares his thoughts on the creative process in data science\n\n[00:23:22] Kyle shares his thoughts on being a perfectionist when you're working on a project\n\n[00:25:28] Do you have any tips for people who are coming from a non-technical background and they're coming up to these technical concepts face to face for the first time?\n\n[00:26:42] We talk about the importance of being a lifelong learner and Kyle shares some advice for aspiring data scientists out there who feel like they haven't learned enough yet to even consider breaking into the field.\n\n[00:28:47] What is your advice for data scientists who they feel like they've learned enough, and just don't even need to learn anything else to be successful?\n\n[00:30:27] We talk about the soft-skils that candidates should pick-up, and Kyle shares advice for people who are in their first data science roles.\n\n[00:31:03] Some insight into the purpose of your resume and how you should leverage that for your job search\n\n[00:34:17] We talk about the pursuit of certificates versus the achievement of self-directed learning projects\n\n[00:36:18] Tips on finding the right type of project to add to your portfolio\n\n[00:39:13] For those people a little further along in their career, Kyle shares tips on how to effectively communicate with executives\n\n[00:42:16] We talk about our shared love for Bill Murray\n\n[00:43:06] How you should respond when you come across job postings that look like they want the skills of an entire team rolled up into one person.\n\n[00:46:22] What's the one thing you want people to learn from your story?\n\n[00:47:19] The lightning round. Special Guest: Kyle Polich.","content_html":"On this episode of The Artists of Data Science, we get a chance to hear from Kyle Polich, a computer scientist turned data skeptic. He has a wide array of interests and skills in A.I, machine learning, and statistics.
\n\nThese skills have made him a sought after consultant in the data science field. He is also the host of the very popular data podcast, Data Skeptic, which discusses topics related to data science all from the perspective of applying critical thinking and the scientific method to evaluate the veracity of claims and efficacy of approaches.
\n\nIn this episode, Kyle defines what a data skeptic is, and also goes on to give advice on how to communicate effectively with leaders and executives as a data scientist. Kyle brings a very unique perspective related to all things data, along with actionable advice!
\n\nWHAT YOU WILL LEARN
\n\n[00:11:49] Probabilistic data structures
\n\n[00:15:19] How probabilitistic data structures will change the future
\n\n[18:55] Is data science more of an art or science?
\n\n[23:36] Advice for data scientists trapped in a perfectionist mindset
\n\n[30:43] Important soft skills that you need to succeed
\n\n[39:40] How to communicate your ideas with executives
\n\nQUOTES
\n\n[11:43] "…greatness is achieved by a commitment to your craft and pursuing it."
\n\n[16:42] "The greatest trick the devil ever pulled was convincing the world he didn't exist. That's what good data science does to me."
\n\n[24:42] …"being able to fall down but get up fast is important."
\n\nFIND KYLE ONLINE
\nLinkedIn:https://www.linkedin.com/in/kyle-polich-5047193/
Twitter:https://twitter.com/DataSkeptic
\n\nPodcast:https://dataskeptic.com/
\n\nSHOW NOTES
\n\n[00:03:01] How Kyle got into data science
\n\n[00:05:20] What the heck is a data skeptic?
\n\n[00:07:47] What do you think the next big thing in data science is going to be the next, say, two to five years.
\n\n[00:11:04] How to be a great data scientist
\n\n[00:11:49] Kyle gives us a primer on probabilistic data structures
\n\n[00:15:19] How do you see probabilistic data structures impacting society in the next two to five years?
\n\n[00:17:19] Data skeptic mission
\n\n[00:18:39] Kyle answers the question - how do you view data science? Do you think it's more of the art or more science?
\n\n[00:21:09] We talk about principles and methodologies as it related to art and science
\n\n[00:21:52] Kyle shares his thoughts on the creative process in data science
\n\n[00:23:22] Kyle shares his thoughts on being a perfectionist when you're working on a project
\n\n[00:25:28] Do you have any tips for people who are coming from a non-technical background and they're coming up to these technical concepts face to face for the first time?
\n\n[00:26:42] We talk about the importance of being a lifelong learner and Kyle shares some advice for aspiring data scientists out there who feel like they haven't learned enough yet to even consider breaking into the field.
\n\n[00:28:47] What is your advice for data scientists who they feel like they've learned enough, and just don't even need to learn anything else to be successful?
\n\n[00:30:27] We talk about the soft-skils that candidates should pick-up, and Kyle shares advice for people who are in their first data science roles.
\n\n[00:31:03] Some insight into the purpose of your resume and how you should leverage that for your job search
\n\n[00:34:17] We talk about the pursuit of certificates versus the achievement of self-directed learning projects
\n\n[00:36:18] Tips on finding the right type of project to add to your portfolio
\n\n[00:39:13] For those people a little further along in their career, Kyle shares tips on how to effectively communicate with executives
\n\n[00:42:16] We talk about our shared love for Bill Murray
\n\n[00:43:06] How you should respond when you come across job postings that look like they want the skills of an entire team rolled up into one person.
\n\n[00:46:22] What's the one thing you want people to learn from your story?
\n\n[00:47:19] The lightning round.
Special Guest: Kyle Polich.
","summary":"On this episode we get an opportunity to hear from Kyle Polich, host of the Data Skeptic Podcast. We discuss his journey into data science, what he's been currently researching, where he thinks data science is headed, tips on communicating with a wide variety of audiences, and advice for breaking into the field of data science.\r\n\r\nJoin the FREE open Slack mastermind community where I'll answer questions and keep you posted on bi-weekly office hours: https://bit.ly/artistsofdatascience\r\n\r\n","date_published":"2020-05-25T06:30:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/027027ac-4901-4a24-96d5-68a2c00dfa48.mp3","mime_type":"audio/mpeg","size_in_bytes":36227919,"duration_in_seconds":3210}]},{"id":"66637d63-f623-4a37-ba9d-b0d14aeb5f46","title":"How to Whisper to Data (and Executives) | Scott Taylor","url":"https://theartistsofdatascience.fireside.fm/the-data-whisperer","content_text":"On this episode of The Artists of Data Science, we get a chance to hear from Scott Taylor, also known as the \"Data Whisperer.\"\n\nHe has spread the gospel of digital transformation through public speaking engagements, blogs, videos, white papers, podcasts, puppet shows, cartoons and all forms of verbal and written communications. He has also helped organizations, such as Microsoft and Nielsen, comb through and organize their data for meaningful use.\n\nScott shares his \"eight 'ates of master data\", a set of rules to engage with master data in a meaningful way. He also goes over his tips for communicating with executives, along with important soft skills that are being overlooked by data scientists.\n\nScott is very articulate, and his passion for data and teaching are definitely evident in this episode!\n\nWHAT YOU WILL LEARN\n\n[12:57] The eight 'ates of master data management\n\n[17:04] Data science communication with executives\n\n[21:45] Legacy data systems\n\nQUOTES\n[3:37] \"It's not all about building. Sometimes it's about making sure things are structured and organized the right way.\"\n\n[7:11] \"Hardware comes and goes. Software comes and goes. Data always remains.\"\n\n[16:11] \"Data, to have value, has got to be in motion.\"\n\n[20:36] \"If you're a data scientist, you are the business….and it's impossible for you to learn too much about your own business.\"\n\n[27:08] \"…you've got to bring people from \"I have no idea what you're talking about\" to \"how can we live without this?\" and that comes from telling a good story.\"\n\nWHERE TO FIND SCOTT ONLINE\n\nLinkedIn: https://www.linkedin.com/in/scottmztaylor/\n\nTwitter: https://twitter.com/stdatawhisperer\n\nWebsite: https://www.metametaconsulting.com/\n\nSHOW NOTES\n[00:01:20] The introduction for our guest today\n\n[00:02:54] Scott talk to us a bit about his professional journey, how he got involved in the data world. And what drew him to this field?\n\n[00:04:40] Scott talks to us about some of the early gigs he had in the data space. \n\n[00:05:54] Where do you see kind of the field of big data and digital transformation? What's this landscape going to look like in two to five years?\n\n[00:07:41] Scott talks about how the stakes are changing and how data management is unavoidable\n\n[00:08:32] Scott goes more in-depth as to how the stakes are changing and how he's seen it play out across enterprise organizations.\n\n[00:09:56] In this vision of the future where the stakes are changing, what do you think is going to separate the great data professionals from the merely good ones?\n\n[00:11:25] Scott takes us through what he calls the \"eight 'Ates\" of data: Relate, Aggregrate, Validate, Integrate, Interoperate, Evaluate, Communicate, Circulate\n\n[00:16:29] Scott breaks down how to effectively communicate with executives and what they care about - hint: not necessarily what you care about as a data scientist\n\n[00:18:27] Scott shares some tips for data scientists coming into organizations with legacy organizations and how to navigate that landscape\n\n[00:21:11] What are the similarities or differences in the challenges a legacy system organization faces versus a tech startup? And how can one navigate these waves?\n\n[00:23:40] So what would you say is the biggest data blunder in the last year or two? He describes the system of hotel keys and how it relates to master data, very interesting!\n\n[00:25:12] So what about some data wonders? He describes an everyday application of a wonder: the checkout counter at a grocery store.\n\n[00:26:41] More insight on communicating with stakeholders and executives\n\n[00:27:56] What are some of the soft skills that that candidates are missing that are really going to separate them from the competition?\n\n[00:29:29] There's a lot of people out there who are trying to break into the data space and maybe they feel like they don't belong there or know enough for they aren't smart enough. Do you have any words of encouragement for them?\n\n[00:31:20] Scott does a deep dive into his passion for data and how you can cultivate it in yourself\n\n[00:33:02] What's the one thing you want people to learn from your story?\n\n[00:34:21] The lightning roundSpecial Guest: Scott Taylor.","content_html":"On this episode of The Artists of Data Science, we get a chance to hear from Scott Taylor, also known as the "Data Whisperer."
\n\nHe has spread the gospel of digital transformation through public speaking engagements, blogs, videos, white papers, podcasts, puppet shows, cartoons and all forms of verbal and written communications. He has also helped organizations, such as Microsoft and Nielsen, comb through and organize their data for meaningful use.
\n\nScott shares his "eight 'ates of master data", a set of rules to engage with master data in a meaningful way. He also goes over his tips for communicating with executives, along with important soft skills that are being overlooked by data scientists.
\n\nScott is very articulate, and his passion for data and teaching are definitely evident in this episode!
\n\nWHAT YOU WILL LEARN
\n\n[12:57] The eight 'ates of master data management
\n\n[17:04] Data science communication with executives
\n\n[21:45] Legacy data systems
\n\nQUOTES
\n[3:37] "It's not all about building. Sometimes it's about making sure things are structured and organized the right way."
[7:11] "Hardware comes and goes. Software comes and goes. Data always remains."
\n\n[16:11] "Data, to have value, has got to be in motion."
\n\n[20:36] "If you're a data scientist, you are the business….and it's impossible for you to learn too much about your own business."
\n\n[27:08] "…you've got to bring people from "I have no idea what you're talking about" to "how can we live without this?" and that comes from telling a good story."
\n\nWHERE TO FIND SCOTT ONLINE
\n\nLinkedIn: https://www.linkedin.com/in/scottmztaylor/
\n\nTwitter: https://twitter.com/stdatawhisperer
\n\nWebsite: https://www.metametaconsulting.com/
\n\nSHOW NOTES
\n[00:01:20] The introduction for our guest today
[00:02:54] Scott talk to us a bit about his professional journey, how he got involved in the data world. And what drew him to this field?
\n\n[00:04:40] Scott talks to us about some of the early gigs he had in the data space.
\n\n[00:05:54] Where do you see kind of the field of big data and digital transformation? What's this landscape going to look like in two to five years?
\n\n[00:07:41] Scott talks about how the stakes are changing and how data management is unavoidable
\n\n[00:08:32] Scott goes more in-depth as to how the stakes are changing and how he's seen it play out across enterprise organizations.
\n\n[00:09:56] In this vision of the future where the stakes are changing, what do you think is going to separate the great data professionals from the merely good ones?
\n\n[00:11:25] Scott takes us through what he calls the "eight 'Ates" of data: Relate, Aggregrate, Validate, Integrate, Interoperate, Evaluate, Communicate, Circulate
\n\n[00:16:29] Scott breaks down how to effectively communicate with executives and what they care about - hint: not necessarily what you care about as a data scientist
\n\n[00:18:27] Scott shares some tips for data scientists coming into organizations with legacy organizations and how to navigate that landscape
\n\n[00:21:11] What are the similarities or differences in the challenges a legacy system organization faces versus a tech startup? And how can one navigate these waves?
\n\n[00:23:40] So what would you say is the biggest data blunder in the last year or two? He describes the system of hotel keys and how it relates to master data, very interesting!
\n\n[00:25:12] So what about some data wonders? He describes an everyday application of a wonder: the checkout counter at a grocery store.
\n\n[00:26:41] More insight on communicating with stakeholders and executives
\n\n[00:27:56] What are some of the soft skills that that candidates are missing that are really going to separate them from the competition?
\n\n[00:29:29] There's a lot of people out there who are trying to break into the data space and maybe they feel like they don't belong there or know enough for they aren't smart enough. Do you have any words of encouragement for them?
\n\n[00:31:20] Scott does a deep dive into his passion for data and how you can cultivate it in yourself
\n\n[00:33:02] What's the one thing you want people to learn from your story?
\n\n[00:34:21] The lightning round
Special Guest: Scott Taylor.
","summary":"The Data Whisperer stops by to talk about the eight 'ates of data management, what master data is and why data scientists need to know about it, and how to effectively communicate with executives.\r\n\r\nJoin the FREE open Slack mastermind community where I'll answer questions and keep you posted on bi-weekly office hours: https://bit.ly/artistsofdatascience","date_published":"2020-05-18T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/66637d63-f623-4a37-ba9d-b0d14aeb5f46.mp3","mime_type":"audio/mp3","size_in_bytes":22555720,"duration_in_seconds":2665}]},{"id":"99149d15-42c4-49cc-a067-dde20e7c1954","title":"Embrace Diversity in Data Science | Brandeis Marshall, Phd","url":"https://theartistsofdatascience.fireside.fm/brandeis-marshall","content_text":"On this episode of The Artists of Data Science, we get a chance to hear from Brandeis Marshall, a computer scientist that is excellent at breaking down difficult concepts into easily digestible pieces. \n\nShe is passionate about educating people on data, as well as understanding the impact data has on race, gender, and socio-economic disparities. \n\nShe is the CEO of DataEdx, a company which focuses on making data science accessible to all professionals.\n\nShe shares her perspective on how data impacts communities, how to promote diversity and inclusion in the data science space, and the importance of documenting your process. It was an absolute pleasure to hear her perspective, and I believe her message will help broaden the data science field.\n\nWHAT YOU WILL LEARN\n\n[8:29] How data impacts marginalized communities\n\n[13:29] From Brandeis's perspective, what separates great data scientist from good ones\n\n[14:48] Understanding how data is packaged, and ways to break it down into bite-size portions\n\n[19:30] The impact of live tweeting on social movements\n\n[30:09] Discussing inclusiveness in the data workspace\n\n[39:46] How to be gritty and break away from negative thoughts\n\nQUOTES\n[7:57] \"I'm trying to do my best to be… that beacon to talk about data in sizeable, understandable nuggets, because it's not just a science thing. It is our everyday life.\"\n\n[11:45] \"…if you stay within your own lane in your own expertise, only talking to people who have your particular background, you're losing the whole story… and with data, there's always a story\"\n\n[29:34] \"…I want…other people to know that they can talk about their particular ethnicities, content in a research space, in the tech space, and still be successful.\"\n\nFIND BRANDEIS ONLINE\n\nTwitter: https://twitter.com/csdoctorsister\n\nLinkedIn: https://www.linkedin.com/in/brandeis-marshall/\n\nWebsite: https://www.brandeismarshall.com/\n\nDataedX: https://www.dataedx.com/\n\nSHOW NOTES\n[00:01:50] Introduction for our guest today\n\n[00:04:51] Brandeis talks to us about how she heard of data science. What drew her to the field and some of the struggles and challenges she faced as she were breaking into the field\n\n[00:07:21] Break data in sizeable, understandable nuggets.\n\n[00:08:21] So where do you see the field headed in the next two to five years?\n\n[00:09:12] How do we shift the conversation so that all people are included in the data conversation?\n\n[00:10:39] What could data scientists start doing today so that two to five years in the future they understand the need for diversity of data and they're cognizant of it. What are some things that they could start doing today?\n\n[00:11:03] Data scientists need to get out of their comfort zone\n\n[00:13:12] How to be a great data scientist\n\n[00:14:27] What is data competency\n\n[00:16:38] What's the mission for your new startup, DataEdX?\n\n[00:19:30] Live tweeting, social movements, and data science\n\n[00:22:28] The technical aspects of the Black twitter project\n\n[00:27:31] Project Ideas for Data Scientists\n\n[00:29:04] If there is any impact that you want your work in this space to have on society as a whole?\n\n[00:30:08] The unfortunate effects marginalization in the data workspace\n\n[00:33:30] Diversity in data science\n\n[00:36:34] Dispelling the myth of \"it's all about technical skills\" and questioning the \"move fast\" ideology in tech.\n\n[00:39:46] Grit and being determined to seeing your goals through even in the face of challenges.\n\n[00:43:05] What's the one thing you want people to learn from your story.\n\n[00:43:23] The lightning roundSpecial Guest: Brandeis Marshall, PhD.","content_html":"On this episode of The Artists of Data Science, we get a chance to hear from Brandeis Marshall, a computer scientist that is excellent at breaking down difficult concepts into easily digestible pieces.
\n\nShe is passionate about educating people on data, as well as understanding the impact data has on race, gender, and socio-economic disparities.
\n\nShe is the CEO of DataEdx, a company which focuses on making data science accessible to all professionals.
\n\nShe shares her perspective on how data impacts communities, how to promote diversity and inclusion in the data science space, and the importance of documenting your process. It was an absolute pleasure to hear her perspective, and I believe her message will help broaden the data science field.
\n\nWHAT YOU WILL LEARN
\n\n[8:29] How data impacts marginalized communities
\n\n[13:29] From Brandeis's perspective, what separates great data scientist from good ones
\n\n[14:48] Understanding how data is packaged, and ways to break it down into bite-size portions
\n\n[19:30] The impact of live tweeting on social movements
\n\n[30:09] Discussing inclusiveness in the data workspace
\n\n[39:46] How to be gritty and break away from negative thoughts
\n\nQUOTES
\n[7:57] "I'm trying to do my best to be… that beacon to talk about data in sizeable, understandable nuggets, because it's not just a science thing. It is our everyday life."
[11:45] "…if you stay within your own lane in your own expertise, only talking to people who have your particular background, you're losing the whole story… and with data, there's always a story"
\n\n[29:34] "…I want…other people to know that they can talk about their particular ethnicities, content in a research space, in the tech space, and still be successful."
\n\nFIND BRANDEIS ONLINE
\n\nTwitter: https://twitter.com/csdoctorsister
\n\nLinkedIn: https://www.linkedin.com/in/brandeis-marshall/
\n\nWebsite: https://www.brandeismarshall.com/
\n\nDataedX: https://www.dataedx.com/
\n\nSHOW NOTES
\n[00:01:50] Introduction for our guest today
[00:04:51] Brandeis talks to us about how she heard of data science. What drew her to the field and some of the struggles and challenges she faced as she were breaking into the field
\n\n[00:07:21] Break data in sizeable, understandable nuggets.
\n\n[00:08:21] So where do you see the field headed in the next two to five years?
\n\n[00:09:12] How do we shift the conversation so that all people are included in the data conversation?
\n\n[00:10:39] What could data scientists start doing today so that two to five years in the future they understand the need for diversity of data and they're cognizant of it. What are some things that they could start doing today?
\n\n[00:11:03] Data scientists need to get out of their comfort zone
\n\n[00:13:12] How to be a great data scientist
\n\n[00:14:27] What is data competency
\n\n[00:16:38] What's the mission for your new startup, DataEdX?
\n\n[00:19:30] Live tweeting, social movements, and data science
\n\n[00:22:28] The technical aspects of the Black twitter project
\n\n[00:27:31] Project Ideas for Data Scientists
\n\n[00:29:04] If there is any impact that you want your work in this space to have on society as a whole?
\n\n[00:30:08] The unfortunate effects marginalization in the data workspace
\n\n[00:33:30] Diversity in data science
\n\n[00:36:34] Dispelling the myth of "it's all about technical skills" and questioning the "move fast" ideology in tech.
\n\n[00:39:46] Grit and being determined to seeing your goals through even in the face of challenges.
\n\n[00:43:05] What's the one thing you want people to learn from your story.
\n\n[00:43:23] The lightning round
Special Guest: Brandeis Marshall, PhD.
","summary":"Dr. Marshall stops by the show to discuss how she broke into data science, her research involving social media, the #BlackTwitterProject, plus the why's and how's of embracing diversity and equity in the tech world.\r\n\r\nJoin the FREE open Slack mastermind community where I'll answer questions and keep you posted on bi-weekly office hours: https://bit.ly/artistsofdatascience","date_published":"2020-05-11T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/99149d15-42c4-49cc-a067-dde20e7c1954.mp3","mime_type":"audio/mp3","size_in_bytes":28085141,"duration_in_seconds":2990}]},{"id":"503671ab-6b5d-4b99-b3c4-d537799d2c76","title":"All The Things I Wish They Taught Us In Bootcamps | Eric Weber, PhD","url":"https://theartistsofdatascience.fireside.fm/eric-weber","content_text":"On this episode of The Artists of Data Science, we get a chance to hear from Eric Weber, a lifelong learner, mathematician, and data scientist. \n\nHe has cultivated a passion for sharing his work and experience with others to help them become excited about data science, as well as educating executives on all aspects of data science. \n\nHe gives insight into his perspective of learning, how to be a leader in the data science field, and important skills that data scientists need to develop.\n\nEric shares with us what drew him to the field, and his transition from academia to the business side of data science. \n\nThis episode highlights the journey and success of someone who has seen the field develop from the beginning, and has continuously improved over time. I think there is a lot to learn from this conversation!\n\nWHAT YOU WILL LEARN\n\n[4:43] How to transition from academia to industry\n\n[11:40] How to become a great data scientist\n\n[20:59] How to communicate effectively with your team\n\n[24:07] The art in science\n\n[34:52] What soft skills you need\n\n[41:15] What you should do about data science job descriptions\n\nQUOTES\n\n[6:35] \"…my journey was all about figuring out two things. One, how to work with data at scale. And two, what does it mean to actually do data science in a business context. And those two things are really, really important…\"\n\n[12:17] \"You don't need to build an incredibly powerful model for every situation, but you need to know what's going to allow the business to thrive in a productive way.\"\n\n[19:48] …\"getting by is not a long term solution to delivering value for a business, because what you're doing right now to get by is probably going to be automated in a few years…\"\n\n[23:50] \"You're not always gonna be the expert in the room. And if you are, you're probably in the wrong room.\"\n\nFIND ERIC ONLINE\nLinkedIn: https://www.linkedin.com/in/eric-weber-060397b7/\n\nTwitter: https://twitter.com/edweber1\n\n[00:01:12] Introduction for our guest today\n\n[00:04:17] How Eric broke into data science\n\n[00:06:20] The challenges of transitioning from academia to industry\n\n[00:08:21] Where do you see the field headed in the next two to five years\n\n[00:09:16] Eric talks about the age of the specialist, and how its become the norm recently. He also talks about how this is now a \"prove it\" time for data science teams\n\n[00:11:32] How to be a great data scientist\n\n[00:12:54] Eric goes into detail about the need to deliver business value versus scientific value\n\n[00:14:01] Data scientists are lifelong learners\n\n[00:16:00] Why data science tends to be a more highly compensated field\n\n[00:16:17] What's your advice to aspiring data scientists who feel like they have not learned enough to start applying for jobs?\n\n[00:18:44] Why you never stop learning as a data scientist\n\n[00:20:47] Don't be afraid to not know something\n\n[00:22:09] The importance of finding teams where asking questions and being open is is valued\n\n[00:23:59] The art of data science \n\n[00:25:20] Curiosity and creativity in data science\n\n[00:30:10] How to be a great leader in data science\n\n[00:33:15] We talk about the book by Liz Wiseman called Multipliers\n\n[00:34:36] The soft skills you need to succeed\n\n[00:38:48] How could data scientists develop their business acumen and product sense?\n\n[00:41:15] Don't be discouraged by these job descriptions\n\n[00:43:28] Going from notebooks to productionizing models\n\n[00:45:51] Why do we even build models in the first place? Mainly for two reasons, find out here.\n\n[00:47:09] What's the one thing you want people to learn from your story?\n\n[00:48:04] The lightning roundSpecial Guest: Eric Weber.","content_html":"On this episode of The Artists of Data Science, we get a chance to hear from Eric Weber, a lifelong learner, mathematician, and data scientist.
\n\nHe has cultivated a passion for sharing his work and experience with others to help them become excited about data science, as well as educating executives on all aspects of data science.
\n\nHe gives insight into his perspective of learning, how to be a leader in the data science field, and important skills that data scientists need to develop.
\n\nEric shares with us what drew him to the field, and his transition from academia to the business side of data science.
\n\nThis episode highlights the journey and success of someone who has seen the field develop from the beginning, and has continuously improved over time. I think there is a lot to learn from this conversation!
\n\nWHAT YOU WILL LEARN
\n\n[4:43] How to transition from academia to industry
\n\n[11:40] How to become a great data scientist
\n\n[20:59] How to communicate effectively with your team
\n\n[24:07] The art in science
\n\n[34:52] What soft skills you need
\n\n[41:15] What you should do about data science job descriptions
\n\nQUOTES
\n\n[6:35] "…my journey was all about figuring out two things. One, how to work with data at scale. And two, what does it mean to actually do data science in a business context. And those two things are really, really important…"
\n\n[12:17] "You don't need to build an incredibly powerful model for every situation, but you need to know what's going to allow the business to thrive in a productive way."
\n\n[19:48] …"getting by is not a long term solution to delivering value for a business, because what you're doing right now to get by is probably going to be automated in a few years…"
\n\n[23:50] "You're not always gonna be the expert in the room. And if you are, you're probably in the wrong room."
\n\nFIND ERIC ONLINE
\nLinkedIn: https://www.linkedin.com/in/eric-weber-060397b7/
Twitter: https://twitter.com/edweber1
\n\n[00:01:12] Introduction for our guest today
\n\n[00:04:17] How Eric broke into data science
\n\n[00:06:20] The challenges of transitioning from academia to industry
\n\n[00:08:21] Where do you see the field headed in the next two to five years
\n\n[00:09:16] Eric talks about the age of the specialist, and how its become the norm recently. He also talks about how this is now a "prove it" time for data science teams
\n\n[00:11:32] How to be a great data scientist
\n\n[00:12:54] Eric goes into detail about the need to deliver business value versus scientific value
\n\n[00:14:01] Data scientists are lifelong learners
\n\n[00:16:00] Why data science tends to be a more highly compensated field
\n\n[00:16:17] What's your advice to aspiring data scientists who feel like they have not learned enough to start applying for jobs?
\n\n[00:18:44] Why you never stop learning as a data scientist
\n\n[00:20:47] Don't be afraid to not know something
\n\n[00:22:09] The importance of finding teams where asking questions and being open is is valued
\n\n[00:23:59] The art of data science
\n\n[00:25:20] Curiosity and creativity in data science
\n\n[00:30:10] How to be a great leader in data science
\n\n[00:33:15] We talk about the book by Liz Wiseman called Multipliers
\n\n[00:34:36] The soft skills you need to succeed
\n\n[00:38:48] How could data scientists develop their business acumen and product sense?
\n\n[00:41:15] Don't be discouraged by these job descriptions
\n\n[00:43:28] Going from notebooks to productionizing models
\n\n[00:45:51] Why do we even build models in the first place? Mainly for two reasons, find out here.
\n\n[00:47:09] What's the one thing you want people to learn from your story?
\n\n[00:48:04] The lightning round
Special Guest: Eric Weber.
","summary":"A must listen for data scientists of all level, we cover everything from the art of data science, how to be creative, how to be an effective leader, what to do when you don't know what to do, and more!\r\n\r\nJoin the FREE open Slack mastermind community where I'll answer questions and keep you posted on bi-weekly office hours: https://bit.ly/artistsofdatascience","date_published":"2020-05-04T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/503671ab-6b5d-4b99-b3c4-d537799d2c76.mp3","mime_type":"audio/mp3","size_in_bytes":30265375,"duration_in_seconds":3410}]},{"id":"5255908b-273c-4238-b23a-d820c6bdc0dc","title":"Overcome Hurdles in the Job Search by Igniting Your Passion | Chhavi Arora","url":"https://theartistsofdatascience.fireside.fm/chhavi-arora","content_text":"On this episode of The Artists of Data Science, we get a chance to hear from Chhavi Arora, one of the rising stars in the data science industry! She gives insight into how she broke into the field, the hurdles she had to overcome in the job search, and how she answered commonly asked questions during an interview.\n\nChhavi shares with us what got her interested in data science in the first place, along with the biggest self-limiting fear that she had to overcome in order to begin her journey into data science. If you are interested in becoming a data scientist but don’t know where to start, then this episode can answer many of your questions!\n\nWHAT YOU'LL LEARN\n\n[9:23] The mindset you need to adopt during the job search process \n\n[11:04] How Chhavi overcame her biggest self-limiting belief\n\n[14:58] How to get a leg-up on your competition when applying for jobs\n\n[18:05] Commonly asked questions during interviews, and how to answer them\n\n[24:55] How to prepare questions for the interviewees, and why it’s crucial \n\nQUOTES\n\n[6:39] “...every project you do as a data scientist needs to be something that you have interest in so that you know what questions you are looking for and you will eventually find answers to your work.”\n\n[12:46] “...every little weakness that you think you have can become a positive thing if you spin the story right.”\n\n[17:16] “...the most important thing is to never, never stop being passionate about data science...because the learning never stops.”\n\nFIND CHHAVI ONLINE\n\nLinkedIn: https://www.linkedin.com/in/chhavi-arora/\n\nSHOW NOTES\n\n[00:01:23] Introduction for our guest today\n\n[00:03:14] Chhavi talks to us about her experience at the NGO and how that got her interested in data science and and machine learning.\n\n[00:05:07] Chhavi tells us more about how she went about building out her projects. And how she comes up with ideas for her projects. She talks about how he creates independent projects based on what she finds interesting.\n\n[00:09:23] How important is having the right mindset during the job search? She talks about the importance of the growth mindset and how it carried her through the ups and downs\n\n[00:11:04] So what would you say was kind of the biggest self limiting belief that you had to overcome when you were in the job search?\n\n[00:12:14] How did you address resumé gaps during the interview process? It's all a matter of perspective - it's only a negative if you let it be negative. Chaavi gives some great tips\n\n[00:14:42] We get into what the job search process was like for Chhavi and she walks us through her process for applying for jobs and then getting interviews or whatnot. Listen to find out why it's not enough to send a resume and just hope that somebody would call you back.\n\n[00:16:06] ow many interviews did you go on before landing your current role?\n\n[00:17:02] Do you have any words of advice or encouragement for those rising stars out there who are now in the same position that you once were?\n\n[00:18:05] Jumping into a mock interview where Chhavi will answer commonly asked interview questions. Starting with: Tell Me About Yourself\n\n[00:19:42] Can you describe a time when you had to deal with competing priorities and with competing deadlines? And how did you handle that?\n\n[00:21:10] What's the most difficult type of person to deal with and how do you deal with them?\n\n[00:23:08] Walk me through your discovery process when you're starting a new project.\n\n[00:24:20] We talk a bit about the STAR format for answering interview questions\n\n[00:24:55] What's the process for coming up with questions to ask during the interview?\n\n[00:27:11]Let's say it comes time to talk about a technical question and the interviewer is asking you about some technical topic. How do you handle that type of question?\n\n[00:28:49] What's the one thing you want people to learn from your story?\n\n[00:29:24] Let's jump into a quick lightning round here. Python or R?\n\n[00:29:29] All right. Where do you see yourself in five years?\n\n[00:30:01] If you can go back in time to have a conversation with 18 year old Chhavi, what would it be? What would you tell her?\n\n[00:30:28] So how about your favorite book, fiction or non-fiction or both of you, if you'd like, and your biggest takeaway from them?\n\n[00:31:23] How people can connect with Chhavi, and also tips on ineffective ways to connect with anyone on LinkedIn.Special Guest: Chhavi Arora.","content_html":"On this episode of The Artists of Data Science, we get a chance to hear from Chhavi Arora, one of the rising stars in the data science industry! She gives insight into how she broke into the field, the hurdles she had to overcome in the job search, and how she answered commonly asked questions during an interview.
\n\nChhavi shares with us what got her interested in data science in the first place, along with the biggest self-limiting fear that she had to overcome in order to begin her journey into data science. If you are interested in becoming a data scientist but don’t know where to start, then this episode can answer many of your questions!
\n\nWHAT YOU'LL LEARN
\n\n[9:23] The mindset you need to adopt during the job search process
\n\n[11:04] How Chhavi overcame her biggest self-limiting belief
\n\n[14:58] How to get a leg-up on your competition when applying for jobs
\n\n[18:05] Commonly asked questions during interviews, and how to answer them
\n\n[24:55] How to prepare questions for the interviewees, and why it’s crucial
\n\nQUOTES
\n\n[6:39] “...every project you do as a data scientist needs to be something that you have interest in so that you know what questions you are looking for and you will eventually find answers to your work.”
\n\n[12:46] “...every little weakness that you think you have can become a positive thing if you spin the story right.”
\n\n[17:16] “...the most important thing is to never, never stop being passionate about data science...because the learning never stops.”
\n\nFIND CHHAVI ONLINE
\n\nLinkedIn: https://www.linkedin.com/in/chhavi-arora/
\n\nSHOW NOTES
\n\n[00:01:23] Introduction for our guest today
\n\n[00:03:14] Chhavi talks to us about her experience at the NGO and how that got her interested in data science and and machine learning.
\n\n[00:05:07] Chhavi tells us more about how she went about building out her projects. And how she comes up with ideas for her projects. She talks about how he creates independent projects based on what she finds interesting.
\n\n[00:09:23] How important is having the right mindset during the job search? She talks about the importance of the growth mindset and how it carried her through the ups and downs
\n\n[00:11:04] So what would you say was kind of the biggest self limiting belief that you had to overcome when you were in the job search?
\n\n[00:12:14] How did you address resumé gaps during the interview process? It's all a matter of perspective - it's only a negative if you let it be negative. Chaavi gives some great tips
\n\n[00:14:42] We get into what the job search process was like for Chhavi and she walks us through her process for applying for jobs and then getting interviews or whatnot. Listen to find out why it's not enough to send a resume and just hope that somebody would call you back.
\n\n[00:16:06] ow many interviews did you go on before landing your current role?
\n\n[00:17:02] Do you have any words of advice or encouragement for those rising stars out there who are now in the same position that you once were?
\n\n[00:18:05] Jumping into a mock interview where Chhavi will answer commonly asked interview questions. Starting with: Tell Me About Yourself
\n\n[00:19:42] Can you describe a time when you had to deal with competing priorities and with competing deadlines? And how did you handle that?
\n\n[00:21:10] What's the most difficult type of person to deal with and how do you deal with them?
\n\n[00:23:08] Walk me through your discovery process when you're starting a new project.
\n\n[00:24:20] We talk a bit about the STAR format for answering interview questions
\n\n[00:24:55] What's the process for coming up with questions to ask during the interview?
\n\n[00:27:11]Let's say it comes time to talk about a technical question and the interviewer is asking you about some technical topic. How do you handle that type of question?
\n\n[00:28:49] What's the one thing you want people to learn from your story?
\n\n[00:29:24] Let's jump into a quick lightning round here. Python or R?
\n\n[00:29:29] All right. Where do you see yourself in five years?
\n\n[00:30:01] If you can go back in time to have a conversation with 18 year old Chhavi, what would it be? What would you tell her?
\n\n[00:30:28] So how about your favorite book, fiction or non-fiction or both of you, if you'd like, and your biggest takeaway from them?
\n\n[00:31:23] How people can connect with Chhavi, and also tips on ineffective ways to connect with anyone on LinkedIn.
Special Guest: Chhavi Arora.
","summary":"A mock interview with a rising star of our industry and some helpful tips for preparing for any upcoming interviews you have\r\n\r\nJoin the FREE open Slack mastermind community where I'll answer questions and keep you posted on bi-weekly office hours: https://bit.ly/artistsofdatascience","date_published":"2020-04-27T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/5255908b-273c-4238-b23a-d820c6bdc0dc.mp3","mime_type":"audio/mp3","size_in_bytes":19067297,"duration_in_seconds":2001}]},{"id":"ac3dced0-b76f-4385-a023-8240c3f2f981","title":"The Legend of Data Science | Jeff Jonas ","url":"https://theartistsofdatascience.fireside.fm/jeff-jonas","content_text":"On this episode of The Artists of Data Science, we get a chance to hear from Jeff Jonas, a data scientist who, for over three decades, has been at the forefront of solving complex big data problems for companies and governments.\n\nHis software has helped casinos identify fraud, increased voter registration, protected Singapore’s waterways from piracy, and even predicted possible collisions between 600,000 asteroids over 25 years.\n\nJeff shares with us his journey from creating word processors in high school to being able to sell one of his companies to IBM, along with being one of three people to complete every Ironman triathlon in the global circuit.\n\nQUOTES\n[15:46] \"For everybody that's had a close call in life…every day since then has been an extra day. When you think about life like that, it allows you to just unleash a little bit more and make the most of it…\"\n\n[31:01] \"…You have to let new observations reverse earlier assertions.\"\n\n[34:31] \"If you don't have something that's like 10 times better and high margins, then you can't innovate\"\n\n[43:03] \"…My work is often about helping humans focus their finite resources\"\n\nWHERE TO FIND JEFF ONLINE\n\nLinkedIn: https://www.linkedin.com/in/jeff-jonas/\n\nTwitter: https://twitter.com/JeffJonas\n\nREGISTER FOR OPEN OFFICE HOURS: https://bitly.com/adsoh\n\nSHOW NOTES\n[00:01:20] The introduction for our guest today\n\n[00:03:53] Jeff walks us through professional journey, how you first heard of data science and machine learning. And what drew him to the field.\n\n[00:05:53] Where do you see the field of artificial intelligence data science machine learning headed in the next two to five years? Jeff talks abou how he sees the field flatlining and how COVID-19 is changing the landscape of the field\n\n[00:07:55] Jeff talks to us about what he thinks is going to separate the great data scientists from the good ones. He talks about the importance of being able to combine data in a way that is going to make it easy to understand the real world, he also makes a distinction between AI and Machine Learning \n\n[00:09:59] There's there's a time very early in his career when he went bankrupt and was living out of his car. Jeff talks to us about what he's saying to himself to get him through that. What did he learn from that to go on to create something bigger and better than what you had before?\n\n[00:13:25] When Jeff 23 years old he was completely paralyzed after terrible accident, he talks about his mindset and the self talk he had during that time. He shares was going on in his head and then how he you overcame those challenges\n\n[00:16:45] A bit of data history - Jeff talks about the different programming languages he was using early in his career.\n\n[00:17:01] Tips for anyone contemplating entrepreneurship\n\n[00:20:19] Jeff talks about what he thinks will be the biggest opportunities for entrepreneurship in the post-COVID world.\n\n[00:22:33] The one soft-skil that will make or break your career as a data scientist and how you can cultivate it within yourself.\n\n[00:24:32] So what compelled you to come to complete every Iron Man on the planet? And can you share some of the many, many accomplishments that you've had in that space?\n\n[00:27:01] Jeff describes an ironman event he did in Mallorca, Spain and the logistics of having to travel half way around the world back to Kentucky to compete in another ironman two days later.\n\n[00:28:42] The infamous \"Tastes like Mango\" Story\n\n[00:31:25] There's a lot of people out there who were trying to to break into data science. And maybe they don't feel like they feel like they don't belong or they don't know enough. They aren't smart enough or whatever. Do you have any words of encouragement for them?\n\n[00:32:41] What's the one thing you want people to learn from your story?\n\n[00:33:09] Jumping in to the lightning round: What's the number one book, fiction or nonfiction that you would recommend for our audience to read and who are most impactful take away from that?\n\n[00:34:50] So if you could somehow get a magical telephone that allowed you to contact 18 year old Jeff, what would you tell him?\n\n[00:35:50] Jeff talks about the work he's done in his career from the Llama Birth registration project he completed, to the modernization of voter registration.\n\n[00:37:12] Jeff has over 100 inventions to his name - he talks about some of his most favorite ones.\n\n[00:38:30] Jeff talks about the project he did with astronomers which involved identifying where in space asteroids are going to be, and which ones may possibly collide with each other or earth.\n\n[00:43:54] Which of your inventions do you think is most relevant now to the current times?\n\n[00:45:43] A quick primer on entity resolution and a very simple example of interweaving common sense with real time AI\n\n[00:47:29] So what's the best advice you ever received?\n\n[00:47:57] Do you have a favorite Iron Man event?\n\n[00:48:31] So what motivates you?\n\n[00:49:10] So how can people connect with you? When can they find you?\n\n[00:49:57] The importance of being accessibleSpecial Guest: Jeff Jonas.","content_html":"On this episode of The Artists of Data Science, we get a chance to hear from Jeff Jonas, a data scientist who, for over three decades, has been at the forefront of solving complex big data problems for companies and governments.
\n\nHis software has helped casinos identify fraud, increased voter registration, protected Singapore’s waterways from piracy, and even predicted possible collisions between 600,000 asteroids over 25 years.
\n\nJeff shares with us his journey from creating word processors in high school to being able to sell one of his companies to IBM, along with being one of three people to complete every Ironman triathlon in the global circuit.
\n\nQUOTES
\n[15:46] "For everybody that's had a close call in life…every day since then has been an extra day. When you think about life like that, it allows you to just unleash a little bit more and make the most of it…"
[31:01] "…You have to let new observations reverse earlier assertions."
\n\n[34:31] "If you don't have something that's like 10 times better and high margins, then you can't innovate"
\n\n[43:03] "…My work is often about helping humans focus their finite resources"
\n\nWHERE TO FIND JEFF ONLINE
\n\nLinkedIn: https://www.linkedin.com/in/jeff-jonas/
\n\nTwitter: https://twitter.com/JeffJonas
\n\nREGISTER FOR OPEN OFFICE HOURS: https://bitly.com/adsoh
\n\nSHOW NOTES
\n[00:01:20] The introduction for our guest today
[00:03:53] Jeff walks us through professional journey, how you first heard of data science and machine learning. And what drew him to the field.
\n\n[00:05:53] Where do you see the field of artificial intelligence data science machine learning headed in the next two to five years? Jeff talks abou how he sees the field flatlining and how COVID-19 is changing the landscape of the field
\n\n[00:07:55] Jeff talks to us about what he thinks is going to separate the great data scientists from the good ones. He talks about the importance of being able to combine data in a way that is going to make it easy to understand the real world, he also makes a distinction between AI and Machine Learning
\n\n[00:09:59] There's there's a time very early in his career when he went bankrupt and was living out of his car. Jeff talks to us about what he's saying to himself to get him through that. What did he learn from that to go on to create something bigger and better than what you had before?
\n\n[00:13:25] When Jeff 23 years old he was completely paralyzed after terrible accident, he talks about his mindset and the self talk he had during that time. He shares was going on in his head and then how he you overcame those challenges
\n\n[00:16:45] A bit of data history - Jeff talks about the different programming languages he was using early in his career.
\n\n[00:17:01] Tips for anyone contemplating entrepreneurship
\n\n[00:20:19] Jeff talks about what he thinks will be the biggest opportunities for entrepreneurship in the post-COVID world.
\n\n[00:22:33] The one soft-skil that will make or break your career as a data scientist and how you can cultivate it within yourself.
\n\n[00:24:32] So what compelled you to come to complete every Iron Man on the planet? And can you share some of the many, many accomplishments that you've had in that space?
\n\n[00:27:01] Jeff describes an ironman event he did in Mallorca, Spain and the logistics of having to travel half way around the world back to Kentucky to compete in another ironman two days later.
\n\n[00:28:42] The infamous "Tastes like Mango" Story
\n\n[00:31:25] There's a lot of people out there who were trying to to break into data science. And maybe they don't feel like they feel like they don't belong or they don't know enough. They aren't smart enough or whatever. Do you have any words of encouragement for them?
\n\n[00:32:41] What's the one thing you want people to learn from your story?
\n\n[00:33:09] Jumping in to the lightning round: What's the number one book, fiction or nonfiction that you would recommend for our audience to read and who are most impactful take away from that?
\n\n[00:34:50] So if you could somehow get a magical telephone that allowed you to contact 18 year old Jeff, what would you tell him?
\n\n[00:35:50] Jeff talks about the work he's done in his career from the Llama Birth registration project he completed, to the modernization of voter registration.
\n\n[00:37:12] Jeff has over 100 inventions to his name - he talks about some of his most favorite ones.
\n\n[00:38:30] Jeff talks about the project he did with astronomers which involved identifying where in space asteroids are going to be, and which ones may possibly collide with each other or earth.
\n\n[00:43:54] Which of your inventions do you think is most relevant now to the current times?
\n\n[00:45:43] A quick primer on entity resolution and a very simple example of interweaving common sense with real time AI
\n\n[00:47:29] So what's the best advice you ever received?
\n\n[00:47:57] Do you have a favorite Iron Man event?
\n\n[00:48:31] So what motivates you?
\n\n[00:49:10] So how can people connect with you? When can they find you?
\n\n[00:49:57] The importance of being accessible
Special Guest: Jeff Jonas.
","summary":"The man National Geographic named the \"Wizard of Big Data\" stops by the show to talk about how he overcame some huge hurdles in life to eventually compete in every Ironman event on the circuit and how helped astronomers save the Earth from impending doom.\r\n\r\nJoin the FREE open Slack mastermind community where I'll answer questions and keep you posted on bi-weekly office hours: https://bit.ly/artistsofdatascience","date_published":"2020-04-20T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/ac3dced0-b76f-4385-a023-8240c3f2f981.mp3","mime_type":"audio/mp3","size_in_bytes":28640250,"duration_in_seconds":3089}]},{"id":"631ee619-e185-46b1-b4d2-bde70c32bcd7","title":"Secrets to Success in Data Science | Kyle McKiou","url":"https://theartistsofdatascience.fireside.fm/kyle-mckiou","content_text":"On this episode of The Artists of Data Science, we get a chance to hear from Kyle McKiou, a data scientist who took the lessons from his own struggles that he faced attempting to break into data science and packaged them into a course for up and coming data scientists. \n\nHe is known for his remarkable talent for building skilled, balanced and productive teams. He gives insight into how he broke into the data science field, his approach for problem solving, and they importance of facing your fears.\n\nKyle shares with us the importance of finding a mentor that can guide you to accomplish your goals and the important soft skills that you may be overlooking. Kyle brings unprecedented wisdom and advice to this episode, and the points he outlines can help everyone step up their professional goals.\n\nWHAT YOU WILL LEARN\n[7:43] What value Kyle believes data science will bring within the next few years\n[11:38] How to transition into data science\n[16:33] The importance of cultivating a growth mindset\n[28:30] Soft skills that data science candidates are missing\n[33:01] The single biggest myth about breaking into data science\n\nQUOTES\n[16:13] \"Be risk averse; Test everything.\"\n\n[24:50] \"You've got to engineer a system that solves the problem for you, because if you have to leverage your own intelligence to solve a problem, well, you're going to be very limited in the amount of work that you can do.\"\n\n[27:23] \"…you start with the problem you want to solve. You break it down to simpler problems. You break those problems down to simpler problems…all the way back until you get to your present state and then you see the exact path forward at any point time…\"\n\n[28:31] \"…I think in most careers it's not going to be the hard skills that separate you, particularly in data science…[it's] those soft skills, because you realize that if you want to make an impact in the company as a scientist, you're going to need other people to work with you…\"\n\n[34:55] \"…it doesn't matter how much you know, it matters how much you can learn and adapt.\"\n\nFIND KYLE ONLINE\nInstagram: https://www.instagram.com/kylemckiou/\nLinkedIn: https://www.linkedin.com/in/kylemckiou/\nFacebook: https://www.facebook.com/datasciencekyle/\nData Science Dream Job: https://dsdj.co/artists70\n\nSHOW NOTES\n[01:30] Introduction of our guest today\n\n[03:10] Talk to us a little bit about how you first heard data science and what drew you to the field\n\n[4:50] How software engineering is different from data science\n\n[06:42] What do you love most about the field of data science?\n\n[07:29] Why do you think the field is headed the next two to five years?\n\n[09:46] What do you think is in the separate the great data scientists from the merely good ones?\n\n[11:21] Switching from software engineering to data science\n\n[12:42] How to productionize a machine learning model\n\n[13:19] Why notebooks don't scale\n\n[16:18] The importance of the growth mindset for data scientists\n\n[19:38] Fear as an indicator\n\n[24:29] The engineers mindset for data science\n\n[28:30] Soft skills for data science\n\n[33:01] The biggest myth about breaking into data science\n\n[35:00] Poker and data science\n\n[37:07] What's the one thing you want people to learn from your story?\n\n[39:17] The lightning round Special Guest: Kyle McKiou.","content_html":"On this episode of The Artists of Data Science, we get a chance to hear from Kyle McKiou, a data scientist who took the lessons from his own struggles that he faced attempting to break into data science and packaged them into a course for up and coming data scientists.
\n\nHe is known for his remarkable talent for building skilled, balanced and productive teams. He gives insight into how he broke into the data science field, his approach for problem solving, and they importance of facing your fears.
\n\nKyle shares with us the importance of finding a mentor that can guide you to accomplish your goals and the important soft skills that you may be overlooking. Kyle brings unprecedented wisdom and advice to this episode, and the points he outlines can help everyone step up their professional goals.
\n\nWHAT YOU WILL LEARN
\n[7:43] What value Kyle believes data science will bring within the next few years
\n[11:38] How to transition into data science
\n[16:33] The importance of cultivating a growth mindset
\n[28:30] Soft skills that data science candidates are missing
\n[33:01] The single biggest myth about breaking into data science
QUOTES
\n[16:13] "Be risk averse; Test everything."
[24:50] "You've got to engineer a system that solves the problem for you, because if you have to leverage your own intelligence to solve a problem, well, you're going to be very limited in the amount of work that you can do."
\n\n[27:23] "…you start with the problem you want to solve. You break it down to simpler problems. You break those problems down to simpler problems…all the way back until you get to your present state and then you see the exact path forward at any point time…"
\n\n[28:31] "…I think in most careers it's not going to be the hard skills that separate you, particularly in data science…[it's] those soft skills, because you realize that if you want to make an impact in the company as a scientist, you're going to need other people to work with you…"
\n\n[34:55] "…it doesn't matter how much you know, it matters how much you can learn and adapt."
\n\nFIND KYLE ONLINE
\nInstagram: https://www.instagram.com/kylemckiou/
\nLinkedIn: https://www.linkedin.com/in/kylemckiou/
\nFacebook: https://www.facebook.com/datasciencekyle/
\nData Science Dream Job: https://dsdj.co/artists70
SHOW NOTES
\n[01:30] Introduction of our guest today
[03:10] Talk to us a little bit about how you first heard data science and what drew you to the field
\n\n[4:50] How software engineering is different from data science
\n\n[06:42] What do you love most about the field of data science?
\n\n[07:29] Why do you think the field is headed the next two to five years?
\n\n[09:46] What do you think is in the separate the great data scientists from the merely good ones?
\n\n[11:21] Switching from software engineering to data science
\n\n[12:42] How to productionize a machine learning model
\n\n[13:19] Why notebooks don't scale
\n\n[16:18] The importance of the growth mindset for data scientists
\n\n[19:38] Fear as an indicator
\n\n[24:29] The engineers mindset for data science
\n\n[28:30] Soft skills for data science
\n\n[33:01] The biggest myth about breaking into data science
\n\n[35:00] Poker and data science
\n\n[37:07] What's the one thing you want people to learn from your story?
\n\n[39:17] The lightning round
Special Guest: Kyle McKiou.
","summary":"The CEO and Founder of Data Science Dream Job and Dream Job Academy stops by the show to talk about how he broke into data science, the challenges he faced along the way, and debunks several myths about breaking into the industry. \r\n\r\nJoin the FREE open Slack mastermind community where I'll answer questions and keep you posted on bi-weekly office hours: https://bit.ly/artistsofdatascience","date_published":"2020-04-13T00:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/631ee619-e185-46b1-b4d2-bde70c32bcd7.mp3","mime_type":"audio/mp3","size_in_bytes":23840848,"duration_in_seconds":2681}]},{"id":"478bccfe-3929-443b-9949-9e5ec31b1b56","title":"How to Learn Effectively and More Tips for Success | Mark Nagelberg","url":"https://theartistsofdatascience.fireside.fm/mark-nagelberg","content_text":"One of Winnipeg's finest data scientists talks about the skills that have helped him become successful (hint: doesn't involve memorize every hyper-parameter of every algorithm). \n\nJoin the FREE open Slack mastermind community where I'll answer questions and keep you posted on bi-weekly office hours: https://bit.ly/artistsofdatascience.\n\nFollow the show in Twitter: @ArtistsOfData, on IG: @TheArtistsOfData Science, on FB:facebook.com/TheArtistsOfDataScience, and on LinkedIn!\n\n[04:38] We talk about how Mark got into data science and the path that led him to where he is now.\n\n[05:59] Mark talks about his awesome blog and how creating the blog helped him learn and grow as a data scientist.\n\n[07:43] Mark tells us about Toastmasters and how being a part of that has helped him imporve his speaking skills and the benefits that a data scientist can gain from joining a Toastmasters club.\n\n[11:00] He tell us a bit more about space repetition and how it's helped him learn more effectively.\n\n[12:53] We discuss the similairties and differences between spaced repetition and deliberate practice, and Mark shares his tips for creating flashcards to help you quiz yourself.\n\n[14:23] Mark talks to us about the hidden power of compouding and how all it takes to master anything is a solid plan of attack, time, and growth will occurr.\n\n[17:50] He share some resources and blogs that expound on the concept of compounding.\n\n[18:30] We get into what Mark's creative process is like for bringing his project ideas to reality and he shares tips for the up and coming data scientists who don't know where to start with their project.\n\n[19:54] How he goes about identifying where to find data to start working on a project. He also talks about scraping the web for projects, some packages you can use for that, and some warnings so you don't get in trouble.\n\n[21:47] Mark talks about how his idols shouted him out on their blog after he scraped their website and analyzed their posting behaviour.\n\n[23:18] We get into another project that Mark worked on which involved analyzing data about trees from the City of Winnipeg open data portal. \n\n[25:34] He also talks about some interesting and weird data that he's seen out there and then Mark talks about his framework for decision making and how this framework has helped him navigate the ambiguities of data science projects.\n\n[27:30] How to use costs and benefits when making deciisons and find out how to best add value.\n\n[28:32] Advice for people starting a new job as a data scientist and how to identify expectations and set expectations so that all parties involved are on the same page.\n\n[29:47] How he describes his role to people within his organization who don't know what a data scientist is. \n\n[30:48] The one thing Mark wants everyone to learn from his story.\n\n[32:39] Getting into our lightning round - Python or R.\n\n[32:58] A book he recommends every data scientist reads\n\n[33:30] His favorite question to interviewee's ask during a job interview.\n\n[34:05] Mark talks about the weird question he's been asked during an interview.\n\n[34:36] Mark talks about his preference for self-directed learning and projects over certifications.\n\n[35:19] How you can get in touch and connect with Mark online!Special Guest: Mark Nagelberg.","content_html":"One of Winnipeg's finest data scientists talks about the skills that have helped him become successful (hint: doesn't involve memorize every hyper-parameter of every algorithm).
\n\nJoin the FREE open Slack mastermind community where I'll answer questions and keep you posted on bi-weekly office hours: https://bit.ly/artistsofdatascience.
\n\nFollow the show in Twitter: @ArtistsOfData, on IG: @TheArtistsOfData Science, on FB:facebook.com/TheArtistsOfDataScience, and on LinkedIn!
\n\n[04:38] We talk about how Mark got into data science and the path that led him to where he is now.
\n\n[05:59] Mark talks about his awesome blog and how creating the blog helped him learn and grow as a data scientist.
\n\n[07:43] Mark tells us about Toastmasters and how being a part of that has helped him imporve his speaking skills and the benefits that a data scientist can gain from joining a Toastmasters club.
\n\n[11:00] He tell us a bit more about space repetition and how it's helped him learn more effectively.
\n\n[12:53] We discuss the similairties and differences between spaced repetition and deliberate practice, and Mark shares his tips for creating flashcards to help you quiz yourself.
\n\n[14:23] Mark talks to us about the hidden power of compouding and how all it takes to master anything is a solid plan of attack, time, and growth will occurr.
\n\n[17:50] He share some resources and blogs that expound on the concept of compounding.
\n\n[18:30] We get into what Mark's creative process is like for bringing his project ideas to reality and he shares tips for the up and coming data scientists who don't know where to start with their project.
\n\n[19:54] How he goes about identifying where to find data to start working on a project. He also talks about scraping the web for projects, some packages you can use for that, and some warnings so you don't get in trouble.
\n\n[21:47] Mark talks about how his idols shouted him out on their blog after he scraped their website and analyzed their posting behaviour.
\n\n[23:18] We get into another project that Mark worked on which involved analyzing data about trees from the City of Winnipeg open data portal.
\n\n[25:34] He also talks about some interesting and weird data that he's seen out there and then Mark talks about his framework for decision making and how this framework has helped him navigate the ambiguities of data science projects.
\n\n[27:30] How to use costs and benefits when making deciisons and find out how to best add value.
\n\n[28:32] Advice for people starting a new job as a data scientist and how to identify expectations and set expectations so that all parties involved are on the same page.
\n\n[29:47] How he describes his role to people within his organization who don't know what a data scientist is.
\n\n[30:48] The one thing Mark wants everyone to learn from his story.
\n\n[32:39] Getting into our lightning round - Python or R.
\n\n[32:58] A book he recommends every data scientist reads
\n\n[33:30] His favorite question to interviewee's ask during a job interview.
\n\n[34:05] Mark talks about the weird question he's been asked during an interview.
\n\n[34:36] Mark talks about his preference for self-directed learning and projects over certifications.
\n\n[35:19] How you can get in touch and connect with Mark online!
Special Guest: Mark Nagelberg.
","summary":"One of Winnipeg's finest data scientists talks about the skills that have helped him become successful (hint: doesn't involve memorize every hyper-parameter of every algorithm). ","date_published":"2020-04-08T17:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/478bccfe-3929-443b-9949-9e5ec31b1b56.mp3","mime_type":"audio/mpeg","size_in_bytes":20734645,"duration_in_seconds":2216}]},{"id":"dc7ab9d6-034e-4b77-bd94-40ad268affdf","title":"How to Find Your Ikigai | Daniel Bourke","url":"https://theartistsofdatascience.fireside.fm/daniel-bourke","content_text":"There's no way you can't be hype after this conversation.\n\nJoin the FREE open Slack mastermind community where I'll answer questions and keep you posted on bi-weekly office hours: https://bit.ly/artistsofdatascience\n\nFollow the show in Twitter: @ArtistsOfData, on IG: @TheArtistsOfDataScience, on FB: facebook.com/TheArtistsOfDataScience, and on LinkedIn!\n\n[02:24] The introduction for our guest\n\n[04:05] Daniel walks us down the path that led him to data science and machine learning and ties it all back to his Ikigai.\n\n[06:05] How the movie Robot Man inspired him to code.\n\n[06:49] Daniel talks to us about how he used to work as an Apple Genius and preferred a customer facing role, and how that experience led to him developing his first app\n\n[09:41] How Siraj Raval got him excited about machine learning and his experiences learning to code in Python for the first time through a Udacity Nanodegree\n\n[14:00] Where Daniel thinks the field of data science and machine learning is headed in the next two to five years.\n\n[16:15] Daniel talks about what is going to seperate the great data scientists from the merely good ones in the future he is imagining. He also talks about the struggles of shiny object syndrome that all engineers face and how to approach your work like a craftsman.\n\n[19:22] We discuss if data science is an art or a science, how it can be both depending on how you're expressing yourself.\n\n[21:11] How Danies expresses himself artistically using data science.\n\n[22:16] What it's like when he's being scientific with it.\n\n[23:04] How Daniel started on his #100DaysOfCode journey.\n\n[25:00] He talks about his favorite day during the challenge.\n\n*[25:54] * Daniel shares some tips for our listeners that they can implement today to help them along in their upskilling process.\n\n[26:53] How to be a fan of yourself by putting your soul into the work that you're doing.\n\n[29:07] How to find a mentor for yourself, how to be a mentor to yourself, and things a good mentor does and doesn't do.\n\n[34:09] How a good mentor plants a seed in your mind, and doesn't just give you the answer.\n\n[37:30] Why it's OK to suck at the beginning, and how to navigate through that suck phase\n\n[39:18] Why you shouldn't compare progress on a day to day basis, but give youself a long enough timeframe so that a meaningful comparison can be made.,\n\n[42:03] How to navigate the myriad courses out there, find some that will work for you, and design your own \"Masters\" program.\n\n[46:50] How to build enough of a foundation in the basics, and then apply what you learn on top of that using the weekend project principle.\n\n[47:39] Why your certificates don't really mean much without a project.\n\n[49:16] The one thing Daniel wants everyone to learn from his story.\n\n[50:24] We jump into our lightning round - Python or R\n\n[50:43] Daniel talks about some books that he recommends and his biggest takeaways from them\n\n[53:07] Daniel describes his morning routine\n\n[54:32] Daniel tells us the best advice that he's ever recieved - it's from his dad.\n\n[55:55] Daniel lets us know how we can connect with him and where we can find him onlineSpecial Guest: Daniel Bourke.","content_html":"There's no way you can't be hype after this conversation.
\n\nJoin the FREE open Slack mastermind community where I'll answer questions and keep you posted on bi-weekly office hours: https://bit.ly/artistsofdatascience
\n\nFollow the show in Twitter: @ArtistsOfData, on IG: @TheArtistsOfDataScience, on FB: facebook.com/TheArtistsOfDataScience, and on LinkedIn!
\n\n[02:24] The introduction for our guest
\n\n[04:05] Daniel walks us down the path that led him to data science and machine learning and ties it all back to his Ikigai.
\n\n[06:05] How the movie Robot Man inspired him to code.
\n\n[06:49] Daniel talks to us about how he used to work as an Apple Genius and preferred a customer facing role, and how that experience led to him developing his first app
\n\n[09:41] How Siraj Raval got him excited about machine learning and his experiences learning to code in Python for the first time through a Udacity Nanodegree
\n\n[14:00] Where Daniel thinks the field of data science and machine learning is headed in the next two to five years.
\n\n[16:15] Daniel talks about what is going to seperate the great data scientists from the merely good ones in the future he is imagining. He also talks about the struggles of shiny object syndrome that all engineers face and how to approach your work like a craftsman.
\n\n[19:22] We discuss if data science is an art or a science, how it can be both depending on how you're expressing yourself.
\n\n[21:11] How Danies expresses himself artistically using data science.
\n\n[22:16] What it's like when he's being scientific with it.
\n\n[23:04] How Daniel started on his #100DaysOfCode journey.
\n\n[25:00] He talks about his favorite day during the challenge.
\n\n*[25:54] * Daniel shares some tips for our listeners that they can implement today to help them along in their upskilling process.
\n\n[26:53] How to be a fan of yourself by putting your soul into the work that you're doing.
\n\n[29:07] How to find a mentor for yourself, how to be a mentor to yourself, and things a good mentor does and doesn't do.
\n\n[34:09] How a good mentor plants a seed in your mind, and doesn't just give you the answer.
\n\n[37:30] Why it's OK to suck at the beginning, and how to navigate through that suck phase
\n\n[39:18] Why you shouldn't compare progress on a day to day basis, but give youself a long enough timeframe so that a meaningful comparison can be made.,
\n\n[42:03] How to navigate the myriad courses out there, find some that will work for you, and design your own "Masters" program.
\n\n[46:50] How to build enough of a foundation in the basics, and then apply what you learn on top of that using the weekend project principle.
\n\n[47:39] Why your certificates don't really mean much without a project.
\n\n[49:16] The one thing Daniel wants everyone to learn from his story.
\n\n[50:24] We jump into our lightning round - Python or R
\n\n[50:43] Daniel talks about some books that he recommends and his biggest takeaways from them
\n\n[53:07] Daniel describes his morning routine
\n\n[54:32] Daniel tells us the best advice that he's ever recieved - it's from his dad.
\n\n[55:55] Daniel lets us know how we can connect with him and where we can find him online
Special Guest: Daniel Bourke.
","summary":"There's no way you can't be hype after this conversation.\r\n\r\n","date_published":"2020-04-08T17:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/dc7ab9d6-034e-4b77-bd94-40ad268affdf.mp3","mime_type":"audio/mpeg","size_in_bytes":33541482,"duration_in_seconds":3436}]},{"id":"3d90d814-4c47-4c06-b577-176a4915abf4","title":"Remove Your Self-Limiting Beliefs and You Will Soar | Lediona Nishani, PhD","url":"https://theartistsofdatascience.fireside.fm/lediona-nishani","content_text":"Remove your self-limiting beliefs, unleash your inner spirit animal, and climb the ranks of the data science competence hierarchy\n\nJoin the FREE open Slack mastermind community where I'll answer questions and keep you posted on bi-weekly office hours: https://bit.ly/artistsofdatascience\n\n[04:34] Lediona talks about her journey from the research world to data science and touches on some of the challenges she faced along the way and how she overcame them.\n\n[06:56] Lediona talks about show passionate she isabout is NLP, what got her interested in NLP and what she thinks the future holds for this particular area of data science.\n\n[10:45] Lediona talks about some of the common challenges she's seen up and coming data scientists face when it comes time to take research into production. \n\n[14:20] Lediona walks us through her analysis discovery process and the first thing she does when she's taking on a new project. She also talks about some of the steps she takes to keep herself on track while navigating the ambiguity of some of data science projects.\n\n[16:21] Lediona talks about the skills she considers to be an essential skill to be and remain successful as a data scientist.\n\n[18:25] Lediona talks about what she is looking for in an up-and-coming data scientist.\n\n[20:15] We talk about the skills that really set Lediona apart from the pack and the non-technical qualities that's really contributed most to her success.\n\n[21:52] We talk more about the growth mindset and how not to let your beliefs limit your success.\n\n[22:53] Lediona speaks to her experience being a woman in tech, her involvement in Toronto WIDS and shares some words of encouragement for our female listeners.\n\n[24:48] She shares the one thing she want everone to learn from her story.\n\n[26:25] Jump into our lightning round with an opening question: Python or R\n\n[26:51] She speaks about her favorite algorithm\n\n[27:41] What's a book that every data scientist should read? \n\n[29:05] How about a book recommendation for people that are wanting to learn NLP. \n\n[29:19] We talk about her favorite question to as the interviewers during an interview and how it helps he find out if this is the right company for her.\n\n[30:00] We talk about the strangest question she's been asked in an interview and also talk about our spirit animals, and touch on being a generalist or a specialist.\n\n[31:02] Lediona let's you know how you can connect with her onlineSpecial Guest: Lediona Nishani.","content_html":"Remove your self-limiting beliefs, unleash your inner spirit animal, and climb the ranks of the data science competence hierarchy
\n\nJoin the FREE open Slack mastermind community where I'll answer questions and keep you posted on bi-weekly office hours: https://bit.ly/artistsofdatascience
\n\n[04:34] Lediona talks about her journey from the research world to data science and touches on some of the challenges she faced along the way and how she overcame them.
\n\n[06:56] Lediona talks about show passionate she isabout is NLP, what got her interested in NLP and what she thinks the future holds for this particular area of data science.
\n\n[10:45] Lediona talks about some of the common challenges she's seen up and coming data scientists face when it comes time to take research into production.
\n\n[14:20] Lediona walks us through her analysis discovery process and the first thing she does when she's taking on a new project. She also talks about some of the steps she takes to keep herself on track while navigating the ambiguity of some of data science projects.
\n\n[16:21] Lediona talks about the skills she considers to be an essential skill to be and remain successful as a data scientist.
\n\n[18:25] Lediona talks about what she is looking for in an up-and-coming data scientist.
\n\n[20:15] We talk about the skills that really set Lediona apart from the pack and the non-technical qualities that's really contributed most to her success.
\n\n[21:52] We talk more about the growth mindset and how not to let your beliefs limit your success.
\n\n[22:53] Lediona speaks to her experience being a woman in tech, her involvement in Toronto WIDS and shares some words of encouragement for our female listeners.
\n\n[24:48] She shares the one thing she want everone to learn from her story.
\n\n[26:25] Jump into our lightning round with an opening question: Python or R
\n\n[26:51] She speaks about her favorite algorithm
\n\n[27:41] What's a book that every data scientist should read?
\n\n[29:05] How about a book recommendation for people that are wanting to learn NLP.
\n\n[29:19] We talk about her favorite question to as the interviewers during an interview and how it helps he find out if this is the right company for her.
\n\n[30:00] We talk about the strangest question she's been asked in an interview and also talk about our spirit animals, and touch on being a generalist or a specialist.
\n\n[31:02] Lediona let's you know how you can connect with her online
Special Guest: Lediona Nishani.
","summary":"Remove your self-limiting beliefs, unleash your inner spirit animal, and climb the ranks of the data science competence hierarchy","date_published":"2020-04-08T17:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/3d90d814-4c47-4c06-b577-176a4915abf4.mp3","mime_type":"audio/mpeg","size_in_bytes":20225684,"duration_in_seconds":1986}]},{"id":"79173d14-696e-4818-bf34-5d805fe0c2e1","title":"How to become a data engineer | Andreas Kretz","url":"https://theartistsofdatascience.fireside.fm/andreas-kretz","content_text":"One of LinkedIn's Top Voices in Data Science and Analytics for two years in a row (2018 and 2019) stops by the show to talk about his journey into data engineering, why you dozens of data science certificates are meaningless, and how you can become a data engineer!\n\nJoin the FREE open Slack mastermind community where I'll answer questions and keep you posted on bi-weekly office hours: https://bit.ly/artistsofdatascience\n\nFollow the show in Twitter: @ArtistsOfData, on IG: @TheArtistsOfDataScience, on FB: facebook.com/TheArtistsOfDataScience, and on LinkedIn!\n\n[02:20] The introduction for our guest today\n\n[04:16] Andreas talks to us about how he got into the world of data science\n\n[05:58] The importance of having both engineers and data scientists on your data science team, and why you need both to really be successful.\n\n[06:35] Andreas talks to us about his upcoming book - The Data Engineering Cookbook\n\n[07:57] What his creative process is like for writing the book, and the differences and similarities between that and doing a data science project.\n\n[09:56] Andreas shares he views on the value of certificates\n\n[11:54] Andreas takes us through a workflow for creating a data engineering project and how you can build one for your portfolio.\n\n[14:47] We talk about his new coaching and mentoring platform and what he is aiming to accomplish and achieve with that. We also talk more details for building out a data engineering project.\n\n[17:21] More details on his coaching platform and what he wants students to gain from going through the program\n\n[19:56] Jump into to the lightning round here. Python or R? \n\n[21:02] What cloud platform data engineers should start using : AWS or Azure?\n\n[21:46] Self study or certificates? \n\n[21:53] Favorite big data tool?\n\n[22:09] His favorite question to ask during an interview\n\n[23:14] The weirdest question he's been asked in an interview\n\n[23:41] How you can connect with Andreas and where you can find him onlineSpecial Guest: Andreas Kretz.","content_html":"One of LinkedIn's Top Voices in Data Science and Analytics for two years in a row (2018 and 2019) stops by the show to talk about his journey into data engineering, why you dozens of data science certificates are meaningless, and how you can become a data engineer!
\n\nJoin the FREE open Slack mastermind community where I'll answer questions and keep you posted on bi-weekly office hours: https://bit.ly/artistsofdatascience
\n\nFollow the show in Twitter: @ArtistsOfData, on IG: @TheArtistsOfDataScience, on FB: facebook.com/TheArtistsOfDataScience, and on LinkedIn!
\n\n[02:20] The introduction for our guest today
\n\n[04:16] Andreas talks to us about how he got into the world of data science
\n\n[05:58] The importance of having both engineers and data scientists on your data science team, and why you need both to really be successful.
\n\n[06:35] Andreas talks to us about his upcoming book - The Data Engineering Cookbook
\n\n[07:57] What his creative process is like for writing the book, and the differences and similarities between that and doing a data science project.
\n\n[09:56] Andreas shares he views on the value of certificates
\n\n[11:54] Andreas takes us through a workflow for creating a data engineering project and how you can build one for your portfolio.
\n\n[14:47] We talk about his new coaching and mentoring platform and what he is aiming to accomplish and achieve with that. We also talk more details for building out a data engineering project.
\n\n[17:21] More details on his coaching platform and what he wants students to gain from going through the program
\n\n[19:56] Jump into to the lightning round here. Python or R?
\n\n[21:02] What cloud platform data engineers should start using : AWS or Azure?
\n\n[21:46] Self study or certificates?
\n\n[21:53] Favorite big data tool?
\n\n[22:09] His favorite question to ask during an interview
\n\n[23:14] The weirdest question he's been asked in an interview
\n\n[23:41] How you can connect with Andreas and where you can find him online
Special Guest: Andreas Kretz.
","summary":"One of LinkedIn's Top Voices in Data Science and Analytics for two years in a row (2018 and 2019) stops by the show to talk about his journey into data engineering, why you dozens of data science certificates are meaningless, and how you can become a data engineer!","date_published":"2020-04-08T16:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/79173d14-696e-4818-bf34-5d805fe0c2e1.mp3","mime_type":"audio/mpeg","size_in_bytes":15405017,"duration_in_seconds":1505}]},{"id":"51dfff53-13d8-468a-90d2-11c2ba25ff47","title":"Don't Let Them Tell You What You Can't Do | David Tello","url":"https://theartistsofdatascience.fireside.fm/david-tello","content_text":"From nearly getting booted from college to going on to earn a PhD in Mathematics.\n\nJoin the FREE open Slack mastermind community where I'll answer questions and keep you posted on bi-weekly office hours: https://bit.ly/artistsofdatascience\n\nFollow the show in Twitter: @ArtistsOfData, on IG: @TheArtistsOfDataScience, on FB: facebook.com/TheArtistsOfDataScience, and on LinkedIn!\n\n[02:32] The introduction for our guest today\n\n[04:07] David talks to us about the struggles he faced when he emigrated to the USA from Peru.\n\n[06:01] David talks about how mathematics turned his life around.\n\n[06:39] Early in his career a professor told David that \"it's clear that your first derivative is positive. The question is, is are secondary derivative positive?\" He explains to us what this means in mathematical terms, what the professor meant using the metaphor. He walks us through the troubles he faced being on academic probation, how he tried to get a letter of recommendation, and he talks about the impact that meeting had on him.\n\n[10:57] A meeting with a professor who told him that he wasn't good enough to be on this campus. He talks about the pain he felt when he wasn't sure what his path in life was going to be.\n\n[11:54] He talks about his experiences at the University of Michigan and the impact of being around mathematicians that looked like him had on his career.\n\n[12:24] I ask David what it's like to be a minority in a field filled with people who look like me (mostly Indians and Asians) and he how he views himself in this industry, and how being a minority in the field of mathematics is different from being a minority in the field of data science\n\n[16:51] David talks about the struggles and obstacles he faced while trying to get past his PhD qualifying., how he almost didn't return back to school, and how he just kept coming back up after setbacks.\n\n[23:46] He shares advice for how to manage the upskilling process thats required to be a data scientist.\n\n[26:26] David tells us the one thing he wants people to learn from his story\n\n[27:45] We jump into the lightning round: Python or R?\n\n[27:55] Favorite classification algorithm\n\n[28:26] Favorite question to ask the interviewer during an interview?\n\n[29:13] The weirdest question he's been asked during an interview\n\n[31:02] David tells us how awesome DSDJ is\n\n[32:12] David lets us know how we can find him onlineSpecial Guest: David Tello.","content_html":"From nearly getting booted from college to going on to earn a PhD in Mathematics.
\n\nJoin the FREE open Slack mastermind community where I'll answer questions and keep you posted on bi-weekly office hours: https://bit.ly/artistsofdatascience
\n\nFollow the show in Twitter: @ArtistsOfData, on IG: @TheArtistsOfDataScience, on FB: facebook.com/TheArtistsOfDataScience, and on LinkedIn!
\n\n[02:32] The introduction for our guest today
\n\n[04:07] David talks to us about the struggles he faced when he emigrated to the USA from Peru.
\n\n[06:01] David talks about how mathematics turned his life around.
\n\n[06:39] Early in his career a professor told David that "it's clear that your first derivative is positive. The question is, is are secondary derivative positive?" He explains to us what this means in mathematical terms, what the professor meant using the metaphor. He walks us through the troubles he faced being on academic probation, how he tried to get a letter of recommendation, and he talks about the impact that meeting had on him.
\n\n[10:57] A meeting with a professor who told him that he wasn't good enough to be on this campus. He talks about the pain he felt when he wasn't sure what his path in life was going to be.
\n\n[11:54] He talks about his experiences at the University of Michigan and the impact of being around mathematicians that looked like him had on his career.
\n\n[12:24] I ask David what it's like to be a minority in a field filled with people who look like me (mostly Indians and Asians) and he how he views himself in this industry, and how being a minority in the field of mathematics is different from being a minority in the field of data science
\n\n[16:51] David talks about the struggles and obstacles he faced while trying to get past his PhD qualifying., how he almost didn't return back to school, and how he just kept coming back up after setbacks.
\n\n[23:46] He shares advice for how to manage the upskilling process thats required to be a data scientist.
\n\n[26:26] David tells us the one thing he wants people to learn from his story
\n\n[27:45] We jump into the lightning round: Python or R?
\n\n[27:55] Favorite classification algorithm
\n\n[28:26] Favorite question to ask the interviewer during an interview?
\n\n[29:13] The weirdest question he's been asked during an interview
\n\n[31:02] David tells us how awesome DSDJ is
\n\n[32:12] David lets us know how we can find him online
Special Guest: David Tello.
","summary":"From nearly getting booted from college to going on to earn a PhD in Mathematics","date_published":"2020-04-08T14:30:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/51dfff53-13d8-468a-90d2-11c2ba25ff47.mp3","mime_type":"audio/mpeg","size_in_bytes":19811098,"duration_in_seconds":1995}]},{"id":"b788f4cc-4f39-4582-97e8-a82414297107","title":"Scrum for Data Science Teams | Amit Jain","url":"https://theartistsofdatascience.fireside.fm/amit-jain","content_text":"Software engineer turned machine learning engineer talks about the challenges of taking research into production, as well as what it means to be a leader in data science, the importance of staying humble, and how to handle scrum on data science teams.\n\nJoin the FREE open Slack mastermind community where I'll answer questions and keep you posted on bi-weekly office hours: https://bit.ly/artistsofdatascience\n\nFollow the show in Twitter: @ArtistsOfData, on IG: @TheArtistsOfDataScience, on FB: facebook.com/TheArtistsOfDataScience, and on LinkedIn!\n\n[02:22] The introduction for our guest today\n\n[03:57] Amit talks to us about his journey from software engineering into data science and machine learning and touches on some of the challenges that he faced along the way and how he overcame them\n\n[08:18] He discusses some of the challenges he's seen freshers confront when taking something from proof of concept into production\n\n[10:54] How freshers can gain an intuition behind the data and the models they are building so that they can deliver business value\n\n[13:57] We talk about the challenges of monitoring model performance post-production\n\n[15:28] How agile methodology plays out on data science teams and the difference he's seen between its implementation in software enginerring and data teams\n\n[17:57] How to navigate the ambiguity of data science projects\n\n[19:56] What are some steps that someone can take to go from expiring data scientists to, to a data science or machine learning team lead? \n\n[22:38] The essential skills that are need that so individuals can be and remain successful as either a data scientist or a machine learning engineer\n\n[24:25] Some characteristics that he is looking for in a up and coming data\n\n[25:29] Apart from your stunning technical skills, what are some qualities you feel have contributed to your success a machine learning engineer?\n\n[26:31] The one thing that he wants people to learn from his story\n\n[26:48] Let's go ahead and jump into our lightning rounds. Python or R?\n\n[27:05] What's your favorite algorithm\n\n[27:39] What's a book that every data scientist or machine learning engineer should read? \n\n[27:51] His favorite question to ask an interviewee in a job interview\n\n[28:32] The stranges question he's been asked in a job interview\n\n[29:25] Amit lets us know how we can connect with him and where we can find him onlineSpecial Guest: Amit Jain.","content_html":"Software engineer turned machine learning engineer talks about the challenges of taking research into production, as well as what it means to be a leader in data science, the importance of staying humble, and how to handle scrum on data science teams.
\n\nJoin the FREE open Slack mastermind community where I'll answer questions and keep you posted on bi-weekly office hours: https://bit.ly/artistsofdatascience
\n\nFollow the show in Twitter: @ArtistsOfData, on IG: @TheArtistsOfDataScience, on FB: facebook.com/TheArtistsOfDataScience, and on LinkedIn!
\n\n[02:22] The introduction for our guest today
\n\n[03:57] Amit talks to us about his journey from software engineering into data science and machine learning and touches on some of the challenges that he faced along the way and how he overcame them
\n\n[08:18] He discusses some of the challenges he's seen freshers confront when taking something from proof of concept into production
\n\n[10:54] How freshers can gain an intuition behind the data and the models they are building so that they can deliver business value
\n\n[13:57] We talk about the challenges of monitoring model performance post-production
\n\n[15:28] How agile methodology plays out on data science teams and the difference he's seen between its implementation in software enginerring and data teams
\n\n[17:57] How to navigate the ambiguity of data science projects
\n\n[19:56] What are some steps that someone can take to go from expiring data scientists to, to a data science or machine learning team lead?
\n\n[22:38] The essential skills that are need that so individuals can be and remain successful as either a data scientist or a machine learning engineer
\n\n[24:25] Some characteristics that he is looking for in a up and coming data
\n\n[25:29] Apart from your stunning technical skills, what are some qualities you feel have contributed to your success a machine learning engineer?
\n\n[26:31] The one thing that he wants people to learn from his story
\n\n[26:48] Let's go ahead and jump into our lightning rounds. Python or R?
\n\n[27:05] What's your favorite algorithm
\n\n[27:39] What's a book that every data scientist or machine learning engineer should read?
\n\n[27:51] His favorite question to ask an interviewee in a job interview
\n\n[28:32] The stranges question he's been asked in a job interview
\n\n[29:25] Amit lets us know how we can connect with him and where we can find him online
Special Guest: Amit Jain.
","summary":"Software engineer turned machine learning engineer talks about the challenges of taking research into production, as well as what it means to be a leader in data science, the importance of staying humble, and how to handle scrum on data science teams.\r\nJoin the FREE open Slack mastermind community where I'll answer questions and keep you posted on bi-weekly office hours: https://bit.ly/artistsofdatascience\r\n\r\nFollow the show in Twitter: @ArtistsOfData, on IG: @TheArtistsOfDataScience, on FB: facebook.com/TheArtistsOfDataScience, and on LinkedIn!","date_published":"2020-04-08T14:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/b788f4cc-4f39-4582-97e8-a82414297107.mp3","mime_type":"audio/mpeg","size_in_bytes":16047123,"duration_in_seconds":1851}]},{"id":"35fe921d-62b6-4945-8015-3c55b34cbb50","title":"You ARE Going to Struggle But It Will Make You Better | Mikiko Bazeley","url":"https://theartistsofdatascience.fireside.fm/mikiko-bazeley","content_text":"There will be a lot of ups and downs on your journey, but how you end up depends on how you frame them...\n\nFollow the show in Twitter: @ArtistsOfData, on IG: @TheArtistsOfDataScience, on FB: facebook.com/TheArtistsOfDataScience, and on LinkedIn!\n\nJoin the FREE open Slack mastermind community where I'll answer questions and keep you posted on bi-weekly office hours: https://bit.ly/artistsofdatascience\n\n[02:41] The introduction for our guest today\n\n[04:26] Mikiko walks us down the career path that ultimately led to her becoming a data scientists. She came from a completelt non-technical background and through hardwork, determination, and grit she was able to accomplish her goals\n\n[07:37] She shares with us the various courses of studies she pursued while trying to find something that really resonated with her\n\n[09:43] She then shares with us how hard it was trying to find a job after graduation and eventually ended up working in a hair salon, which \n\n[12:07] She talks about how she used this opportunity to level up her skillset so that she could be more competitive in the marketplace\n\n[13:43] Mikiko talks to us about the first time she got involved with data anlaytics and goes into something she calls the \"MacGyver Principle\"\n\n[17:31] We talk a bit about thinking like a business leader and why after a certain point, an accumulation of memorized facts doesn't get you to the executive level.\n\n[21:09] Picasso and Data Science\n\n[23:55] What exactly is a growth hacker?\n\n[27:44] Mikiko shares some life lessons she learned from a long time mentor of hers\n\n[29:43] The importance of being so good they can't ignore you\n\n[32:55] Why you need to treasure a days work\n\n[35:58] Mikiko discusses where her desire to help aspiring data scientists comes from\n\n[39:45] She tells us about the concept of \"mentors at a distance\" and shes with us some of hers\n\n[40:58] Mikiko talks to us about passion, grit, and a growth mindset.\n\n[42:02] How the Pareto principle manifests itself in the day to day job of a data scientist\n\n[43:07] Passion is not innate or something to be found, its something to be cultivated through hardwork and sustained effort.\n\n[45:25] The concept of adaptability and how its helpful navigating the the data science job search process.\n\n[51:24] Mikiko talks about her experience being a woman in tech, being harassed on LinkedIn, and why women need to bring their full selves to the office.\n\n[01:03:42] The one thing Mikiko wants us to learn from her story\n\n[01:04:55] Jumping into the lightning round - Python or R?\n\n[01:05:07] Mikiko's favorite question to ask an interviewee during an interview.\n\n[01:06:22] The weirdest question she's been asked in an interview\n\n[01:07:31] She tells us what her favorite fiction book is\n\n[01:07:57] She shares her favorite non-fiction book\n\n[01:08:54] What she would say to 20 year old Mikiko Special Guest: Mikiko Bazeley.","content_html":"There will be a lot of ups and downs on your journey, but how you end up depends on how you frame them...
\n\nFollow the show in Twitter: @ArtistsOfData, on IG: @TheArtistsOfDataScience, on FB: facebook.com/TheArtistsOfDataScience, and on LinkedIn!
\n\nJoin the FREE open Slack mastermind community where I'll answer questions and keep you posted on bi-weekly office hours: https://bit.ly/artistsofdatascience
\n\n[02:41] The introduction for our guest today
\n\n[04:26] Mikiko walks us down the career path that ultimately led to her becoming a data scientists. She came from a completelt non-technical background and through hardwork, determination, and grit she was able to accomplish her goals
\n\n[07:37] She shares with us the various courses of studies she pursued while trying to find something that really resonated with her
\n\n[09:43] She then shares with us how hard it was trying to find a job after graduation and eventually ended up working in a hair salon, which
\n\n[12:07] She talks about how she used this opportunity to level up her skillset so that she could be more competitive in the marketplace
\n\n[13:43] Mikiko talks to us about the first time she got involved with data anlaytics and goes into something she calls the "MacGyver Principle"
\n\n[17:31] We talk a bit about thinking like a business leader and why after a certain point, an accumulation of memorized facts doesn't get you to the executive level.
\n\n[21:09] Picasso and Data Science
\n\n[23:55] What exactly is a growth hacker?
\n\n[27:44] Mikiko shares some life lessons she learned from a long time mentor of hers
\n\n[29:43] The importance of being so good they can't ignore you
\n\n[32:55] Why you need to treasure a days work
\n\n[35:58] Mikiko discusses where her desire to help aspiring data scientists comes from
\n\n[39:45] She tells us about the concept of "mentors at a distance" and shes with us some of hers
\n\n[40:58] Mikiko talks to us about passion, grit, and a growth mindset.
\n\n[42:02] How the Pareto principle manifests itself in the day to day job of a data scientist
\n\n[43:07] Passion is not innate or something to be found, its something to be cultivated through hardwork and sustained effort.
\n\n[45:25] The concept of adaptability and how its helpful navigating the the data science job search process.
\n\n[51:24] Mikiko talks about her experience being a woman in tech, being harassed on LinkedIn, and why women need to bring their full selves to the office.
\n\n[01:03:42] The one thing Mikiko wants us to learn from her story
\n\n[01:04:55] Jumping into the lightning round - Python or R?
\n\n[01:05:07] Mikiko's favorite question to ask an interviewee during an interview.
\n\n[01:06:22] The weirdest question she's been asked in an interview
\n\n[01:07:31] She tells us what her favorite fiction book is
\n\n[01:07:57] She shares her favorite non-fiction book
\n\n[01:08:54] What she would say to 20 year old Mikiko
Special Guest: Mikiko Bazeley.
","summary":"There will be a lot of ups and downs on your journey, but it all depends on how you view them...","date_published":"2020-04-08T14:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/35fe921d-62b6-4945-8015-3c55b34cbb50.mp3","mime_type":"audio/mpeg","size_in_bytes":37304987,"duration_in_seconds":4326}]},{"id":"194d698d-79b3-4916-a7d2-e297a4902cd2","title":"How to Crush Your Interviews | Alex Lim","url":"https://theartistsofdatascience.fireside.fm/alex-lim","content_text":"A mock interview with a rising star of our industry.\n\nJoin the FREE open Slack mastermind community where I'll answer questions and keep you posted on bi-weekly office hours: https://bit.ly/artistsofdatascience\n\nFollow the show in Twitter: @ArtistsOfData, on IG: @TheArtistsOfDataScience, on FB: facebook.com/TheArtistsOfDataScience, and on LinkedIn!\n\n[02:33] The introduction for the episode and our guest today\n\n[04:23] Alex tells us about the path that led him to data science and machine learning as a career choice\n\n[05:22] Alex tells us about the inspiration behind one of this data science projects\n\n[06:46] He then walks us through the plan of attack for coming up with a strategy for executing on his project.\n\n[07:48] Alex goes into detail about struggles he had to face kind of sourcing data, organizing his thoughts, the project structure, how he overcome these challenges\n\n[08:47] He walk us through his post application protocol for getting interviews\n\n[09:51] Some tips on how to find the right people in an organization to reach out to\n\n[11:10] Alex goes through, in detail, the challenges he faced in the job search, how many interviews he went on, and how he kept his head right during rejections.\n\n[13:13] Alex shares some books and some advice for cultivating the right mindset to navigate you through the job search ups and downs.\n\n[14:23] We start off the mock interview portion with the first question usually asked in an interview: Tell me about yourself.\n\n[15:50] Can you describe a time when you had to deal with competing priorities or competing deadlines? \n\n[16:42] What would you say is the most difficult type of person to deal with and how do you deal with that type of person?\n\n[17:50] Can you walk me through your discovery process when you're starting a new project? \n\n[19:10] Alex tells us the formula he uses to come up with such well crafted responses to commonly asked interview questions\n\n[21:04] Alex talks to us about his process for coming up with questions to ask during an interview\n\n[21:56] The one thing Alex wants us to learn from his story\n\n[22:31] Jumping into the lightning round:Python or R? \n\n[22:44] What's a book every data scientist should read? \n\n[23:00] His favorite question to ask the interviewer in a job interview?\n\n[23:40] His view on certifications and self-directed learning\n\n[24:15] Alex let's us know how we can connect with him and where we can find him onlineSpecial Guest: Alex Lim.","content_html":"A mock interview with a rising star of our industry.
\n\nJoin the FREE open Slack mastermind community where I'll answer questions and keep you posted on bi-weekly office hours: https://bit.ly/artistsofdatascience
\n\nFollow the show in Twitter: @ArtistsOfData, on IG: @TheArtistsOfDataScience, on FB: facebook.com/TheArtistsOfDataScience, and on LinkedIn!
\n\n[02:33] The introduction for the episode and our guest today
\n\n[04:23] Alex tells us about the path that led him to data science and machine learning as a career choice
\n\n[05:22] Alex tells us about the inspiration behind one of this data science projects
\n\n[06:46] He then walks us through the plan of attack for coming up with a strategy for executing on his project.
\n\n[07:48] Alex goes into detail about struggles he had to face kind of sourcing data, organizing his thoughts, the project structure, how he overcome these challenges
\n\n[08:47] He walk us through his post application protocol for getting interviews
\n\n[09:51] Some tips on how to find the right people in an organization to reach out to
\n\n[11:10] Alex goes through, in detail, the challenges he faced in the job search, how many interviews he went on, and how he kept his head right during rejections.
\n\n[13:13] Alex shares some books and some advice for cultivating the right mindset to navigate you through the job search ups and downs.
\n\n[14:23] We start off the mock interview portion with the first question usually asked in an interview: Tell me about yourself.
\n\n[15:50] Can you describe a time when you had to deal with competing priorities or competing deadlines?
\n\n[16:42] What would you say is the most difficult type of person to deal with and how do you deal with that type of person?
\n\n[17:50] Can you walk me through your discovery process when you're starting a new project?
\n\n[19:10] Alex tells us the formula he uses to come up with such well crafted responses to commonly asked interview questions
\n\n[21:04] Alex talks to us about his process for coming up with questions to ask during an interview
\n\n[21:56] The one thing Alex wants us to learn from his story
\n\n[22:31] Jumping into the lightning round:Python or R?
\n\n[22:44] What's a book every data scientist should read?
\n\n[23:00] His favorite question to ask the interviewer in a job interview?
\n\n[23:40] His view on certifications and self-directed learning
\n\n[24:15] Alex let's us know how we can connect with him and where we can find him online
Special Guest: Alex Lim.
","summary":"A mock interview with a rising star of our industry.\r\n\r\nJoin the FREE open Slack mastermind community where I'll answer questions and keep you posted on bi-weekly office hours: https://bit.ly/artistsofdatascience\r\n\r\nFollow the show in Twitter: @ArtistsOfData, on IG: @TheArtistsOfDataScience, on FB: facebook.com/TheArtistsOfDataScience, and on LinkedIn!","date_published":"2020-04-08T14:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/194d698d-79b3-4916-a7d2-e297a4902cd2.mp3","mime_type":"audio/mpeg","size_in_bytes":15518695,"duration_in_seconds":1504}]},{"id":"8c90acd9-56a1-403e-916a-9b02f23c9b3d","title":"Data Science Needs People Like YOU | Angela Baltes, PhD","url":"https://theartistsofdatascience.fireside.fm/angela-baltes","content_text":"Why diversity and inclusion is necessary in data scientist and why you shouldn't spend your time trying to \"spot a fake data scientist\".\n\nFollow the show in Twitter: @ArtistsOfData, on IG: @TheArtistsOfDataScience, on FB: facebook.com/TheArtistsOfDataScience, and on LinkedIn!\n\nJoin the FREE open Slack mastermind community where I'll answer questions and keep you posted on bi-weekly office hours: https://bit.ly/artistsofdatascience\n\n[2:32] The introduction for our guest today\n\n[03:55] Angela walks us through her background and how he started off as a Criminology major, did some data projects, fell in love with the field, and then decided that data is what she wanted to pursue.\n\n[05:32] She talks to us about the inspiration for doing the #100DaysOfCode challenge and how it helped combat imposter syndrome.\n\n[07:15] Angela walks us through the process for planning out and executing on her undertaking of the #100DaysOfCode challenge.\n\n[08:14] Angela tells us about her favorite day during the challenge.\n\n[08:55] She then tells us about her least favorite day during the challenge\n\n[10:14] Angela tells us how she stayed focused, disciplined, and maintained her execution during her #100DaysOfCode.\n\n[11:08] She talk to us about emotional intelligence and why we, as data scientists, need to start incorporating soft skills into our toolkit\n\n[12:52] Angela talks to us about some of the skills up-and-coming data scientists are missing and the importance of knowing your audience and how to present to them.\n\n[15:43] She also shares some tips on how to network with people in LinkedIn\n\n[16:53] She talks about including personalized messages with your request to connect.\n\n[17:28] She shares some tips with us on how to present findings and how to develop projects that add business value and address the bottom line.\n\n[18:51] Angela talks to us about being a woman in tech, why we need everyone in tech, and how our strength is in diversity.\n\n[19:54] Angela shares with us how she finds fulfillment outside of work.\n\n[20:54] Angela tells us the one thing she wants everyone to learn from her story.\n\n[21:44] Jumping into the lightning round: Python or R?\n\n[21:54] Angela tells us what her favorite algorithm is\n\n[22:19] We also learn the title of her PhD dissertation\n\n[22:27] She also shares her favorite data visualization tool with us\n\n[23:05] We learn what her data science superpower is\n\n[23:31] She shares the title of her favorite machine learning book\n\n[23:46] The largest data set that she's worked with\n\n[24:15] Angels lets us know where we can find her and how we can connect with herSpecial Guest: Angela Baltes.","content_html":"Why diversity and inclusion is necessary in data scientist and why you shouldn't spend your time trying to "spot a fake data scientist".
\n\nFollow the show in Twitter: @ArtistsOfData, on IG: @TheArtistsOfDataScience, on FB: facebook.com/TheArtistsOfDataScience, and on LinkedIn!
\n\nJoin the FREE open Slack mastermind community where I'll answer questions and keep you posted on bi-weekly office hours: https://bit.ly/artistsofdatascience
\n\n[2:32] The introduction for our guest today
\n\n[03:55] Angela walks us through her background and how he started off as a Criminology major, did some data projects, fell in love with the field, and then decided that data is what she wanted to pursue.
\n\n[05:32] She talks to us about the inspiration for doing the #100DaysOfCode challenge and how it helped combat imposter syndrome.
\n\n[07:15] Angela walks us through the process for planning out and executing on her undertaking of the #100DaysOfCode challenge.
\n\n[08:14] Angela tells us about her favorite day during the challenge.
\n\n[08:55] She then tells us about her least favorite day during the challenge
\n\n[10:14] Angela tells us how she stayed focused, disciplined, and maintained her execution during her #100DaysOfCode.
\n\n[11:08] She talk to us about emotional intelligence and why we, as data scientists, need to start incorporating soft skills into our toolkit
\n\n[12:52] Angela talks to us about some of the skills up-and-coming data scientists are missing and the importance of knowing your audience and how to present to them.
\n\n[15:43] She also shares some tips on how to network with people in LinkedIn
\n\n[16:53] She talks about including personalized messages with your request to connect.
\n\n[17:28] She shares some tips with us on how to present findings and how to develop projects that add business value and address the bottom line.
\n\n[18:51] Angela talks to us about being a woman in tech, why we need everyone in tech, and how our strength is in diversity.
\n\n[19:54] Angela shares with us how she finds fulfillment outside of work.
\n\n[20:54] Angela tells us the one thing she wants everyone to learn from her story.
\n\n[21:44] Jumping into the lightning round: Python or R?
\n\n[21:54] Angela tells us what her favorite algorithm is
\n\n[22:19] We also learn the title of her PhD dissertation
\n\n[22:27] She also shares her favorite data visualization tool with us
\n\n[23:05] We learn what her data science superpower is
\n\n[23:31] She shares the title of her favorite machine learning book
\n\n[23:46] The largest data set that she's worked with
\n\n[24:15] Angels lets us know where we can find her and how we can connect with her
Special Guest: Angela Baltes.
","summary":"Why diversity and inclusion is necessary in data scientist and why you shouldn't spend your time trying to \"spot a fake data scientist\".","date_published":"2020-04-08T14:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/8c90acd9-56a1-403e-916a-9b02f23c9b3d.mp3","mime_type":"audio/mpeg","size_in_bytes":15505294,"duration_in_seconds":1515}]},{"id":"bd097576-e543-496d-8b9a-5e64ff8b601e","title":"Data Science is Doomed, But WE Can Save It | Vin Vashishta","url":"https://theartistsofdatascience.fireside.fm/vin-vashishta","content_text":"One of LinkedIn's 2019 Top Voice's for Data Science shares why he thinks we're all doomed.\n\nJoin the FREE open Slack mastermind community where I'll answer questions and keep you posted on bi-weekly office hours: https://bit.ly/artistsofdatascience\n\nFollow the show in Twitter: @ArtistsOfData, on IG: @TheArtistsOfDataScience, on FB: facebook.com/TheArtistsOfDataScience, and on LinkedIn!\n\n[02:29] The introduction for our guest today\n\n[03:58] How Vin first heard of data science and what drew him into the field\n\n[05:22] Why data science is doomed\n\n[07:44] What separates the great data scientists from the merely good ones\n\n[10:22] What role does being creative and curious play in being successful as a data scientist and how can someone who doesn't see themselves as creative be creative? \n\n[13:04] What are some soft skills that candidates are missing that are really going to separate them from their competition? \n\n[15:23] Why women are excelling in data science \n\n[18:18] Vin talks to us about the growth mindset, gives us his definition of it, and how it's important that data scientists embrace this type of mindset.\n\n[19:58] It's not a zero sum game: If you are of a growth mindset, you're not only will want to teach, you want to learn and those two pieces of communication are essential\n\n[20:38] Vin reflects back on his career and recounts the importance of diversity\n\n[22:23] How a up and coming data scientists can tie a particular ability or a particular requirements with a business need specifically in in cases where one doesn't have any work experience to speak of? \n\n[23:47] How up-and-coming data scientists are actually in a better place then those who have been working on the same team for a long time\n\n[24:58] Could you share some tips or words of encouragement for our listeners who've got a couple of decades, let's say 10 to 20 years of a traditional IT experience under the belt who are now trying to break into data science. What challenges do you foresee them facing and how can they overcome some of those challenges they built?\n\n[27:04] I ask Vin what advice or insight he could share with people breaking into the field who are looking at these job postings? Some that seemingly want the abilities of an entire team wrapped up in one person and they end up feeling dejected or even discouraged from applying.\n\n[30:48] What are some challenges that a notebook data scientists face when it comes time to productionalize a model. And do you have any tips for them to overcome those hurdles? \n\n[33:11] If you've already mastered Python, Vin tells you what programming languages you should learn next\n\n[34:31] We touch on the importance of writing good comments in your code\n\n[35:04] What cloud technology should people pick up prior to breaking into the field? Or is this something they should even focus on if they're just looking to land their first role?\n\n[36:04] The one thing Vin wants us to learn from his story\n\n[36:37] We jump into our lightning round: Python or R? \n\n[36:47] What's your data science super power?\n\n[37:29] What's your favorite algorithm for regression and your favorite algorithm for classification?\n\n[37:51] . So what's the number one book you would recommend our audience read and your most impactful takeaway from it? \n\n[38:13] I go off into a tirade about how much that book has changed my life.\n\n[39:05] Certifications vs self-study\n\n[40:00] What motivates you?\n\n[41:44] The societial impact that COVID-19 is going to have\n\n[43:45] Vin let's us know how we can connect with him and shares a message for smaller businesses going through rough times due to our current global pandemic situationSpecial Guest: Vin Vashishta.","content_html":"One of LinkedIn's 2019 Top Voice's for Data Science shares why he thinks we're all doomed.
\n\nJoin the FREE open Slack mastermind community where I'll answer questions and keep you posted on bi-weekly office hours: https://bit.ly/artistsofdatascience
\n\nFollow the show in Twitter: @ArtistsOfData, on IG: @TheArtistsOfDataScience, on FB: facebook.com/TheArtistsOfDataScience, and on LinkedIn!
\n\n[02:29] The introduction for our guest today
\n\n[03:58] How Vin first heard of data science and what drew him into the field
\n\n[05:22] Why data science is doomed
\n\n[07:44] What separates the great data scientists from the merely good ones
\n\n[10:22] What role does being creative and curious play in being successful as a data scientist and how can someone who doesn't see themselves as creative be creative?
\n\n[13:04] What are some soft skills that candidates are missing that are really going to separate them from their competition?
\n\n[15:23] Why women are excelling in data science
\n\n[18:18] Vin talks to us about the growth mindset, gives us his definition of it, and how it's important that data scientists embrace this type of mindset.
\n\n[19:58] It's not a zero sum game: If you are of a growth mindset, you're not only will want to teach, you want to learn and those two pieces of communication are essential
\n\n[20:38] Vin reflects back on his career and recounts the importance of diversity
\n\n[22:23] How a up and coming data scientists can tie a particular ability or a particular requirements with a business need specifically in in cases where one doesn't have any work experience to speak of?
\n\n[23:47] How up-and-coming data scientists are actually in a better place then those who have been working on the same team for a long time
\n\n[24:58] Could you share some tips or words of encouragement for our listeners who've got a couple of decades, let's say 10 to 20 years of a traditional IT experience under the belt who are now trying to break into data science. What challenges do you foresee them facing and how can they overcome some of those challenges they built?
\n\n[27:04] I ask Vin what advice or insight he could share with people breaking into the field who are looking at these job postings? Some that seemingly want the abilities of an entire team wrapped up in one person and they end up feeling dejected or even discouraged from applying.
\n\n[30:48] What are some challenges that a notebook data scientists face when it comes time to productionalize a model. And do you have any tips for them to overcome those hurdles?
\n\n[33:11] If you've already mastered Python, Vin tells you what programming languages you should learn next
\n\n[34:31] We touch on the importance of writing good comments in your code
\n\n[35:04] What cloud technology should people pick up prior to breaking into the field? Or is this something they should even focus on if they're just looking to land their first role?
\n\n[36:04] The one thing Vin wants us to learn from his story
\n\n[36:37] We jump into our lightning round: Python or R?
\n\n[36:47] What's your data science super power?
\n\n[37:29] What's your favorite algorithm for regression and your favorite algorithm for classification?
\n\n[37:51] . So what's the number one book you would recommend our audience read and your most impactful takeaway from it?
\n\n[38:13] I go off into a tirade about how much that book has changed my life.
\n\n[39:05] Certifications vs self-study
\n\n[40:00] What motivates you?
\n\n[41:44] The societial impact that COVID-19 is going to have
\n\n[43:45] Vin let's us know how we can connect with him and shares a message for smaller businesses going through rough times due to our current global pandemic situation
Special Guest: Vin Vashishta.
","summary":"One of LinkedIn's 2019 Top Voice's for Data Science shares why he thinks we're all doomed.\r\n\r\nJoin the FREE open Slack mastermind community where I'll answer questions and keep you posted on bi-weekly office hours: https://bit.ly/artistsofdatascience\r\n\r\nFollow the show in Twitter: @ArtistsOfData, on IG: @TheArtistsOfDataScience, on FB: facebook.com/TheArtistsOfDataScience, and on LinkedIn!","date_published":"2020-04-08T14:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/bd097576-e543-496d-8b9a-5e64ff8b601e.mp3","mime_type":"audio/mpeg","size_in_bytes":24480026,"duration_in_seconds":2802}]},{"id":"a7c47ec0-ced5-4958-b80a-dbbe7754557c","title":"Microsoft Executive Shares Her Leadership Secrets | Pooja Sund","url":"https://theartistsofdatascience.fireside.fm/pooja-sund","content_text":"On this episode of The Artists of Data Science, we get a chance to hear from Pooja Sund, a technology leader who has over two decades of global technology and financial experience delivering business and organizational impact across a variety of roles.\n\nHer contributions and expertise have led her to be a powerful leader and energizer, and she currently serves as the Director of Technology and Analytics at Microsoft. \n\nShe gives insight into her journey into working for Microsoft, her tips to becoming more self-aware, and how she energizes her teams.\n\nPooja shares with us his powerful journey from switching career paths and landing her dream job at Microsoft. This episode is packed with advice, wisdom, and tips about cultivating a growth mindset. \n\nWHAT YOU WILL LEARN\n[10:29] Desirable qualities of a data scientist\n\n[17:38] Why mindset is key\n\n[24:32] How to develop self-awareness\n\nFind Pooja Online\nLinkedIn: https://www.linkedin.com/in/pooja3p/\n\nQUOTES\n[7:03] \"You need to really look at the things that are in front of you and decide what are the things that excite you…\"\n\n[12:42] …\"Rather than jumping in, take time to understand the problem.\"\n\n[24:42] \"I have seen people, including me, thinking that… I need to keep on learning…there's nothing wrong with it but at times you'll need to really look at the arsenal that you have created for yourself.\"\n\nSHOW NOTES\n[02:47] The introduction for our guest today\n\n[04:57] Pooja talks to us about the path she took from finance into data analytics and shares some tips for those making a similar transition\n\n[08:43] She shares some things that aren't taught in school about leadership, how to think outside the box so that you can align your team goals with the greater organizational goals, and tells us about the \"mindshare mindset\".\n\n[10:17] Pooja talks to us about the things we can do to cultivate the qualities of a good leader within ourselves, and what she is looking for when she's interviewing candidates.\n\n[11:54] She talks to us about her philisophy that insights aren't useful without understanding the key question to be answered and gives us tips for how we can cut through the BS to get to the heart of the question and find out the key question to be answered.\n\n[15:10] Pooja gives us her take on what it means to be a thought leader in data science and how one would be a thought leader even if they're operating out of an individual contributor role.\n\n[17:14] We talk about how to go from the \"impossible\" to the \"i'm possible\" mindset\n\n[20:55] We discuss the importance of the growth mindset, the nearly unlimited potential of human beings, and how the pursuit of skills is never time lost.\n\n[24:32] Pooja gives us her definition of executive presence and how important it is to be self-aware.\n\n[26:16] Pooja talks to us about servant leadership and why its so important. You're only a leader if people want to follow you, and everyone get's a bigger piece of the pie if we all work together to make the pie bigger.\n\n[27:24] Pooja talks to us about her experience being a women in tech, and that you need to be assertive and bring your ideas to the table.\n\n[27:45] The one thing Pooja wants all of us to learn from her story.\n\n[28:55] The lightning roundSpecial Guest: Pooja Sund.","content_html":"On this episode of The Artists of Data Science, we get a chance to hear from Pooja Sund, a technology leader who has over two decades of global technology and financial experience delivering business and organizational impact across a variety of roles.
\n\nHer contributions and expertise have led her to be a powerful leader and energizer, and she currently serves as the Director of Technology and Analytics at Microsoft.
\n\nShe gives insight into her journey into working for Microsoft, her tips to becoming more self-aware, and how she energizes her teams.
\n\nPooja shares with us his powerful journey from switching career paths and landing her dream job at Microsoft. This episode is packed with advice, wisdom, and tips about cultivating a growth mindset.
\n\nWHAT YOU WILL LEARN
\n[10:29] Desirable qualities of a data scientist
[17:38] Why mindset is key
\n\n[24:32] How to develop self-awareness
\n\nFind Pooja Online
\nLinkedIn: https://www.linkedin.com/in/pooja3p/
QUOTES
\n[7:03] "You need to really look at the things that are in front of you and decide what are the things that excite you…"
[12:42] …"Rather than jumping in, take time to understand the problem."
\n\n[24:42] "I have seen people, including me, thinking that… I need to keep on learning…there's nothing wrong with it but at times you'll need to really look at the arsenal that you have created for yourself."
\n\nSHOW NOTES
\n[02:47] The introduction for our guest today
[04:57] Pooja talks to us about the path she took from finance into data analytics and shares some tips for those making a similar transition
\n\n[08:43] She shares some things that aren't taught in school about leadership, how to think outside the box so that you can align your team goals with the greater organizational goals, and tells us about the "mindshare mindset".
\n\n[10:17] Pooja talks to us about the things we can do to cultivate the qualities of a good leader within ourselves, and what she is looking for when she's interviewing candidates.
\n\n[11:54] She talks to us about her philisophy that insights aren't useful without understanding the key question to be answered and gives us tips for how we can cut through the BS to get to the heart of the question and find out the key question to be answered.
\n\n[15:10] Pooja gives us her take on what it means to be a thought leader in data science and how one would be a thought leader even if they're operating out of an individual contributor role.
\n\n[17:14] We talk about how to go from the "impossible" to the "i'm possible" mindset
\n\n[20:55] We discuss the importance of the growth mindset, the nearly unlimited potential of human beings, and how the pursuit of skills is never time lost.
\n\n[24:32] Pooja gives us her definition of executive presence and how important it is to be self-aware.
\n\n[26:16] Pooja talks to us about servant leadership and why its so important. You're only a leader if people want to follow you, and everyone get's a bigger piece of the pie if we all work together to make the pie bigger.
\n\n[27:24] Pooja talks to us about her experience being a women in tech, and that you need to be assertive and bring your ideas to the table.
\n\n[27:45] The one thing Pooja wants all of us to learn from her story.
\n\n[28:55] The lightning round
Special Guest: Pooja Sund.
","summary":"Microsoft executive shares her secrets for success and effective leadership","date_published":"2020-04-08T14:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/a7c47ec0-ced5-4958-b80a-dbbe7754557c.mp3","mime_type":"audio/mpeg","size_in_bytes":20892411,"duration_in_seconds":2131}]},{"id":"422d9e78-80c1-4d02-806c-1c029069d8c8","title":"The Stories of Data Science","url":"https://theartistsofdatascience.fireside.fm/trailer","content_text":"Clips of one piece of advice that our guests want you to take away from their stories.\n\nJoin the FREE open Slack mastermind community where I'll answer questions and keep you posted on bi-weekly office hours: https://bit.ly/artistsofdatascience","content_html":"Clips of one piece of advice that our guests want you to take away from their stories.
\n\nJoin the FREE open Slack mastermind community where I'll answer questions and keep you posted on bi-weekly office hours: https://bit.ly/artistsofdatascience
","summary":"There are a lot of aspiring data scientists out there - what's the one thing you want them to learn from your story?","date_published":"2020-04-08T12:00:00.000-04:00","attachments":[{"url":"https://chtbl.com/track/693648/aphid.fireside.fm/d/1437767933/7086fae3-a152-4774-962c-4eaeba52bbdb/422d9e78-80c1-4d02-806c-1c029069d8c8.mp3","mime_type":"audio/mpeg","size_in_bytes":4679469,"duration_in_seconds":388}]}]}