A Conversation with The Data Professor | Chanin Nantasenamat


April 22nd, 2022

53 mins 13 secs

Season 19

Your Host

About this Episode

Support the show: https://www.buymeacoffee.com/datascienceharp
Find Chanin online: https://th.linkedin.com/in/chanin-nantasenamat
Watch the video of this episode: https://youtu.be/pCHubISFBTI

Memorable Quotes from the show:

[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."

Highlights of the show:

[00:00:36] Guest Introduction

[00:03:12] Where you grew up and what it was like there?

[00:04:22] What brought you back to Thailand?

[00:05:15] How different is your life now than what you thought it would be growing up?

[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?

[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?

[00:09:47] What is bioinformatics and how did you get into that?

[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?

[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?

[00:19:00] What is drug discovery? Where does data science enter into the mix here?

[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?

[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?

[00:27:09] YouTubing, but where did that spark to help other data scientists come from?

[00:31:40] where is the science in data science?

[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?

[00:36:50] Talk to us about a few of your blog posts.

[00:43:43] It is 100 years in the future, what do you want to be remembered for?

[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?

[00:46:38] What are you currently reading?

[00:48:06] What song do you currently have on repeat?

[00:48:38] What are your pet peeves?

[00:49:02] Do you have any nicknames?

[00:49:22] What talent would you show off in a talent show?

[00:49:44] When was the last time you changed your opinion about something major?

[00:51:29] What's your favorite city?

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