The Artists of Data Science

The ONLY self-development podcast for Data Scientists

Everybody Has A Unique Gift and Perspective | Deborah Berebichez, PhD

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. She 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.

Deborah 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. This episode is packed with actionable advice along with wisdom from someone who has had tremendous success!

Some notable segments from the show

[17:11] What value Deborah believes data science will bring within the next few years

[20:43] Deborah’s role model for being curious and inquisitive

[27:42] Actionable tips for cultivating the habit of critical thinking

[40:07] Advice on how to be the hero when you feel like a failure

[51:47] Advice for women that want to break into tech

Deborah's journey into data science

As a physicist, Deborah began her career working on wall street, where she analyzed stock market data. Her background was in computational physics, which is very closely related to data science.

One day, she was invited to the STRATA conference, which was a humbling experience for Deborah. It gave her more insight into how data science can be applied outside of wall street stock data, and the various types of data that can be analyzed.

She always knew she wanted to have a large impact with her work. That led her to Metis, where she was able to connect education with data science. Her work at Metis has allowed her to teach others data science, while also taking part in projects that help underprivileged areas.

[3:54] I just so I think it was a serendipitous path in that I didn't really expect to become a data scientist.

I had never heard about the term. And maybe about 15 years ago when I had finished my PhD, I, I started working in Wall Street.

Like many physicists, because I wanted to be able to get a green card and stay in the U.S.. And as you know, there was a strong connection between the financial markets and the PhD programs in physics and math and statistics across the country. And so it was kind of not even raised eyebrows. There were over a few thousand physicists working Wall Street. And so I finished two post-doctoral fellowships after Stanford at Columbia University and at NYU, at the Grant Institute in Applied Math and Applied Physics.

And then I started working in physics and I realized that academia was a bit too isolating for me. And I wanted to communicate more with the public and evangelize different products and have an impact with my coding and what I was doing. I did computational physics, by the way. And so it was pretty close to Data science. I just would not we would just not call it that. But I had never realized that what I was doing was a very narrow form of Data science, meaning I was, you know, quite proficient with that particular aspect of machine learning.

But when it came to Data science, which was much more vast than what I was doing. And so I was humbled by an experience I had at STRATA, the big data science conference when I was interviewed on video. And I think I said something that I, I, I've regretted saying ever since, which was oh, but come on, Data science is nothing new. You know, we have physicists and Wall Street people doing it for the past 50 years and nothing has changed. And, you know, I was proven wrong quite quickly because we definitely were analyzing things with different algorithms and we were analyzing different kinds of data that we never analyzed before, such as audio and text and images and what not. And so there there were a lot of differences. And also in Data science, you we required to translate the insights that were gained into quite, you know, lay and entertaining terms so that the stakeholders in a company could actually enact policies in a. And change things in vier the company into a different direction to gain success based on those insights. So that's how I started. I finished my appose socks. I worked in Wall Street for six years and then I realized that what I was doing in Wall Street was research and and again, do working with Data. It was the stock market Data to be specific. But I also knew that I wanted to have more of an impact in the world and do good for people.

And at the time, I had been following my friend Hilary Mason, who's a renowned Data a scientist, and I loved her work.

And I saw Cathy O'Neil and other people do use the Data science analysis that they did for bringing more ethics in into the world and and more visibility into under served communities when it came to doing data science work.

And so I ended up wanting to connect education with Data science. And that's how I came about Metis, which is where I'm the currently the chief data scientist at. And we're a Data science training company where I've had the chance to not only train people by teaching a machine, learning bootcamp and create curriculum, but also where I've had the chance to do Metis for good projects like helping create a live map of needed and things during an earthquake that happened in Mexico about four years ago. And people could go to the map and in real time see what kinds of items or people were needed in different locations. So it's been a wonderful world of work where I can actually not only help people, but also educate companies and others in Data literacy. And that's what I loved about my work and Data.

Where is the field headed in 2-5 years?

Data science has been slowly partitioning into more and more specific professions. People in the field have tried to capture a lot under the umbrella of data science, but that has not worked well. People want to know how data science can serve them. With this in mind, we are going to see a shift in who has access to data. People will now have insights at every level.

We are also going to see a shift in how data is reported to executives with this change.
Companies will need to hire people with heavy engineering backgrounds or data science backgrounds for those things.

At the same time, as sophisticated and complex algorithms become successful at solving certain problems, we're going to see more people hire specific experts within data science. This will create more jobs in the field for people with specific skills and training in certain areas, as well as people with less technical backgrounds.

[9:51] “Yeah. So I think that a lot of Data science has been slowly partitioning into more and more specific professions.

We have tried to capture a vast amount of things under the umbrella of Data science, and that has not worked well because companies have been hiring Data scientists, some of whom have expertise in Data management.

Others more in sophisticated algorithms like deep learning and others more in a more statistical base. Data analysis. And so I think people want to know what they can get out of data science.

And so we're seeing the proliferation of dashboards and easy platforms like Tableau that are going to be able to be used within an organization with very little training.

That is pretty much anyone will be able to have access having an initial training to the data that a company has. And people will have insights at every level. So we're going to see that.

And those people are going to be translators or bridging bridges between the executive levels of the company and the companies Data.

And so we will need to hire very kind of heavy engineering background or Data science backgrounds for those things. At the same time as algorithms, sophisticated and more complex algorithms become successful at solving certain problems, we're going to see more people hire specific bands within Data science.

That is somebody who is exclusively an expert in NLP algorithms or in visualization techniques and whatnot. And so I think that more and more jobs are going to open.

But we're going to they're going to require more specific skills and more training in certain areas, as well as people from less technical backgrounds having access to more commodity sized platforms.”

What will separate great data scientists from the rest of them?

A good data scientist is someone that can efficiently manipulate, clean, and gain insights from a data set and can propel a company forward.

A great data scientist can think outside the box and outside the established algorithm. They have the ability to critically think. They check to make sure the statistics are correct, and are not deceived by the data source. They are aware of possible agenda behind the data source, and make sure that data is not being misused to propagate opinions or certain political views.

[12:19] “Oh, that's a good question. I think somebody who has the skills that I call critical thinking will definitely advance. Way more than the good data scientist. So I think we could define a good data scientist as somebody who is able to efficiently manipulate, clean and gain insights from a data set that have actionable metrics that can propel a a company or an institutions business forward, whereas a great data scientist will be somebody who can think outside the box and outside of the established algorithm in both. Go back to the basics and make sure that the statistics are correct, which a lot of people don't think about now and not be deceived by, say, the sample that gather the data. The agenda behind the data source out of the company that's providing the data and whatnot and really gain deeper insights by creating an algorithm that specifically tests what they know they want to test with with the metrics that are as specific as to the errors that I get propagated with statistically measuring only a sample of the population.

And we're not really paying attention to how at every step of a data science project, we can unintentionally or sometimes intentionally propagate these errors and misuse data science to gain insights. That are eliminating from our goal the version of a comprehensive truth, so to speak, like we can, you know, test voter Data set, by eliminating unconsciously the opinions of certain minorities or certain other political views. And, of course, then gain insights that are not actually representative of what the political ecosystem is.”

Key takeaways from the episode

Art or science

[34:00] I caution people who view it as an art. Although there are aspects to data science that are artful, the best data scientists are those who are meticulous in their scientific process.

The creative process

[37:08] A good data scientist can look at the data and begin to piece together a story. What is the data telling us? How will it affect change? This is how the creative process manifests itself in data science.

How to be a hero when you feel like a failure

[40:07] It’s by reminding ourselves that our measure of success is not about how many likes we get, but how we measure our learning and our growth based on what our goal was before we took on an enterprise. Just learning to appreciate how much you have grown and come forward is an incredible skill that you need to nurture and practice.

Biggest myth

[44:14] You need to be a genius in every aspect of data science to do well. In reality, the vast majority of data scientists are good at a few aspects of data science. Have some general knowledge, but do not strive to become an expert before you get your first job.

Diversity and inclusion

[51:47] Don't let the perception and the stereotypes that have formed your unfortunate biases govern what you do and how you behave. Act as if you're confident, even if you don't feel confident yet, and things will happen for you.

To foster inclusion in the data science community, make women role models visible. Put technical women in highly visible roles. That's the best you can do.

Memorable quotes

[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.”

[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.”

[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”

[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.”

The one thing that Deborah wants you to learn from her story

[55:47] You can make your dreams come true no matter what. Do not believe what people think of you and what your abilities are. Always seek for that inner voice that tells you what you want to do and believe in yourself.

From the lightning round

Deborah's data science superpower

Being very detail oriented! She's known for finding even the most infuriating of bugs in code - like a missing comma buried in deep in some module.

Deborah talks about the most fundamental truth of physics

Science is not about facts. It's about discovery and an ever increasing, more comprehensive view of reality. The school system doesn't do us justice when it comes to learning science, it's more than just selecting an answer on a multiple choice test. The real world is messy, and finding the truth is an iterative process. Science is process, and it is a process of discovery.

The best advice that Deborah has ever received

The best advice she got was from her husband's PhD adviser. He once said: "Hold your water". It didn't click in the moment, but she has since understood it to mean: Don't engage. Don't try to be the one who's right. Don't try to be the one who wins an argument.

The advice that Deborah would give to her 20 year-old self

At 20 years old, Deborah was in Mexico studying for an undergraduate degree in philosophy, and was being told by everyone around her not to pursue the hard sciences. If she could go back in time, she would tell herself to pursue those dreams that she has. Even if you are not the best at them. It's better rather do that - do the things that are hard - than stay doing something that comes easy to me.

What's a topic outside of data science we should study

Critical thinking.

Recommended book

“What Do You Care What Other People Think?” By Richard Feynman

Song on repeat:

David Bowie, Changes

Books and other media mentioned in this episode

Books by Ruth Spiro (for children).
Outrageous Acts of Science

Where you can find Dr. Berebichez online

Personal Website

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