The Data Science Education Podcast

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“...When [we] decided to take the leap and do a data science course, one of the things that we intentionally made as a requirement was for a course to have a low bar of entry, but a high probability of success.”

In today’s episode, we speak with Mahmoud Harding, who is a math instructor at North Carolina School of Science and Mathematics. At NCSSM, he teaches an introductory data science class based off of UC Berkeley’s Foundations of Data Science course. Today, he tells us about his experience bringing data science to high schoolers and how they’ve been reacting to the course. He also shares how he sees the future of data science education in high school and why it’s so important to teach high school students how to work with data.

“Data science is not just for advanced students who get accepted to a public residential high school in North Carolina. It’s for any student who’s willing to take a chance to learn something they don’t know.”

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“…But for math in general, I think data science education bridges this gap between the data that affects our lives and […] the more theoretical understandings of these concepts… ”

Today, we’re speaking with Mario Bañuelos who is an assistant professor and Associate Chair of Mathematics at California State University, Fresno. In this episode, he shares how he incorporates data science education into his math courses and how to get intellectual buy-in from students. He also speaks on the importance of linking mathematics and data science to social contexts.

“I think part of [bringing social context into math classes] is having students understand that the data affects people and that the models you create are gonna have effects on those people.”

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“The second half of the vision [for my course], is that machine learning concepts should be accessible.”

In this episode, we speak with Zachary Pardos, an associate professor at the UC Berkeley School of Education and director of the Computational Approaches to Human Learning research lab, where they focus on education data science and how to utilize AI to shape education at all levels. Today, he discusses the field of education data science and ponders its possible future. In addition, he shares some findings from the research that he’s done and from his own experiences in the classroom.

“…We need to be in conversation with policy makers, with administrators, with decision makers, so that it can be […] a joint learning process of how data scientists, domain experts, policy makers, and AI […] can collaborate.”

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For the third episode of the season, we’re bringing back three Cal Data Science alumni who are applying what they’ve learned to the healthcare industry. Each of them will be sharing their unique story and how they paved their own paths into the healthcare industry. Jordan Jomsky is currently working as a data scientist for the National Institute of Health (NIH), Tiffany Yu is pursuing her Masters Degree in Clinical Informatics Management at Stanford while working as a consultant at Kaiser Permanente, and Rosey Stone is working as a software engineer at Myriad Genetics. Tune in to hear about their undergraduate journeys and how they were able to combine their passions for data science, biology, and healthcare to pursue their dream careers.

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“It’s been really exciting to me in my new job … to start thinking about how data science can be taught across a whole university and touch every major, every discipline, every aspect of what we’re doing in our mission, in research, in teaching, and in engagement.”

On this episode, we welcome Rachel Levy, the executive director of North Carolina State University’s new Data Science Academy, who shares her excitement and future plans for the new program. She also gives us insight into how NC State built their new program around interdisciplinarity and how they’re creating a data science curriculum that is accessible and insightful for both students and faculty of all majors and levels of education.

“But we know that the big challenges of the future … are going to have to be solved in an interdisciplinary manner so we’re going to have to figure out how to do that, not only in our course work, but also in our research and how the public think about solving problems, and then about how we support engagement going forward.”

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“...As humans, we’re prone to fool ourselves… the good news is we can learn to use scientific reasoning and methods to counteract this constant tendency.”

Welcome, and welcome back to the Data Science Education Podcast! We’re thrilled to be back and equally as thrilled to have you listening with us again.

We’re kicking off Season 4 by speaking with Dr. Julia Koschinsky, the executive director of the Center for Spatial Data Science at the University of Chicago. In this episode, she discusses what spatial data science is and how UChicago is incorporating it into their curriculum. She also delves deep into the ideas of scientific reasoning and attitude and how UChicago uses them to re-engage their students in the “thrill of discovery.”

“What makes this exciting to me is that’s about changing the way we think about teaching data science, including spatial data science.”

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“Any paper that I write, there is some sort of data science… and I noticed how even a little bit of coding knowledge goes a long way.”

In this episode, David Broockman, an associate professor of political science at UC Berkeley, shares his perspective on how the fields of data science and political science intersect within coursework. David shares his experiences with teaching PoliSci3, a course that introduces statistics and data science to political science students. He also discusses his team-based approach to teaching the course and expresses the importance of pushing students out of their comfort zone.

“I think there is no way to be a good data science educator unless you push students outside of their comfort zone. In fact, it’s especially important to take the students who start off being a little afraid of data science and coding, because once pushed, they realize they have these capabilities they didn’t know they had!”

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“Engaging in Data Science at the undergraduate level [at UC Berkeley] first wasn’t any particular person’s idea…they concluded that: instead of first creating a graduate program and having things filter down to the undergraduate level years later, we would start with a first year course for undergraduates and build from there!”

In this episode, John DeNero, the Associate Dean of Undergraduate Studies in Computing, Data Science, and Society and Associate Teaching Professor in the UC Berkeley EECS department, discusses his role in bringing Data Science Education to undergraduates at Cal. He also speaks on his passion for teaching introductory courses, and how he approaches creating them. John has played an integral role throughout our UC Berkeley Data Science journey, and we hope you enjoy hearing his unique perspective and words of wisdom!

“Our goal at Berkeley has always been to make this a campus-wide endeavor where all the different domain experts get to contribute to students’ education. That’s a really natural thing to do at the undergraduate level.”

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In this special episode of the Data Science Education Podcast, we’ve featured three interviews from recent UC Berkeley Data Science graduates! All three of these graduates are currently applying their Data Science education in industry, and each has a unique perspective and story to share. Amal Bhatnagar (at the time of this interview) was working as a Data Scientist at the startup, ProducePay. Alex Nakagawa is currently working as a Full Stack Developer for the LA Clippers. Claire Dominique Medina is currently working as a Junior Data Science & Analytics Associate at Publicis Sapient. Tune in to hear these recent grads discuss how their education prepared them for their daily work, and also learn what they wish their coursework emphasized more!

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“We surveyed admissions officers around the country and we found that they’re much more familiar and comfortable with a Calculus course on a student’s transcript. Whereas with Data Science courses, they sometimes think it may not be as rigorous.”

In this episode, Pamela Burdman, an independent policy analyst on college access, readiness, and the founder of Just Equations joins us to discuss how mathematics curriculum could successfully integrate within Data Science Education. She also shares her unique perspective on how Data Science education is affecting certain education policy.

“Data Science is integral in learning how to ‘do’ math, and use quantitative reasoning and think mathematically! Data Science may be seen as much more effective than a lot of the traditional ways to teach math. ”