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“Students can say, I understand what’s in this data, because I’m part of the data.” — Alana Unfried

In this episode, we speak with Alana Unfried, Professor of Statistics at Cal State Monterey Bay, about the future of statistics and data science education. Alana shares her path from classical statistics training to undergraduate teaching, educational research, and her work on MASDER, a national project focused on measuring student motivation, attitudes, and learning environments in statistics and data science classrooms.

Alana discusses why data science education needs stronger research tools, better shared data, and a clearer understanding of what students are actually experiencing in the classroom. She explains how MASDER helps faculty collect survey data, compare their classes to national trends, and contribute to a larger picture of what is working across institutions. The conversation also explores major gaps in access to data science education, especially between highly selective and more inclusive schools, and how different departments shape what students learn. Alana also reflects on the growing role of generative AI in data science education and why faculty development will be essential as the field continues to evolve.

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“Data science, to me, is all about breaking down walls—breaking down walls between disciplines, and breaking down walls between faculty and students.”

In this episode, we speak with Kagba Suaray, Professor of Mathematics and Statistics at Cal State Long Beach, about building a more community-centered vision for data science education. Kagba shares how his work connects data science to local issues in Long Beach and Compton, from public health and housing justice to educational equity, while creating opportunities for students to learn through real, meaningful data. He discusses the power of interdisciplinary collaboration, breaking down barriers that keep students from seeing themselves as “data people,” and designing programs that make data science more inclusive, applied, and community-driven. Kagba also reflects on what it takes to build partnerships, support underrepresented students, and help communities tell their own stories through data.

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“A big passion was, how do we think through care? Improvement is fundamentally like a first-order AI problem, not just how to make it easier to do clinical care of today…but how do you make new types of things possible?…If we dig really deep into what’s happening: Why? Why is it caught at this time? Why do we see it in this way? And I think latent into every one of these problems is a frontier AI problem…Through everything—trials and evidence—I think there’s the same type of dynamism we see like in general software, and this kind of pace of change / of improvement that we feel in other parts of AI. Bringing that type of pace to health is the mission of my career, and I’m excited to work on it.”

In this episode, we sit down with Adam Yala, Assistant Professor at UC Berkeley and UCSF and co-founder of Voio, to explore how AI is reshaping the future of healthcare. Adam walks through his path from research to building real-world systems, and why computational health is emerging as its own distinct field rather than just an application of AI.

We dive into what it actually takes to build in this space, from understanding clinical complexity to navigating challenges like data access and compute. Adam also shares how his experience across academia and startups has shifted his perspective on speed, innovation, and creating meaningful impact.

Finally, he offers advice for students and aspiring data scientists, emphasizing the importance of adaptability, curiosity, and focusing on the problems you want to solve in a world where technology is constantly evolving.

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“I think we had this feeling that there’s so many students that don’t make it to calculus, and that in the field of data science and the STEM field itself, we really have a gap to fill because we’re missing all of that knowledge and expertise that those students that don’t ever get through calculus would really bring to the field.”

In this episode, we speak with Mikahl Banwarth-Kuhn (MBK), Assistant Professor of Mathematics at Cal State East Bay, about reimagining the traditional calculus pathways for today’s data science students. MBK helped develop a new course sequence, Math for Data Science, designed to remove barriers that often prevent students from reaching calculus. She discusses the motivation behind the course and whether traditional pen-and-paper calculus sequences still serve data science students today. MBK advocates for a more intuitive, application-driven approach to help students more deeply understand concepts like derivatives, optimization, and differential equations.

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“When you go out and talk to other people, you realize that you become the opposite of being siloed. You really start to realize that you might have been in an echo chamber when you were talking amongst your own colleagues, and when you start to hear other people, you go, Oh, there’s more that I could understand.”

Today, we speak with Rachel Saidi, Professor in the Math, Statistics, and Data Science Department and Data Science Program Director at Montgomery College, a two-year college outside Washington, DC. Rachel shares her path from teaching math to statistics to data science, and what it’s like to scale a data science program in the community college setting, with the goal of catering to students of all ages and experiences. She tackles holistic data science education, combining curriculum, experiential learning, speaker series, and more, while also acknowledging difficulties with constraints like faculty capacity and transfer articulation with four-year universities. Finally, she reflects on how professional organizations can help educators find community and stay on top of best practices, and offers advice to educators and learners on how to tackle data science teaching and learning today.

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“I think of data as being the base of the scientific pyramid that we have. You literally can’t do science if you don’t have data—and good data. If your data is bad, then your science is going to be bad. So really, at the heart of science and research is having good data that people can find, and people can access and use.”

In this week’s episode, we speak with Tasha Marie Snow, a cryosphere researcher who works at the intersection of Earth system science, data science, cloud computing, and open science. Snow is a Co-Founder and Lead Scientist for the CryoCloud cloud-computing community and platform, and works at both NASA and the University of Maryland. She touches on how her work with NASA satellite data, such as ICESat-2 data, focuses on making large, complex datasets more accessible and usable for researchers. She also discusses her role in supporting geoscience researchers to transition their workflows to the cloud via CryoCloud within JupyterHub, as well as the educational benefits of shared computing environments.

Listen to Tasha’s talk from JupyterCon in November here, and view the interactive Antarctica map notebook Eric mentioned here!

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“The goal of the students is not to learn how JupyterHub works. The goal is to learn what’s the topic of the course. So we want to make it as easy as possible to get into an environment where they can learn what they’re actually there to learn, and not get in their way with the tools that they’re supposed to be using.”

Welcome to season 11 of the podcast! To kick off the new season, we interviewed Min Ragan-Kelley, Senior Open Infrastructure Architect at Berkeley Institute for Data Science (BIDS) and a founding member of JupyterHub. Min discusses the origin story of JupyterHub and how it evolved into the scalable platform that students and researchers alike utilize daily, reflecting on key design decisions that have shaped the platform into what it is today. He describes the importance of the platform to “get out of the way” of students in order to best aid in learning how to operate within a computing environment. Finally, Min touches on his passion for open source projects and what he hopes to come of it in relation to data science education.

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“From my own experience, you don’t need to really be the perfect data scientist to do the work. I think, especially at Berkeley, there’s a lot of pressure to know everything. That’s not necessarily the case…For a lot of the types of work that I do and in my industry, you don’t actually need to have or be the most technical person…The thing that’s actually more important, and if you want to get hired in politics or in political work is actually having domain knowledge.” —Ian Castro

In the last episode of the season, as always, we sit down with some recent Data Science graduates from UC Berkeley. Today, we talked with Ian Castro, Political Database Manager at Equis Research and former DATA 8 course staff member, who talked about how teaching and building foundational data science courses shaped his commitment to tackling issues like housing, inequality, and political representation. We also talked with Lydia Sidhom, Data Reporter at The Washington Post, who reflected on how her experiences with DATA 8 and working for the Daily Cal helped pull her towards data journalism. Together, Ian and Lydia show how recent graduates are using data to analyze and explain the world!

“I think being a journalist—especially a data journalist—requires you to be kind of like a mini expert on every story that you do. So being curious about many different fields and diving into different kinds of data is really a big plus.” —Lydia Sidhom