The Data Science Education Podcast

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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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Access the full transcript for this episode
“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!


This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit datascienceeducation.substack.com

Access the full transcript for this episode
“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.


This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit datascienceeducation.substack.com

Access the full transcript for this episode
“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


This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit datascienceeducation.substack.com

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“It struck me that academic integrity is a serious issue, but one whose treatment I felt was overly punitive. I don’t want us to have to act as police for our students. Students very much want to do the work, but they often are just ignorant, for whatever reason, of what academic standards at the university level are. And so I wanted to instill this kind of restorative justice framework to make moments where students do falter and they do make mistakes, I wanted to turn those into teachable moments where they could learn, and turn what is a bad situation into perhaps a positive one.” —Taiyo Inoue
Today, we speak with Sarah Senk and Taiyo Inoue, co-hosts of My Robot Teacher, which is a podcast affiliated with the California Learning Lab. Sarah and Taiyo discuss how they both bring their respective lenses of comparative literature and mathematics to examine the question and implementation of AI in education, sharing concrete classroom and academic policy uses for LLMs. They touch on academic integrity through a restorative-justice lens, the idea of AI as an opaque cultural archive, and examining higher education as a “slow disaster.” Finally, they end with valuable advice for faculty listening in, giving tips on how to approach AI.
To hear more about Sarah and Taiyo’s thoughts about all things AI and education, listen to their podcast, My Robot Teacher!
“When we talk about cultural memory, we’re thinking about things that no one individual or social group could hold in their minds. It’s the stuff that is recorded in archives, libraries, cultural practices, arts, etc., and so all of that stuff trained large language models. And so I think you can think about large language models as a kind of archive, but a pretty opaque one.”—Sarah Senk


This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit datascienceeducation.substack.com