The Data Science Education Podcast

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“I realized that it’s one thing to tell stories; stories are powerful on their own. But when you couple them with the quantitative information, they become an even more powerful storyteller together”

Welcome to the final episode of Season 5! In this episode, Carrie Diaz Eaton, an Associate Professor of Digital and Computational Studies at Bates College, delves into how she intertwines her passions for social justice and data science via digital narratives, duoethnography, and other mixed-method approaches. She expands upon her own work with the Rios Institute, running an open education resource sharing and community platform for STEM education, and her sabbatical time working with the Latinx community in Rhode Island, creating a general community resources database for the Providence area.

Thanks for tuning in this season, we’ll be back in the Fall with Season 6! Until then, we’d love to hear your opinions on this season, as well as what you’d like to hear next season. I’ve linked a survey here to let us know your thoughts. See you all in the Fall!

“I realized that it’s one thing to tell stories; stories are powerful on their own. But when you couple them with the quantitative information, they become an even more powerful storyteller together.”

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“We’re starting to see many more people using JupyterLite in class because it’s much easier to grow and scale…”

In this episode, we’re speaking with Jeremy Tuloup, who is a Technical Director at QuantStack and a contributor to Project Jupyter. In this conversation, he talks about Project Jupyter’s project, JupyterLite, which will allow users to run code directly from the browser and how this can make coding more accessible to users. He also shares upcoming goals the Jupyter team is working on, as well as their plans on making interactive coding more accessible to students and educators.

“With all these developments [to Jupyter] we really hope that this is going to lower the barrier of entry for accessing these type of tools and also making interactive computing, especially in the browser with JupyterLite, more accessible to more people.”

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“An emphasis that we really put on the work that the students are doing is to think about the ethical landscape in which the work is taking place…how to do that work responsibly and to do what is actually going to be meaningful to the stakeholders versus maybe what's the coolest new technique in machine learning.”

In this episode, Sarah Stone, Executive Director of the University of Washington’s eScience Institute, discusses her work with the Data Science for Social Good program, which works with undergraduate and graduate students to create and integrate community-driven projects. She also delves into the work that the eScience Institute employs at the university, spreading data expertise across departments that each department can further customize. Finally, she ends by discussing how her Ph.D. in Oceanography has informed her of the need to train existing researchers across all disciplines.

“We need to not just be training students; we need to be training the existing faculty and existing researchers, and recognizing that a lot of this training hasn't been part of the history of those fields, so meeting people where they're at in order to be able to do that.”

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In this season’s installment of “Data Science Graduates in Industry,” we’re speaking to three UC Berkeley alumni to learn more about how they’re applying what they’ve learned at UC Berkeley in industry. Samantha Chean-Udell is a Digital Product Associate at Converse, Elda Pere is working as a Senior Data Scientist at Curate, and Carlos Ortiz is working as a Data Scientist at Snap, Inc. Tune in to hear their thoughts about how their degrees prepared them for the workforce, as well as what they wish they had known before getting into the industry.

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"Now in data science, I find that what the students need is more of an ability to integrate different models and different techniques on their own."

In this episode, Vik Gopal and David Chew from the National University of Singapore (NUS) delve into the university's growing Data Science major, as well as their roles as instructors for courses in Data Visualization and Data Science in Practice. They also discuss the university-wide required data literacy course, a no-coding class taken by most first-year students, as well as share their insights into the differences between teaching Statistics and Data Science.

"We came up with projects, but the projects have to be very vague and open-ended, just like in practice, which will put [students] in a situation where they feel kind of lost...it's up to them to be resourceful to learn what they need."

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“…We try to make high-level, engaging curriculum for math, and the best part about it is we figured it out using the lens of data science.”

In this episode, Niki Wells, a K-12th Grade Curriculum Developer for Chicago Public Schools and Curriculum Editor for Skew the Script, shares her experiences teaching high school math and how she’s used cultural relevance and data science in order to get her students excited about math. While speaking on that topic, she also shares her own personal journey with math, what led her to also fall back in love with the subject, and how she uses that to create engaging content that her students love.

“For us as teachers, we really had to wake up and realize that we’re all doing the exact same thing and if we can pull from each other, we can actually make engaging math lessons that make students see that it’s real and actually want to do it.”

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“I think over time, in every discipline, there’s more and more eagerness to connect with data science, or AI, or both.”

On this episode, Yolanda Gil, a Research Professor of Computer Science and Spatial Science at the University of Southern California and Principal Scientist at USC’s Information Sciences Institute, discusses how USC makes programming and data science more accessible to non-programmers in hopes of creating more diversified data science teams. She also shares how student’s eagerness to connect with data science and AI has led USC to “rethinking” current degrees and incorporating data science across their curriculum.

“We’re not just creating new degrees, we’re also rethinking our current degrees.”

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“We take the perspective that data science is truly something unique and different, it’s not just stats plus programming…”

Hello, and welcome back to the fifth season of the Data Science Education Podcast. In this episode, we’re chatting with Bradley Voytek, a professor in cognitive science at the University of California, San Diego. He shares with us how he created the first data science class at UCSD and how that later led him to being involved in the creation of the Data Science major within UCSD’s Halıcıoğlu Data Science Institute. He also touches on his neuroscience background, how that shaped the way he approaches data science education, and why data science education should be an interdisciplinary effort between all fields.

“COGS 9 is meant to be the first foray into the world of data, for all of its goods and all of its ills; so we created that class with the intent of teaching students how to think about data and the power and the peril.”

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“You hear a lot about the metaphor ‘data is the new oil’ and I think data really is the new oil – maybe it’s even the new fabric for the way that we all interact with each other in the world…”

Hey Everyone, and welcome to the final episode of Season 4. In this episode, we’re speaking with Zarek Drozda, the director of Data Science 4 Everyone, a coalition created by the University of Chicago Center for Radical Innovation for Social Change, aimed at increasing equitable access to data science education for K-12 students. Today, he shares how the coalition is assisting high school educators in bringing data science to their schools, some of the national initiatives going on that are bringing data science to K-12, and how to get involved.

“Technology is a moving target, so I always push people towards thinking about how we can focus on the fundamentals and the fundamentals on how data is shifting society in really big ways.”

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“In this emerging world in our sciences, everyone has to understand data science and how to move forward with it.”

In this episode, we’re speaking with Deb Agarwal who is Scientific Data Division Director at Lawrence Berkeley National Laboratory, which is a United States Department of Energy national laboratory managed by the University of California. She shares her experiences getting started in data science research and how she’s seen the field of data science evolve into what we know it as today. She also speaks about why increasing representation in data science research is so important and how we can encourage diversity within the research field.

“For me, [research is about] enabling something that wasn’t possible before.”