The Data Science Education Podcast

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“Years and Years ago […] at Cal Poly, Statistics and Computer Science were in the same college, but now [they’re in separate colleges] and it’s been that way for quite a long time, but it’s kind of funny that we used to have a lot more proximity to each other and now [...] we kind of come back around.”

In this episode, Hunter Glanz, an associate professor at Cal Poly San Luis Obispo joins us to discuss the “fun challenge” of creating a cross-disciplinary major, such as Data Science, and how cross disciplinary collaboration and community can grow from this challenge. He also shares why it’s so important for educators to continue to be open to learning and shares a few of his favorite places to find inspiration and remain curious about Data Science research and education.

“Scholarship is not just important for the sake of pushing the boundary of our knowledge but because it makes our classroom experience that much richer for our students.”

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“So if we’re able to integrate the [computer science and data science] topics in one course, we let students experience both fields before they have to make a premature choice of which one they want to study in more depth […] But, I think it helps to put them side by side, so that students can appreciate what they both are and how they might use them.” 

In this episode, Kathi Fisler, a research professor and the associate director of the undergraduate Computer Science program at Brown University, speaks on how Bootstrap, a program she co-directs, introduces Data Science education to the K-12 school system. Among other things, she speaks on the importance of integrating Data Science curricula and Data Engineering into Computer Science coursework and ponders the future of Data Science education and how to foster a sense of community within the field. 

“Data science education is poised to get to some really interesting places, if we can figure out how to get there”

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“We’re lucky at UVA because we have this school of Data Science… it’s a freestanding school with its own dean. The nice thing about this is that it creates a ‘known identity’ for where Data Science sits on campus…”

Welcome back to Season 3 of the Data Science Education Podcast! We’re happy you’re listening with us again, and we hope you look forward to this upcoming season.

In this episode, Brian Wright, an Assistant Professor and the Director of Undergraduate Programs at the School of Data Science for the University of Virginia, discusses the importance of fostering a sense of ‘Data Intuition’ within their Data Science program. He also outlines how to build a meaningful Data Science capstone project for students, while urging educators to prioritize maintaining domain knowledge throughout Data Science coursework.

“We should be honest to the field and ask ourselves, ‘what do we really think Data Science is?’… I think the process of going through [this question] is important for the field, seeing as it’s still evolving.”

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“What can you do with Data? What stories can you tell with Data? … Communication is not a soft skill in the derogatory sense, it is THE skill in some sense.”

In this episode, Sara Stoudt, an Assistant Professor at Bucknell University and co-author of Communicating with Data, discusses the importance of communication in Data Science as an art and a creative medium. She sheds light on how challenging it was for her to describe her process for writing in data analysis, especially as she worked with Berkeley Professor, Deborah Nolan, on publishing their book. Stoudt also elaborates on her perspective towards the Data Science Education community - she marvels at how supportive and open educators are when it comes to the sharing of resources and shares her hopes to engage with students at deeper levels.

“I’m hoping that the future is leading more people into thinking they could find themselves in Data Science, that they could identify as a Data Scientist.”

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“If I was in charge, Data Science would be a requirement at the collegiate level, the high school level, the junior high level, and at the grade school level … Data is so centric to everything, I think we’re missing the boat by not having it integrated into everything.”

In this episode, Steven Levitt, the William B. Ogden Distinguished Service Professor of Economics at the University of Chicago and co-author of Freakonomics shares his experiences as a leader in the ‘Data Science for All’ movement and as an active proponent of Data Science Education in general. Additionally, Levitt elaborates on his perspective on how Data Science integrates within the field of Economics; he discusses how courses that he teaches utilize Data Science tools in practical ways, and pinpoints the challenges in integrating these tools at different educational levels. In this conversation, Professor Levitt shares many more meaningful insights into his busy world of teaching, writing, and podcasting - we hope you enjoy this 'must-listen’ episode!

“If Economics continues to be the major it is now, and Data Science emerges as an alternative, a good chunk of people who are currently going into economics will realize that it makes much more sense to become a Data Science major, with a minor in economics - and I would say they’re making the right choice!”

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“In designing Data Science curriculum, we should have a participatory design approach with individuals from several different communities that have been marginalized or disenfranchised by Data Science or AI techniques.”

In this episode, Gloria Washington, an assistant professor at Howard University, shares her experiences structuring her Data Science-driven course to accommodate students who may be hesitant to join the field; through this discussion, she also urges institutions to construct their Data Science curriculum with marginalized communities in mind. Later in the conversation, Gloria speaks on her research that utilizes human-centered data.

“I truly believe that there should be Data Science courses where faculty swap in and out. An applied statistic person can teach the first half of the class, a Data Scientist can teach the middle part, and a Data Ethics person can pop in-and-out at every single topic to describe how the techniques could be used against different populations/communities.”

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“[An] important thing to realize is that different schools in different places or at different levels have different student bodies, different needs and goals. So I think it’s important that people across the globe share their content so that people at different institutions that are looking to introduce Data Science have a good sense of the different ways that they could introduce Data Science and teach these ideas because the same course is not going to work everywhere for all types of students.”

In this episode, Suraj Rampure, a lecturer at the Halıcıoğlu Data Science Institute at UCSD and recent UC Berkeley graduate, shares his experience with Data Science instruction. When reflecting on his experiences, he speaks on the importance of student teaching assistants in developing content based on student perspective and experience. Suraj also discusses enhancing the course offerings at UC San Diego in order to cope with the shift to virtual learning and to attract more students.

With regard to data literacy, he notes “a lot of them [students] will use tools that we’re giving them to do what they were going to do anyway but with a quantitative lens... it’s about bridging the gap with other disciplines.”

Check out Suraj’s website, with open access to his application materials for teaching faculty positions.

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“If you’re a Community College thinking about whether or not to do this [bring Data Science to your institution], just jump in! There’s people here that are trying to make it work, and I think we have some answers.”

In this episode of the Data Science Education Podcast, Katia Fuchs discusses her experiences as the Chair of City College of San Francisco’s Mathematics department. Katia also elaborates on her efforts in bringing Data Science curriculum to the community college level. Katia discusses how her institution adapts to curriculum demands with their varying resource levels, and calls for her community to band together!

“It’s been a pleasure to meet my colleagues at community colleges in Southern California and realize that we’re all stumbling in the same places. And there’s a beauty to that ‘synchronized stumbling’… but if more of us tread water, it’ll be easier not to sink!”

Katia Fuchs is the department chair of City College of San Francisco’s Mathematics Department.

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In this podcast episode, Rebecca Nugent, the Associate Head and Co-Director of Undergraduate Studies for the Carnegie Mellon Statistics & Data Science Department, discusses the importance of making Data Science education accessible. She speaks about her work at CMU and how she is studying how to teach Data Science and build entry points into the field for people of all backgrounds and ages in a bid to make Data Science more inclusive. She also encourages open-mindedness in teaching Data Science, highlighting that the education does not need to start with programming and people can benefit from learning about the conceptual ideas first as well. Furthermore, she emphasizes the importance of data literacy as well as the risk of inequity in society that limited access to data poses. Finally, she discusses how scaling poses a challenge in the ever-growing field of Data Science and how educators need more investment and resources for pedagogical innovations.

Rebecca Nugent is the Stephen E. and Joyce Fienberg Professor of Statistics & Data Science, the Department Head and Co-Director of Undergraduate Studies for the Carnegie Mellon Statistics & Data Science Department, and an affiliated faculty member of the Block Center for Technology and Society. She received her PhD in Statistics from the University of Washington in 2006.  Prior to that, she received her B.A. in Mathematics, Statistics, and Spanish from Rice University and her M.S. in Statistics from Stanford University.  She was won several national and university teaching awards including the American Statistical Association Waller Award for Innovation in Statistics Education and serves as one of the co-editors of the Springer Texts in Statistics.  She recently served on the National Academy of Sciences study on Envisioning the Data Science Discipline:  The Undergraduate Perspective and is the co-chair of the current NAS study Improving Defense Acquisition Workforce Capability in Data Use.  She is the Founding Director of the Statistics & Data Science Corporate Capstone program, an experiential learning initiative that matches groups of faculty and students with data science problems in industry, non-profits, and government organizations.  She has worked extensively in clustering and classification methodology with an emphasis on high-dimensional, big data problems and record linkage applications.  Her current research focus is the development and deployment of low-barrier data analysis platforms that allow for adaptive instruction and the study of data science as a science.

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Welcome back to Season 2 of The Data Science Education Podcast! We’re happy to be back, and hope you look forward to the return of biweekly episode releases here, and on our Spotify!

In the seventh episode of our Podcast, Jim Colliander speaks on his experiences helping create projects like Syzygy, Callysto, and 2i2c. Through this conversation, he provides valuable perspective on how to respond to the university demand for interactive computing while maintaining and promoting STEM diversity/inclusion.

Jim Colliander is a Professor of Mathematics at the University of British Columbia and the Director of the Pacific Institute for the Mathematical Sciences.