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“It's not a matter of should we all get involved in data or data science - it's which aspect of data science am I interested in, based on my culture, my background, my beliefs, and what's important to me. And so I think that's the beginning of introducing someone to data science, is showing them how you use it every single day.”

In this episode, we’re chatting with Kari Jordan, the Executive Director of the Carpentries. Throughout the conversation, Jordan emphasizes the importance of building a shared understanding of data science terminology and creating an inclusive, collaborative atmosphere. She highlights the significance of partnerships and the potential for data scientists to make a positive impact on a broad range of issues, which students can choose from based upon their interests. Above all, Jordan emphasizes the importance of equity within the data science community, providing increased access to everyone, no matter their background or learning style.

“Data science is for everyone. It really is. It is nothing to be coveted or held to yourself. That's one reason why I believe that open access journals, open source material, open source education, is not to be a secret. You have to collaborate in order to make change happen. And in order to get a diversity of perspectives and ideas, you need other people's opinions, and you need their expertise. No one person knows at all. And so if you're an educator, I would just encourage you to create an atmosphere that's conducive to collaboration. Because those collaborative data science projects are the ones that really change the world.”

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“It doesn't take too long to look into social phenomena quantitatively to realize that we have profound inequities that are structural in societies around the world. It's not only the United States; these inequities are promoted by those who own resources, those who want to keep certain privileges, those who keep certain wealth at the expense of others. And this introduces abject situations.”

Hello and welcome back to the sixth season of the Data Science Education Podcast! In this episode, we’re chatting with Juan Gutierrez, Professor and Chair of Mathematics at the University of Texas at San Antonio. Gutierrez begins by detailing how his research on malaria utilizing mathematical biology helped him to realize the importance of looking into social phenomena quantitatively in order to bring to light the inequities in the world. He goes on to reflect upon how his introduction to programming at 10 years old in Colombia allowed him to immigrate to the US in 2001, fluent only in programming and not yet in English, emphasizing to him the value of education.

“We have to recognize that every individual comes with different strengths and deficiencies in their knowledge. So having an adaptive learning system that adjusts to those peaks and valleys might help accelerate the discovery and the acquisition of skills so that we can truly provide meaningful pathways to competence. We want to make this as easy as possible for everybody, to bring all participants in the educational experience to a level of competency that we require in society to function properly.”

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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.”