Access the full transcript for this episode

“I literally collected 150 jobs on Indeed.com and parsed out all of the skills that were mentioned in all the jobs, created a graphic and said, Okay, here's the courses we already have that have these skills, and here's the skills I need to create courses for.”

Today, we sat down with Crystal Wiggins, a pioneering educator in two-year college data science programs at Connecticut State Community College. Crystal shares her journey in developing Connecticut’s first two-year data science program, which has since expanded to five campuses. She discusses her innovative approach to project-based learning, teaching students to "get comfortable with the uncomfortable," and preparing them to adapt in a rapidly evolving field. Crystal also delves into her leadership role in nationwide conversations about data science in community colleges, her work with organizations like AMATYC (American Mathematical Association of Two-Year Colleges), and her vision for industry partnerships in the classroom.

“Don't be afraid to dive in. You do not need to be an expert. You can learn this with your students. There's many things that students ask me, and I'm like, Well, let me show you how to find the answer. And I was actually finding the answer for myself because I didn't know, but that's what's great about the field; it's more about teaching them how to find answers than it is knowing everything yourself. So again, my slogan, be comfortable with the uncomfortable, is like the slogan for data science for me, because you're never going to know everything, and that's what I tell my students.”

Access the full transcript for this episode

“I developed a class called DS 100, which is in a lot of ways very similar [to Data 8], with the primary objective being, I want people to walk away from the class with saying I understand what data science is. I can do a little bit of programming, and now it's up to me whether I think it's interesting or not. I don't want anyone ever to feel like they can't do it. It's just whether or not they enjoy doing it.”

In this episode, we sit down with Langdon White from Boston University to discuss his journey from software consulting to becoming a key figure in BU's growing data science program, starting off with BU Spark! He shares the challenges of expanding a data science curriculum, including the launch of new programs, and his overarching mission to make data science accessible to students of all backgrounds. Langdon also explores innovative teaching methods like experiential learning and gamification, while highlighting the importance of diversity, ethics, and inclusivity in data science education.

“I continue to think that our biggest challenge in this industry is making sure that we have representation from all backgrounds, right?…Every student should be walking out of the school with an expectation of inclusion and diversity, but also ethics. And that the ethics falls to you…and you know, encouraging students to step up and represent themselves, from an ethical perspective, an inclusion perspective, and the diversity perspective.”

Access the full transcript for this episode

“So what we do in Data Feminism is try to synthesize a whole lot of feminist ways of thinking about the world, that have to do with questions of bias and oppression, that have to do with questions of sort of unequal power, and who gets to make choices about how to design systems — with these sort of really broad social questions, we try to apply them to data science as both a field and as a practice.”

Join us as we engage in a conversation with Lauren F. Klein, Associate Professor at Emory University and Director of the Digital Humanities Lab. Klein shares her unique journey from a background in comparative literature to pioneering the field of digital humanities, where she bridges the gap between computational methods and humanistic inquiry. We delve into her innovative projects, including her influential "Data Feminism" book and the "Data by Design" project, exploring how these works challenge traditional data science perspectives and emphasize the importance of context, history, and ethics in data visualization.

“The point that I'm trying to make in this project is that if we take this historicized, almost literary and critical, humanistic lens to this history, we can see how the people who were designing data visualizations were either asking very similar questions to the kinds of questions about responsible data visualization that we're asking today, or they weren't. And because of that, we can see how their visualizations — far from being some sort of neutral representation of data — in fact, represented a certain policy sort of unreflective politics that I think we also need to be able to identify again, so that we don't reproduce that in the present.”