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.

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

Access the full transcript for this episode

“I think the biggest thing I would say is just involve students in real work as early as possible. I think sometimes we have in our mind, oh, we cannot do research with students until they’re advanced in their mathematical studies, but I’ve actually found this isn’t true. I think if there’s a compelling project and students are excited about it, they are really great at learning the tools that they need to do it, and that’s something we as faculty can also help with. Students are able to make really meaningful contributions early in their careers. In terms of teaching or mentoring, I think it’s just about teaching thinking, not tools.” —Kamila Larripa

In this episode, we speak with Kamila Larripa, Associate Professor of Mathematics and Data Science Program Lead at Cal Poly Humboldt, along with her former students John Gerving and Jonathan Juarez. Kamila shares about the development of Humboldt’s new Data Science major and its "data for good” mission, as well as her California Education Learning Lab project, which builds a cross-campus community of practice, fosters data literacy, and bring climate justice modules into introductory science courses for students. Students John and Jonathan reflect on their undergraduate research experiences, highlighting how real-world data projects helped identify their interests and build collaboration skills.