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“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

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“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.

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“The driving framework of how I think about equity in my classroom is from a paper by Rochelle Gutiérrez, who is a fairly predominant math educator, about equity being of these two axes: the dominant and the critical. It has four main components—access and achievement—which form the dominant axes, and identity and power, which form the critical axes. I think of these four ideas as guiding the way that I think of equity across every classroom I design.”

In this episode, we speak with Allison Theobold, Assistant Professor of Statistics at Cal Poly SLO. Allison shares her journey from economics to statistics and data science education, and explore her research on equitable pedagogy. She discusses frameworks for equity and how these inform her teaching practices, as well as how her own experiences as a learner in the age of AI help to inform her own teaching.

“For me, a lot of this work comes from me studying and reflecting on how my pedagogy impacts who might be successful in my class, and what types of students may or may not be successful. How can I broaden that more, in terms of assessment, classroom spaces, and access to resources, whether it’s through their peers, me, or outside of class. So thinking about and reflecting on ways in which the way I’m teaching might not be as favorable for some students as opposed to others.”

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“It struck me that academic integrity is a serious issue, but one whose treatment I felt was overly punitive. I don’t want us to have to act as police for our students. Students very much want to do the work, but they often are just ignorant, for whatever reason, of what academic standards at the university level are. And so I wanted to instill this kind of restorative justice framework to make moments where students do falter and they do make mistakes, I wanted to turn those into teachable moments where they could learn, and turn what is a bad situation into perhaps a positive one.” —Taiyo Inoue

Today, we speak with Sarah Senk and Taiyo Inoue, co-hosts of My Robot Teacher, which is a podcast affiliated with the California Learning Lab. Sarah and Taiyo discuss how they both bring their respective lenses of comparative literature and mathematics to examine the question and implementation of AI in education, sharing concrete classroom and academic policy uses for LLMs. They touch on academic integrity through a restorative-justice lens, the idea of AI as an opaque cultural archive, and examining higher education as a “slow disaster.” Finally, they end with valuable advice for faculty listening in, giving tips on how to approach AI.

To hear more about Sarah and Taiyo’s thoughts about all things AI and education, listen to their podcast, My Robot Teacher!

“When we talk about cultural memory, we’re thinking about things that no one individual or social group could hold in their minds. It’s the stuff that is recorded in archives, libraries, cultural practices, arts, etc., and so all of that stuff trained large language models. And so I think you can think about large language models as a kind of archive, but a pretty opaque one.”—Sarah Senk

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“The biggest challenge for us initially was just, where does data science live? Is it in your math department? Is it in your computer science department? Who's going to teach it? Are you going to have a math faculty? Computer science faculty? And then once you decide where it's going to be, then you have to ensure that you have faculty who are willing to teach, because the class is challenging: it does require some programming, as well as statistical analysis, so it's a lot for a faculty. Usually faculty don't have both of those skills, so that's a challenge.”

In this episode, we sit down with Kyla Oh, Acting Dean of Math, Science, and Career Education at Berkeley City College. Kyla shares her unique path from engineering to patent law and now community college leadership. Together, we discuss the evolving role of community colleges in expanding access to data science education, as well as the challenges that come with building out new programs. Kyla discusses the importance of collaboration across departments and institutions as a means of expanding data science across schools, and highlights the power of support programs and internships to keep students motivated.

“I treat my students like clients. If my students are not showing up to class, then I feel like, oh, I'm doing something wrong. And the same with our industry partners—I want to be able to bring in industry partners, so I have to treat them like clients. Like, how can we best serve you and ensure that that partnership is mutually beneficial?”

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“I feel like most practicing teachers grew up in the same educational system that I did where you are penalized for getting the wrong answer, and you kind of get into this flow of needing to have the correct answer. And that has really informed the way that they teach—they're afraid to be wrong. And so the number one thing I work with teachers on is really building up their confidence to be flexible in the classroom.”

In this episode, we sit down with Hannah Kurzweil, STEM educator and Community Manager for Data Science for Everyone. Hannah shares her unconventional journey to STEM teaching and national community-building in data literacy, and reflects on what it means to support teachers in embracing flexibility and designing interdisciplinary curriculum. Together, we discuss the barriers to bringing data literacy into K-12 classrooms and strategies for building stronger educator communities.

“A lot of teachers feel like they're working in silos…a lot of teachers, often because of the median salaries for educators, don't feel like professionals, and that's really hard when you are so passionate about your work and you don't feel like you're able to be a valued member in society for the work that you're doing. And that's why a community of educators is so important, to bring those levels and that sense of community back, and professionalism back, to the classrooms and the classroom teachers.”

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“At Howard, we're looking at having people understand that data is the new oil, right? Everyone has access to it, everyone should be aware of it, everyone should be able to understand where they fall when it comes to their own data, but not knowing that cost. So we want everyone to kind of have a space to say, I'm not a computer scientist, I am not someone who loves statistics, but I want to get involved in this ecosystem of data science, where can I start? And social impact and social justice is where everyone can find a space to begin to understand why data is so important.”

Today, we sit down with Dr. Amy Yeboah Quarkume—also known as Dr. A—Associate Professor at Howard University and Director of Graduate Studies for the Applied Data Science and Analytics program. Dr. A shares her journey from Africana Studies into data science, and how she’s building Howard into a hub for data science, social justice, and environmental justice. Together, they discuss her groundbreaking projects like What’s Up with All the Bias and the HELLO BLACK WORLD curriculum, the importance of addressing “data pollution” in marginalized communities, and how students of all ages can find their way into coding and data science.

“Let's create more space to make mistakes. And even though mistakes cost—because the environmental impacts of all this…there are impacts to what we do—being able to make a mistake and learn should be something that we should continue to encourage. Coding takes practice, it takes patience.”

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“We're all used to tracking changes in Word, so why wouldn't we want to have something like that for our code? And we're all used to Google Docs where we can collaborate in real time, so why wouldn't we want to be doing that with our code too? So both for keeping track of changes and for facilitating collaboration, anyone who I work with, I mentor them in using GitHub”

Welcome to Season 10! To kickoff our new season, we sit down with Jade Benjamin-Chung, an Assistant Professor at Stanford University in the Department of Epidemiology and Population Health, to talk about her journey into public health and becoming a leader in reproducible data science practices. Throughout the episode, we discuss the creation of her lab manual outlining best practices in data science, mentoring in low-resource settings, and promoting ethical data practices.

“If a student isn't able to be part of data collection, then I really encourage them to build a relationship with a local collaborator who knows the data really deeply. For example, I'll have a student who is really bright with coding, but has less experience working with real world data sets. I'll have them pair up with someone from, say, Bangladesh, where I do a lot of research, and they'll kind of mentor them in coding…and the person working in Bangladesh will mentor them in the data”