Faculty and Student Voices from Cal Poly Humboldt: Data Science in Action (feat. Kamila Larripa, John Gerving, and Jonathan Juarez)
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 LarripaIn 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. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit datascienceeducation.substack.com
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Equity in the Classroom: Allison Theobold on Teaching Data Science with Empathy
Access the full transcript for this episode“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.” This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit datascienceeducation.substack.com
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Scaling Impact: How Community Colleges are Shaping Data Science Access (feat. Kyla Oh)
Access the full transcript for this episode“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?” This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit datascienceeducation.substack.com
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Supporting Teachers and Building Communities (feat. Hannah Kurzweil)
Access the full transcript for this episode“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.” This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit datascienceeducation.substack.com
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Building a Hub for Data Justice (feat. Dr. Amy Yeboah Quarkume)
Access the full transcript for this episode“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.” This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit datascienceeducation.substack.com
Produced by UC Berkeley's Data Science Undergraduate Studies. In this space, you will hear from a variety of distinguished Data Science educators and professionals. The individuals we’ll speak with are diverse in experience and perspective, but share the common goal of shaping the future of Data Science Education! Transcripts available at https://datascienceeducation.substack.com/
To learn more about UC Berkeley's Data Science Undergraduate Studies, visit our website at https://cdss.berkeley.edu/dsus. datascienceeducation.substack.com
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