The Data Science Education Podcast

Recent Data Science Graduates: Transfer Pathways, Real-World Projects, and Career Advice (feat. Mike Alfaro & Annet Isa)

Access the full transcript for this episode [https://docs.google.com/document/d/e/2PACX-1vRFk1iT7yjN27vkqHCdx3yWr5DkniyV2eeUUmzMoK1BkES85VLLwM4_Cwlnbk8GL8pErGvbn0O3aI2m/pub] “Data science shows up in a lot of places where people don’t expect, but at the end of the day, the goal is the same: using data skills and data tools to help organizations make better decisions.”— Mike Alfaro “If it feels hard, it’s because it’s unfamiliar. The more you do it, the easier it will get, and the more fun you’re going to have.”— Annet Isa In this episode of the UC Berkeley Data Science Education Podcast, we speak with recent data science students Mike Alfaro and Annet Isa about their different paths into the field. Mike shares how a data visualization course at Montgomery College first introduced him to the power of storytelling with data, eventually leading him to internships in marketing analytics, transportation, and environmental work. His story highlights how community college, hands-on technical skills, and networking can open doors into data science careers. We also hear from Annet Isa, who returned to school after two decades of professional experience and found data science through her interest in patterns, prediction, and messy data. She discusses a capstone project using GIS, AI, and aerial imagery to identify solar panel installations in Montgomery County, showing how data science can support real-world environmental work. Together, their stories offer practical advice for students beginning their own journeys, from building projects to reaching out to professionals and staying patient through the learning curve. 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 [https://datascienceeducation.substack.com?utm_medium=podcast&utm_campaign=CTA_1]

Data About Data Science: Rethinking How We Teach (feat. Alana Unfried)

Access the full transcript for this episode [https://docs.google.com/document/d/11HDdcdvKALzEeCy4091Io8huyKm_XOmCK0exiPKlCgc/edit?tab=t.0] “Students can say, I understand what’s in this data, because I’m part of the data.” — Alana Unfried In this episode, we speak with Alana Unfried, Professor of Statistics at Cal State Monterey Bay, about the future of statistics and data science education. Alana shares her path from classical statistics training to undergraduate teaching, educational research, and her work on MASDER, a national project focused on measuring student motivation, attitudes, and learning environments in statistics and data science classrooms. Alana discusses why data science education needs stronger research tools, better shared data, and a clearer understanding of what students are actually experiencing in the classroom. She explains how MASDER helps faculty collect survey data, compare their classes to national trends, and contribute to a larger picture of what is working across institutions. The conversation also explores major gaps in access to data science education, especially between highly selective and more inclusive schools, and how different departments shape what students learn. Alana also reflects on the growing role of generative AI in data science education and why faculty development will be essential as the field continues to evolve. 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 [https://datascienceeducation.substack.com?utm_medium=podcast&utm_campaign=CTA_1]

Breaking Down the Walls: Community-Centered Data Science Education (feat. Kagba Suaray)

Access the full transcript for this episode [https://docs.google.com/document/d/e/2PACX-1vQcuSQ3rh-Xa54Mxqg_tkkXKXCwRn62SLYcNRvPpPKPfhKTaOze_5mEXIa9BrD3wI5ER2QuR1L5ggNL/pub] “Data science, to me, is all about breaking down walls—breaking down walls between disciplines, and breaking down walls between faculty and students.” In this episode, we speak with Kagba Suaray, Professor of Mathematics and Statistics at Cal State Long Beach, about building a more community-centered vision for data science education. Kagba shares how his work connects data science to local issues in Long Beach and Compton, from public health and housing justice to educational equity, while creating opportunities for students to learn through real, meaningful data. He discusses the power of interdisciplinary collaboration, breaking down barriers that keep students from seeing themselves as “data people,” and designing programs that make data science more inclusive, applied, and community-driven. Kagba also reflects on what it takes to build partnerships, support underrepresented students, and help communities tell their own stories through data. 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 [https://datascienceeducation.substack.com?utm_medium=podcast&utm_campaign=CTA_1]

A New Frontier: Computational Health and AI Innovation (feat. Adam Yala)

Access the full transcript for this episode [https://docs.google.com/document/d/e/2PACX-1vSSNkx3OKz23wY63vUIIrOstTRdUJQEFabdDA7HnVeaGlbnNx3xv-Mjl0OleKnuQy2dWc2sEQDMb9oH/pub] “A big passion was, how do we think through care? Improvement is fundamentally like a first-order AI problem, not just how to make it easier to do clinical care of today…but how do you make new types of things possible?…If we dig really deep into what’s happening: Why? Why is it caught at this time? Why do we see it in this way? And I think latent into every one of these problems is a frontier AI problem…Through everything—trials and evidence—I think there’s the same type of dynamism we see like in general software, and this kind of pace of change / of improvement that we feel in other parts of AI. Bringing that type of pace to health is the mission of my career, and I’m excited to work on it.” In this episode, we sit down with Adam Yala, Assistant Professor at UC Berkeley and UCSF and co-founder of Voio, to explore how AI is reshaping the future of healthcare. Adam walks through his path from research to building real-world systems, and why computational health is emerging as its own distinct field rather than just an application of AI. We dive into what it actually takes to build in this space, from understanding clinical complexity to navigating challenges like data access and compute. Adam also shares how his experience across academia and startups has shifted his perspective on speed, innovation, and creating meaningful impact. Finally, he offers advice for students and aspiring data scientists, emphasizing the importance of adaptability, curiosity, and focusing on the problems you want to solve in a world where technology is constantly evolving. 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 [https://datascienceeducation.substack.com?utm_medium=podcast&utm_campaign=CTA_1]

Rethinking Calculus: Building Math for Data Science at CSU East Bay (feat. Mikahl Banwarth-Kuhn)

Access the full transcript for this episode [https://docs.google.com/document/d/e/2PACX-1vRr17qTdF-8xFUWRJg7kOWT5EBBuyr1QFvgxP7Ctqm3Vt__RedRRYTBmfprgH5zFGh5-DEXdzff2D8t/pub] “I think we had this feeling that there’s so many students that don’t make it to calculus, and that in the field of data science and the STEM field itself, we really have a gap to fill because we’re missing all of that knowledge and expertise that those students that don’t ever get through calculus would really bring to the field.” In this episode, we speak with Mikahl Banwarth-Kuhn (MBK), Assistant Professor of Mathematics at Cal State East Bay, about reimagining the traditional calculus pathways for today’s data science students. MBK helped develop a new course sequence, Math for Data Science, designed to remove barriers that often prevent students from reaching calculus. She discusses the motivation behind the course and whether traditional pen-and-paper calculus sequences still serve data science students today. MBK advocates for a more intuitive, application-driven approach to help students more deeply understand concepts like derivatives, optimization, and differential equations. 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 [https://datascienceeducation.substack.com?utm_medium=podcast&utm_campaign=CTA_1]