The Data Science Education Podcast

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“I like to work on intellectually engaging problems, but I also like to maximize for impact.” — Kevin Miao
In this episode, we speak with Kevin Miao about his path from studying and teaching data science at Berkeley to working on AI and extended reality at Apple, and eventually starting his own company. He shares how his experiences as both a student and instructor shaped the way he thinks about education, and discusses the creation of Berkeley’s post-training course, which was designed to help students connect foundational concepts with rapidly evolving areas like model evaluation, agentic systems, and AI engineering. He also reflects on the challenge of keeping curriculum relevant when technology is moving so quickly, arguing that students should focus less on chasing every new tool and more on learning how new technologies fit into frameworks they already understand.
We also discuss the future of AI, XR, and the skills students should develop as technology continues to reshape knowledge work. Kevin shares his perspective on how AI and extended reality could eventually bring digital intelligence more deeply into the physical world, while emphasizing that students should combine technical skills with interests and expertise in areas they genuinely care about. He offers advice on lifelong learning, staying adaptable, and using AI not simply to automate work, but to free people to spend more time on the problems and creative work that matter most to them.


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“Just because we have Amazon with Kindle doesn’t mean that we don’t also need community libraries or university libraries.”— Julian Pistorius
“Research thrives on openness: open-source software, open data, open weights.” — Javier Guaje
In the first episode of season 12, we speak with Javier Guaje and Julian Pistorius about their different paths into research computing and their work making powerful computing tools more accessible through Jetstream2. Javier shares how his PhD experience led him into research software engineering, where he now helps researchers across different fields. He discusses how open-source software, shared computing resources, and listening to users can support research, along with how AI is changing the tools researchers need.
We also hear from Julian, who moved from industry into research computing and found a community built around helping people solve problems together. He shares how hands-on workshops help students and educators build confidence, emphasizing small teams, real-world projects, and learning from one another. Together, Javier and Julian offer practical advice for students interested in the field, from experimenting with new tools to connecting with researchers and developing the communication skills that make collaboration possible.


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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“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

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“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

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“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