About our educational partnerships

UC Berkeley's Data Science Undergraduate Studies is dedicated to making high-quality data science education accessible beyond Berkeley. Through partnerships with other institutions, we strive to broaden access and impact.

Our approach to data science education:

  • Empowers student discovery. Through our approachable foundations course and by developing curricula add-ons for other disciplines, we seed opportunities for students to discover this burgeoning field.
  • Bridges disciplines. We view data science as an interdisciplinary field with applications spanning from the sciences to the humanities and we promote connections across these diverse areas. 
  • Integrates human contexts and ethics. Our curricula incorporates human contexts and ethical considerations, equipping students to make responsible and impactful decisions in their careers. 
Students attend a data science event at UC Berkeley

Bring Data Science Education to Your School

Resources for adopting classes and building curricula

California Education Learning Lab grantees meet at the National Workshop on Data Science Education.

Our Current Educational Partnerships

Learn more about our current partnership projects

An El Camino Community College expert speaks at the National Workshop on Data Science Education in June 2024.

Community

Events and opportunities to learn from other educators

Media

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Podcast thumbnail: Data Science Education Podcast

Open Science, Jupyter, and Data Science Education (feat. Fernando Pérez)

Episode in Spanish.

“Lo nuevo que va a entrar al curso esta vez es la pregunta de qué hacemos con las herramientas de inteligencia artificial en este contexto. ¿Cómo? ¿Cómo usar? Yo no voy a pretender que eso no existe. Yo creo que es absurdo hoy en día imaginarnos que los estudiantes no lo van a usar. Prohibirles usar esas herramientas yo creo que es, es, es fútil. Entonces la pregunta mía es bueno, cómo le creo a los estudiantes un ambiente en el cual sepa que su privacidad está siendo respetada, que tienen acceso a herramientas que pueden usar potencialmente en su propio computador.”

Fernando Pérez, Faculty Director of the Berkeley Institute for Data Science at UC Berkeley (BIDS), a Professor of Statistics, and co-founder of Project Jupyter and IPython

Listen on Spotify or Apple Podcasts

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Podcast thumbnail: Data Science Education Podcast

Teaching Data Science in a Changing World: Judith Canner on Reform, Collaboration, and Social Good

“I think a lot of times, we focus on data science as a tech thing, right? Oh, you're going to go work for Meta. You're going to go work for Google. You're going to go work for insert tech company here or AI startup here. And for a lot of students, especially a lot of my students, they really want to contribute to their communities and give back, right? They're thinking about how to make their community stronger. [...] You can use it in ways that actually serve the community, serve the world, from helping develop algorithms that can read MRIs or other medical imaging data, to help diagnose some sort of disease or cancer, or to identify human rights violations by being able to search massive amounts of documentation.”

Judith Canner, professor of statistics at California State University, Monterey Bay

Listen on Spotify or Apple Podcasts

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Podcast thumbnail: Data Science Education Podcast

Beyond Calculations: Ani Adhikari on the Art and Philosophy of Data Science Education

“It was in the 1970s that David Friedman and his colleagues completely changed the way statistics is taught in the world, from going from just an emphasis on calculation, calculation, calculation, without really paying any attention to, what's the question, and what can you do with the answer?… Why does anyone care? What is the calculation that you can justifiably do, given the information at hand? And then how do you interpret the answer? That is traditional statistics teaching, and I haven't strayed one step away from it. I'm still there. It's called data science now. The tools are different. And because the tools are different, we are empowered to ask questions that we wouldn't have dared to ask before. And we can answer it in ways that we couldn't before. But I still think I am teaching traditional statistics.”

—Ani Adhikari, Faculty Director of Pedagogy at Data Science Undergraduate Studies, UC Berkeley

Listen on Spotify or Apple Podcasts