April Feature: CA Learning Lab Cohort Collaboration Grant
The California Education Learning Lab (Learning Lab) awarded over $8 million across 13 grants in response to Learning Lab’s funding opportunity Grand Challenge: Building Critical Mass for Data Science. This grant opportunity incentivizes public higher education institutions to embrace data science as an opportunity to build new pathways, modernize majors, attract historically underrepresented students into STEM, and deepen both civic and interdisciplinary learning.
UC Berkeley Data Science Undergraduate Studies serves the critical role in promoting collaboration across awarded projects.
Data Science Undergraduate Studies and Puente partner to inspire underrepresented students in STEM
This summer, we partnered with the Puente MaS program to introduce CA community college students underrepresented in STEM to the field of data science. Through student narratives, a career professional's advice, and information from our Data Science Undergraduate Studies team, the students discovered pathways into a major many had never heard of.
UC Berkeley convenes, connects California data science education pioneers
Dozens of California public educators walked into a sunlight streaked, first floor-room at the edge of UC Berkeley in late June. Hugging each other and snagging coffee or water, they navigated blue and gold star-shaped balloons to settle at tables facing a projector screen.
These scholars are part of a state effort to make California the nation’s leader in excellent, equitable and accessible undergraduate data science education. For the next two days, the assembled attendees would share how they’re building programming, community or pathways to do just that...
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.
Bring Data Science Education to Your School
Resources for adopting classes and building curricula
Our Current Educational Partnerships
Learn more about our current partnership projects
Community
Events and opportunities to learn from other educators
Media
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
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
The AI Revolution in Data Science (feat. Paul Groth)
“I think one of the things we've approached in our data science curriculum is this idea that data science is a team sport…You're never really doing data science on your own. You're always in a team and you're working with product managers. You're working with end users. You're working with software engineers. You're working with salespeople. And that idea of how do I translate people's problems? What is my system going to do? What are the variables and what considerations I have when I'm designing a system with people? What are the algorithms going to do and what does that mean? So that kind of idea of treating it as a team sport and figuring that out as a student, is like a fundamental principle for how we do data science in these environments.”
—Paul Groth, Professor of Algorithmic Data Science at the University of Amsterdam
Listen on Spotify or Apple Podcasts