May Feature: Adopting Infrastructure--JupyterHub and Otter Grader
Setting up and teaching a data science course often may require significant infrastructural setup, especially at scale. Two types of infrastructure are generally required: JupyterHub (an open-source service part of Project Jupyter that creates on-demand cloud-based Jupyter notebook servers) and autograding (allows instructors to easily and efficiently grade large amounts of student submissions, and also lets students have a way to check their progress and ensure they’re heading the right direction).
Learn more about these resources and how to adopt them at your institution.
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
What I Wish I Knew: Transfer Reflections on Entering Berkeley Data Science
In our final episode of Season 9, we hear from three graduating UC Berkeley seniors, all of whom transferred from California community colleges into the Data Science major: Avani Gireesha, Hannah Brown, and Jake Pastoria. They reflect on their transitions from community college to Berkeley, discussing the clubs, research, and experiences they’ve gained in their two years here. Listen in as they offer advice for incoming transfer students on how to prepare academically, find community, and get the most out of their Berkeley experience!
Listen on Spotify or Apple Podcasts
Interdisciplinary Roots and Inclusive Pathways in Data Science
In this week’s episode, we talk with Professors Mike Ludkovski and Alex Franks from UC Santa Barbara about their diverse research backgrounds—ranging from stochastic modeling to sports analytics—and how they shaped their approach to data science education. They also touch on some major initiatives aimed at expanding access to data science education, including the Southern California Consortium and the Pacific Alliance for Low-Income Inclusion.
Listen on Spotify or Apple Podcasts
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