UC Berkeley welcomes inquiries about how to design and implement a broad-based data science program. Below please find resources for further exploration into our undergraduate data science curriculum.
Workshop on Undergraduate Data Science Pedagogy & Practice July 16 - 19th, 2018
Our website at data.berkeley.edu provides a wealth information about our program, including a curriculum overview, list of courses offered, news, and more. Our original design document may be helpful if you are developing your own program. Feel free to contact us to learn more.
Foundations of Data Science (Data 8)
- Zero to Data Guide - Comprehensive guide to how to deploy a Data 8 like course into your institution.
Data 8X on edX - Free online version of Data 8
- Data8.org - The textbook, lectures, and labs for UC Berkeley’s Foundations of Data Science (Data 8) are available from previous semesters. All materials for the course, including the textbook and assignments, are available for free online under a Creative Commons license. To access the video lectures without a UC Berkeley email, please look at the Fall 2016 class materials
- Computational and Inferential Thinking: The Foundations of Data Science - The Data 8 textbook.
- Run the class's homeworks, labs, and projects using mybinder. This is purely a temporary Jupyter instance - there is no persistent storage, and your session will end after 10 minutes of inactivity.
For more information about the Connector Courses in many fields that “connect” to Data 8, please visit our list of all connectors that have been offered. Connectors are taught by instructors from many different domains with varying degrees of data science experience. Individual instructors may be willing to answer questions about their courses if you contact them individually.
- Berkeley Data Science Connectors on Github - View course content from various Connector Courses on Github.
- Connector Instructors’ Guide - To learn more about teaching a connector course.
JupyterHub is a tool that allows Berkeley’s data science program to quickly utilize cloud computing infrastructure to manage a hub that enables users to interact remotely with a specific computing environment. JupyterHub offers a useful way to standardize the computing environment of a group of people (e.g., for a class of students), as well as allowing people to access the hub remotely.
To learn about creating a JupyterHub, please review this documentation:
Thank you for your interest in the Berkeley Data Science curriculum!