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.
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)
The textbook, lectures, and labs for UC Berkeley’s Foundations of Data Science (Data 8) are available from previous semesters at http://data8.org/. All materials for the course, including the textbook and assignments, are available for free online under a Creative Commons license.
The Data 8 textbook, “Computational and Inferential Thinking: The Foundations of Data Science,” is available directly at http://inferentialthinking.com.
The Data 8 slides are publicly available each semester, linked from the syllabus at http://data8.org/.
To access the video lectures without a UC Berkeley email, please look at the Fall 2016 class materials (http://data8.org/fa16).
To access the in-class demos and labs, visit http://try.datahub.berkeley.edu/. You can log in with your own institutional email and any password. This is purely a demo instance - there is no persistent storage, and your session will end after an hour 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.
To learn more about teaching a connector course, read the connector instructors’ guide: https://github.com/data-8/connector-instructors/wiki/Contents. For tips on developing your own version of the connector course program, please contact us through the contact form.
Berkeley’s Data Science Education Program and its online computing environment were designed to make data science available to a broad array of students — many without previous programming or statistics experience. The Jupyter platform enables browser-based computation, avoiding the need for students to install software, transfer files, or update libraries.
Jupyter is a cloud-based platform that allows students to weave calculations, code, data visualizations, and explanations into one document that they create interactively. In many of our courses, assignments are distributed as Jupyter notebooks that contain outlines of data analyses. These outlines guide students through the process of discovering, validating, and communicating insights from data. The notebooks let students learn how to “think together” with the computer, a broadly important and powerful skill.
To learn more about running a course using Jupyter notebooks, read the connector instructors’ guide: https://github.com/data-8/connector-instructors/wiki/Contents.
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!