Data 8: Foundations of Data Science combines three perspectives: inferential thinking, computational thinking, and real-world relevance. Given data arising from some real-world phenomenon, how does one analyze that data so as to understand that phenomenon? The course teaches critical concepts and skills in computer programming and statistical inference, in conjunction with hands-on analysis of real-world datasets, including economic data, document collections, geographical data, and social networks. It delves into social issues surrounding data analysis such as privacy and design.

Partner Institutions

List of institutions that have adopted Data 8 and or Modules

Course Website

The course website can be found at You can find all previous iterations of the course here.


Computational and Inferential Thinking: The Foundations of Data Science is the textbook for Data 8 at UC Berkeley. The book is a free online textbook that includes interactive Jupyter notebooks and public data sets for all examples. The textbook source is maintained under the CC BY-NC-ND 4.0 License. 

Data 8X

Data 8X(link is external) is a Massive Open Online Course (MOOC) of Data 8 offered on edX that increases access for Data 8 to students around the world. The course contains recorded pedagogy videos by Professor John Denero, Ani Adhikari, and David Wagner.

Public Course Materials

Many of UC Berkeley’s resources for Data 8 are open source.  The Spring 2020 public repository contains the Juptyer notebooks for the Homeworks, Labs, and Lectures.  These materials are what the students work on through the course of Data 8. 

Private Resource Sharing

Fill out this interest form if you are interested in more sensitive content that cannot be shared publicly, including solutions, private tests, worksheets, and lecture slides. Since many schools utilize the same resources in their data science courses, you will have to additionally sign a privacy form agreeing to not distribute these resources.

Zero 2 Data 8

This Jupyter Book goes over the pedagogical methods utilized in Data 8 and discusses how to begin teaching an introductory data science course at your university.

Technology Adoption Guide

This Jupyter Book is a technology guide for others who wish to adopt a data science classroom environment and is based on the Data Science Education Program’s (DSEP) experiences from running Data 8 and other data science courses. It mainly covers JupyterHub and auto grading setup.

TA + Tutor Guides

Berkeley-centric guides for Data 8 teaching assistants and tutors: