Fall 2021 Courses

Questions about enrolling in a Data course?

  1. Start by reviewing our Fall 2021 Enrollment FAQs
  2. Check for updates on the Data 001 Piazza page.
  3. Read the Class Notes for each class on the Schedule of Classes.

If you have checked the resources above and cannot find the answer to your question:

  • For questions about enrolling in Data courses, please contact us at ds-enrollments@berkeley.edu.
  • For questions about enrolling in other courses, please contact the department that manages the course (for example: for IND ENG 135, please contact IEOR; for COMPSCI 61B, please contact EECS).

Enrollment Permission Requests

Please note that enrollment permission will only be granted for the specific cases listed below:

Data C100

  • If you have satisfied one or more of the pre/co-requisites with an approved substitute course or outside of UC Berkeley, please submit an enrollment request for Data C100.

Data C102

  • If you have satisfied one or more of the prerequisites for Data C102 outside of UC Berkeley OR you are a graduating senior in Fall 2021 who will be declared in Data Science and you have not yet satisfied the Modeling, Learning & Decision Making requirement, please submit an enrollment request for Data C102.

Data C140

  • If you completed Stat 21/W21 instead of Stat 20 plus CompSci 61A, or an approved linear algebra course outside of UC Berkeley, please submit an enrollment request for Data C140.


Please check the Schedule of Classes for the most up-to-date information on times, locations and remote availability.

Title Course Number Description Instructor
Computational Structures in Data Science

CompSci 88

Development of Computer Science topics appearing in Foundations of Data Science (C8); expands computational concepts and techniques of abstraction. Understanding the structures that underlie the programs, algorithms, and languages used in data science and elsewhere. Mastery of a particular programming language while studying general techniques for managing program complexity, e.g., functional, object-oriented, and declarative programming. Provides practical experience with composing larger systems through several significant programming projects. Friedland, G. & Ball, M.
Economic Models

Data 88E

This Data Science connector course will motivate and illustrate key concepts in Economics with examples in Python Jupyter notebooks. The course will give data science students a pathway to apply python programming and data science concepts within the discipline of economics.  The course will also give economics students a pathway to apply programming to reinforce fundamental concepts and to advance the level of study in upper division coursework and possible thesis work.

Van Dusen, E.
Python & Earth Science

EPS 88

Dreger, D.
Data Science Applications in Physics

Physics 88

Introduction to data science with applications to physics. Topics include: statistics and probability in physics, modeling of the physical systems and data, numerical integration and differentiation, function approximation. Connector course for Data Science 8, room-shared with Physics 77. Recommended for freshmen intended to major in physics or engineering with emphasis on data science.
Scientific Study of Politics PolSci 88
  Little, A.
Probability and Mathematical Statistics in Data Science Stat 88
In this connector course we will state precisely and prove results discovered while exploring data in Data 8. Topics include: probability, conditioning, and independence; random variables; distributions and joint distributions; expectation, variance, tail bounds; Central Limit Theorem; symmetries in random permutations; prior and posterior distributions; probabilistic models; bias-variance tradeoff; testing hypotheses; correlation and the regression model.
Data and Decisions UGBA 88 The goal of this connector course is to provide an understanding of how data and statistical analysis can improve managerial decision-making. We will explore statistical methods for gleaning insights from economic and social data, with an emphasis on approaches to identifying causal relationships. We will discuss how to design and analyze randomized experiments and introduce econometric methods for estimating causal effects in non-experimental data. The course draws on a variety of business and social science applications, including advertising, management, online marketplaces, labor markets, and education. This course, in combination with the Data 8 Foundations course, satisfies the statistics prerequisite for admission to Haas.