Getting into Data Classes
- Check our Spring 2024 Enrollment FAQs.
- Check for updates on the Data 001 Ed Discussion page.
- 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 firstname.lastname@example.org.
- 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).
Requests to Clear Prerequisites
Data Science enforces class prerequisites and corequisites for most classes, as approved in the Berkeley Academic Guide, with limited substitutions allowed. We do not consider exceptions (i.e. requests to enroll without completing the requisites or any approved substitute).
Students are expected to complete all prerequisites with passing grades of C- or better before the class begins, including prerequisites that were in-progress or Incomplete at the time of enrollment. We reserve the right to drop students who don't meet these requirements.
TIMING: Please be sure to submit your request before your Phase 1 enrollment time, or as soon as you want to enroll. Enrollment requests are reviewed as soon as possible, usually within 1-2 business days.
Please note that enrollment permission will only be granted for the specific cases listed below:
|Course||Enforced Requisites||Exceptions Allowed|
Co-requisite: Data C8
For Data 8: Stat 20* + CS 61A or Data C88C allowed
Co-requisite: Data C8
For Data 8: Stat 20* allowed
Prerequisites: Data C8; and CS 61A or Data C88C or Engin 7
Co-requisite: Math 54 or 56 or EECS 16A
For Data 8: Stat 20*, 21, W21, 135, PH 142 or IndEng 165 allowed
For Linear Algebra: Stat 89A, Physics 89, Math 110 or Math 91 in Fall 2022 allowed
Prerequisites: Math 54 or 56 or 110 or Stat 89A or Physics 89 or both of EECS 16AB; Data C100; and any of EECS 126, Data C140, Stat 134, IndEng 172, Math 106
Approved transfer courses only
Prerequisites: Data C8, or C100, or both Stat 20* and Computer Science 61A; one year of calculus at the level of Math 1A-1B or higher
Co-requisite: Math 54 or 56 or 110, EECS 16B, Stat 89A
For Linear Algebra: Math 91 in Fall 2022, or Physics 89
Other approved transfer courses
*Solely for the purpose of satisfying the course prerequisite, Stat 21, W21, 135, PH 142 or IndEng 165 may be allowed in substitution for Data C8, when combined with CS 61A or 88. Please note that this substitution does not apply to the Data C8 requirement for the Data Science major or minor.
Yes, you can enroll in a class for next semester if you have the prerequisite in progress at UC Berkeley and will complete it before next semester begins.
*If you are currently taking a prerequisite class outside of UC Berkeley, the registration system will not automatically recognize this and you will need to submit a request to clear the prerequisite, including your proof of enrollment.
Faculty reserve the right to validate requisites at the start of the semester and drop students who did not complete or did not pass requisites that they were previously enrolled in.
An enforced co-requisite may be taken either before or concurrently with the class it is requisite to. If you want to take a course and its co-requisite in the same semester, you must FIRST enroll (not be on the waitlist) in the co-requisite course. For example, if you are taking Math 54 concurrently with Data C100, you must enroll in Math 54 first before you will be able to add Data C100.
If you completed a requisite course outside of UC Berkeley and have equivalent credit listed on your Transfer Credit Report in CalCentral (for example, if your Transfer Credit Report lists transfer credit equivalent to Math 54), the registration system will automatically recognize that this course is completed when you enroll. You do not need to submit any form.
If you completed a requisite course outside of UC Berkeley but haven't yet submitted your transcript, or your transcript hasn't yet been processed on your Transfer Credit Report, submit the form above to clear the prerequisite, including an unofficial copy of your transcript.
If you completed a requisite course outside of UC Berkeley that isn't listed as equivalent on your Transfer Credit Report but you have already received approval for it as equivalent to a UC Berkeley course, submit the form above to clear the prerequisite, including your documentation of equivalency approval.
This section has not yet been updated for 2023-24.
Please check the Schedule of Classes for the most up-to-date information on times, locations and availability.
|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.||E. Van Dusen|
|Computational Structures in Data Science||Data/CompSci C88C||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.||M. Ball|
|Probability and Mathematical Statistics in Data Science||Data/Stat C88S||
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.
|Python and Earth Science||EPS 88||Earthquakes and El Ninos are examples of natural hazards in California. The course uses Python/Jupyter Notebook and real-world observations to introduce students to these and other Earth phenomena and their underlying physics. The students will learn how to access and visualize the data, extract signals, and make probability forecasts. The final module is a project that synthesizes the course material to make a probabilistic forecast. The course will be co-taught by a team of EPS faculty, and the focus of each semester will depend on the expertise of the faculty in charge.|
|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||We will focus on the theoretical side of political science. The main goals here are to understand what makes a good political science theory, and to give a brief overview of how game theory and related tools make up a powerful way to construct theories. This side of the class will be less data-focused, we will also see how the programming tools you learn in Data 8 can be used in this part of the scientific process. We will pivot to the empirical side in the second part of the class, we will cover how political scientists and other social scientists think about the challenges of causal inference, and the tools we use to overcome them.|
|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.|