Welcome New Bears! If you were recently admitted as a transfer student for Fall 2022 and would like more information about Data Science at UC Berkeley, please connect with us by filling out this contact form. We'll get back to you with opportunities to engage with the Berkeley Data Science community.
Welcome to Data Science at Berkeley! We’re excited to tell you about our program and help you navigate the major. If you aren’t able to find the answer to your question here, please don’t hesitate to email us at ds-advising@berkeley.edu.
Please note, admissions to the College of Letters & Science is handled by the UC Berkeley Admissions office. For questions related to eligibility or likelihood of admission, contact UC Berkeley Admissions.
Berkeley's Data Science Major
Who majors in Data Science?
Our students come from all sorts of backgrounds and interests, with very diverse career pursuits. Some of our students are in research, others spend their summers in internships. The Data Science major is very flexible and allows students to create a curriculum that best fits their educational goals. We encourage you to explore the upper division curriculum, including our unique Domain Emphasis requirement, to get a sense if this is the right major for you.
Step One: Preparation for the Data Science Major
As one of the criteria considered in the admission review process, transfer students are encouraged to complete as many as possible of the lower-division courses toward their intended major. However, applicants are not expected to complete all of the prerequisites prior to transfer, since some courses are very rarely offered outside of UC Berkeley.
See below for details about how transfer credit can be applied to the lower-division requirements in the Data Science major. You can also find more information about what equivalent courses are offered at your community college on the UC Berkeley Admissions Transfer Guides website.
Per UC Berkeley policy, transfer courses completed at other institutions in Spring 2020 with a grade of Pass are acceptable to satisfy requirements toward the Data Science major or minor.
Step Two: Admission as a Data Science-intended Major in the College of Letters & Science
All applicants to UC Berkeley should ensure that they are satisfying the criteria for admission as a transfer student. For any questions about eligibility or likelihood of admission, please contact the UC Berkeley Admissions office.
Data Science major advisors are available to help ensure that you are as prepared as possible to transfer into the Data Science major at UC Berkeley. However, we do not review applications or have input in the admission decision. If you have a question about the major, email ds-advising@berkeley.edu.
Step Three: Entering Berkeley - Your First Semester
Congratulations on your acceptance to Berkeley! It’s important to note that you are not officially declared in the Data Science major. You should assume that you will spend your first semester finishing up any lower division prerequisites that you were unable to take at your previous college. Once you complete all lower division requirements, you may apply for the major. Learn how to declare the Data Science major here.
You’ll be going through several orientation programs both online and in-person to help you acclimate to the university. The most important piece of advice we have for you is: Do not overload your first semester schedule.
Sample Course Plan
The following sample plan is based on a transfer student who has already completed 1 year of calculus, linear algebra and data structures, as well as L&S 7-Course Breadth or IGETC.
All students should work with a Data Science Advisor to develop an individualized plan based on your specific situation.
FALL SEMESTER |
SPRING SEMESTER |
Data C8 CS 88 or 61A Lower-division Domain Emphasis |
Data C100 Probability (ex. Stat 140) American Cultures/Upper-division Outside Major |
Computational & Inferential Depth #1 Upper-division Domain Emphasis #1 Upper-division Domain Emphasis #2 |
Computational & Inferential Depth #2 Modeling, Learning & Decision Making (ex. Data C102) Human Contexts & Ethics (ex. Data C104) |