Requirements: Lower Division

Based on changes to L&S policy, courses completed at UC Berkeley with a grade of Pass in Spring 2020, Fall 2020, Spring 2021 and Summer 2021 will count toward Data Science major requirements, including prerequisites to declare the major. Please see the L&S P/NP policy modifications for more information.


  • All courses toward the major must be completed for a letter grade and passed with a 'C-' or higher.

See Declaring the Major for more information about declaring the Data Science major.

Lower Division Requirements

Requirement Preferred Track Alternate Course Options
Foundations of Data Science Data C8 (also listed as CompSci/Stat/Info C8)
Calculus I* Math 1A or N1A Math 10A or 16A
Calculus II* Math 1B or N1B
Linear Algebra Math 54/N54/W54 or Stat 89A (4 unit offering) EE/EECS 16A and 16B (both required), or Physics 89
Program Structures CompSci 61A or CompSci 88 Engin 7
Data Structures CompSci 61B or CompSci 61BL
Domain Emphasis 1 lower-division course from approved list
*The Data Science BA accepts Advanced Placement, International Baccalaureate and A-Level exam credit for the calculus requirements only, per the UC Berkeley Math department guidelines. Exam credit is not accepted for the Domain Emphasis or other major requirements.

Program Updates

Updated 11/9/21.

Data 8 Substitutions:

  • All Students in Summer 2021, Fall 2021 or Spring 2022: Students may petition to substitute Statistics 20, if taken in Summer 2021, Fall 2021 or Spring 2022, for Data 8 toward the Data Science major or minor; this option is not available for students who take Engin 7 for their Program Structures requirement.
  • All Students Through Spring 2021: Students may substitute Statistics 20, if completed by the end of Spring 2021, for Data 8 toward the Data Science major or minor; this option is not available for students who take Engin 7 for their Program Structures requirement. No petition is required.
  • Continuing Students Through Spring 2019: When the Data Science BA was first approved, the faculty approved a substitution option for continuing students to substitute for Data 8 by completing CompSci 61A and one from Stat 20, 21, W21, 131A or 135. This substitution option for Data 8 applies only to courses completed by Spring 2019. First year students admitted to UC Berkeley in or after Fall 2018 are not eligible to use this option.

Linear Algebra:

  • We accept linear algebra without differential equations from California community colleges in all cases where the course is included in a Math 54 articulation agreement. Linear algebra courses from non-California community colleges may be considered by individual petition.

Courses taken P/NP:

  • Prerequisite courses taken on a P/NP basis before Fall 2018 will be accepted. Grades of ‘P’ earned will be evaluated as ‘C-’ for GPA calculation purposes. Courses taken on a P/NP basis after Summer 2018 will not be accepted.

Need advising?

Data Science advisors are available to help! Email us at, or find out about our other advising services.


Data 8, Foundations of Data Science (CS/INFO/STAT C8), introduces students to the field of Data Science through computational and inferential thinking. Intended for students of all backgrounds, it has no prerequisites.

Beyond Data 8, a firm mathematical foundation allows students to understand with precision the ideas and methods of data science. Calculus forms a basis for studying distributions and optimization. Linear algebra leads to understanding properties of arrays of data, including dependence and dimensionality.  Program structures provides students with a rigorous working knowledge of computer science concepts important for data analytics, including algorithms,  interpretation, abstraction, and a proficiency of programming based upon them sufficient to construct substantial stand-alone programs. Data structures establish algorithmic foundations that dictate whether a computational process is efficient or intractable, and understanding properties of arrays, lists, trees, graphs, heaps, hashes and associated computational and storage complexity is as essential to data science as its mathematical foundations.