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 and Spring 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.

Policies

  • 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 Math 10A or 16A
Calculus II* Math 1B
Linear Algebra Math 54/N54 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/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 1/22 at 12:55pm.

Data 8 Grandfathering:

  • When the Data Science BA was first approved, the faculty approved a grandfathering option for continuing students to substitute for Data 8 by completing CompSci 61A and one from Stat 20, 21, W21, 131A or 135. Grandfathering 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 substitute for Data 8.

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.

Overview

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.