Requirements: Lower Division

Policies

Declaration Eligibility
  • All prerequisites to declare must be completed for a letter grade and passed with a 'C-' or higher.
  • Students must achieve a 2.0 Grade Point Average in Data 8, Linear Algebra, and Data Structures to be eligible to declare. The grades earned in Calculus I & II and Program Structures will not be factored into this GPA requirement.
  • If a student chooses to take more than one upper-division course toward the major prior to declaring, they must have a minimum 2.0 Grade Point Average in all upper-division courses completed toward the major at the time of declaration.

Once you have completed the prerequisites, see Declaring the Major for instructions on how to declare.

Major Prerequisites

The table below reflects the lower-division courses that all students must take to declare the Data Science major.

Requirement Preferred Track Alternate Course Options
Foundations of Data Science Data 8 (COMPSCI/STAT/INFO C8)
Calculus I* Math 1A Math 10A or 16A
Calculus II* Math 1B
Linear Algebra Math 54 or Stat 89A (4 unit offering) EE/EECS 16A and 16B (both required), or Physics 89
Program Structures CS 61A or CS 88 ENGIN 7
Data Structures CS 61B
*The Data Science BA accepts Advanced Placement, International Baccalaureate and A-Level exam credit for the calculus requirements, per the UC Berkeley Math department guidelines.

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.