The major program is designed to provide integrative course experiences in the lower division and upper division, as well as the technical depth in computation and inference required for students to engage in data science upon graduation.
Data 100, Principles and Techniques of Data Science (CS/STAT C100), explores the data science lifecycle and focuses on quantitative critical thinking and key principles and techniques needed to carry out this cycle. It bridges between Foundations of Data Science (Data 8) and upper division courses.
All policies subject to finalization.
- To satisfy the requirements of the major, all courses must be taken for a letter grade and passed with a 'C-' or higher.
- Students must maintain a 'C' average in courses taken for the major, and in the upper-division courses taken for the major.
- A minimum 2.0 overall Grade Point Average is required to remain in good standing.
See details about each of the upper-division requirements and the lists of courses that satisfy each in the drop-down menus below.
The Data Science major requires a minimum of 8 upper-division courses, totaling a minimum of 28 upper-division units, effective Fall 2019. Along with Computational and Inferential Depth, Probability, Modeling, Machine Learning, Decision Making, and Human Contexts and Ethics (all detailed below), students will also select and complete a Domain Emphasis. Learn more about the Domain Emphasis requirement here.
A single course may not be used to fulfill more than one requirement within the requirements of the major.