This course was recently approved in its extended four unit form, and so it was not listed in the course catalogue last fall. Sign-up is now available.
"Stat 89A (in its new, 4-unit Spring 2018 form) can be used as an alternate linear algebra co-requisite for Data 100 this semester, along with Math 54 and EE 16A. When the Data Science major and minor are approved, the faculty aim to add Stat 89A (in its new, 4-unit Spring 2018 form) to the list of courses to satisfy the linear algebra requirement."
Stat89a: Linear Algebra for Data Science
Instructor: Michael Mahoney
Time and Location: Tue-Thu 9:30-11am, 103 Moffitt. Additional lab hours, TBA.
Prerequisite or corequisite: Foundations of Data Science (COMPSCI C8 / INFO C8 / STAT C8). One year of calculus.
Course Description: An introduction to linear algebra for data science. The course will cover introductory topics in linear algebra, starting with the basics; discrete probability and how probability can be used to understand high-dimensional vector spaces; matrices and graphs as popular mathematical structures with which to model data (e.g., as models for term-document corpora, high-dimensional regression problems, ranking/classification of web data, adjacency properties of social network data, etc.); and geometric approaches to eigendecompositions, least-squares, principal components analysis, etc.
Data Science Connector: During spring 16 and spring 17, this course was a two-unit connector. Going forward, this course will be expanded, covering similar topics in a more methodological manner. In particular, it will serve as a comprehensive introduction to linear algebra, but presented in a way more appropriate for students of data science. Officially, the course remains a Data Science connector.
Course Requirements: The course evaluation will consist primarily of homeworks (ca. 50%)---split between pencil-and-paper (ca 30%) and computational (ca 20%) homeworks---a midterm (ca. 25%), and a final (ca. 25%).