STAT89A
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STAT 89A - Linear Algebra for Data Science
Course Title
Linear Algebra for Data Science
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 prob- ability 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.
Minimum Units
4
Maximum Units
4
Grading Basis
Default Letter Grade; P/NP Option
Method of Assessment
Written Exam
Prerequisites
One year of calculus. Prerequisite or corequisite: Foundations of Data Science (COMPSCI C8 / INFO C8 / STAT C8).
Repeat Rules
Course is not repeatable for credit.
Credit Restriction Courses. Students will receive no credit for this course if following the course(s) have already been completed.
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Formats
Laboratory, Lecture
Term
Fall and Spring
Weeks
15 weeks
Weeks
15
Lecture Hours
3
Lecture Hours Min
3
Lecture Hours Max
3
Laboratory Hours
2
Laboratory Hours Min
2
Laboratory Hours Max
2
Outside Work Hours
8
Outside Work Hours Min
8
Outside Work Hours Max
8