STAT89A
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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
4
Maximum
4
Grading Basis
Default Letter Grade; P/NP Option
Method of Assessment
Written Exam
American Cultures Requirement
No
Reading and Composition Requirement
None of the Reading and Composition Requirement
Prerequisite
Complete ANY of the following Courses:
Prerequisite
One year of calculus.
Repeat Rules
Course is not repeatable for credit.
Credit Restriction Courses. Students will receive no credit for this course if the following the course(s) have already been completed.
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Formats
Laboratory, Lecture
Term
Fall and Spring
Duration (in weeks)
15
Minimum Hours
3
Maximum Hours
3
Minimum Hours
2
Maximum Hours
2
Outside Work Hours Min
8
Maximum Hours
8