MATH 2310

Linear Algebra for Data Science

Course Description

Course information provided by the Courses of Study 2024-2025.

An introduction to linear algebra for students interested in applications to data science. The course diverges from traditional linear algebra courses by emphasizing data science applications while teaching similar concepts. Key topics include matrices as data tables, high-dimensional datasets, singular value decomposition for data compression, and linear transformations in computer graphics.

When Offered Fall, Spring.

Prerequisites/Corequisites Prerequisite: MATH 1106, MATH 1110, or equivalent AP credit.
Forbidden Overlaps Forbidden Overlap: due to an overlap in content, students will receive credit for only one course in the following group: MATH 2210, MATH 2230, MATH 2310, MATH 2940.

Distribution Category (SMR-AS) (MQL-AG, OPHLS-AG)

Comments Students who have taken MATH 2310 may need more foundational coursework before pursuing further study in mathematics and should contact the director of undergraduate studies for advice before continuing. For guidance in selecting an appropriate course, please consult First Steps in Math.

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