MATH 403 - Linear Algebra and Modeling for Data Science

3 Credits

Description
Foundations of using matrices, differential equations and other models in modeling. The course takes the theoretical models and focuses on computational solutions and modeling in a modern programming language and modeling environment. Topics include matrices and determinants, systems of linear equations, vector spaces, linear transformations, eigenvalues and eigenvectors, singular value decompositions, orthogonal matrices, mathematical modeling using differential equations, optimization and applications. Focus is on how the models and tools are applied in data science and analytics.

Prerequisites
MATH 252, MATH 313, MATH 320, MATH 341, and CS 201

Repeatable
No

Foundational Studies Credit
No

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