DSC 301 - Applied Linear Algebra and Algorithms

Aspects of linear algebra and algorithms focused on applications to the data science field. Topics include eigenvalues, eigenvectors, symmetric matrices, singular value decomposition, principle component analysis, gradient descent, and optimizations.

Credit Hours: 3

Prerequisite Courses: MAT 151 or MAT 161.
Course Repeatability: Course may not be repeated. Maximum Repeatable Hours: 3

Click here for the Spring 2025 Class Schedule.