Linear Algebra for Data Science.

Catalog description

Linear algebra is the generalized study of vector spaces and transformations in n-dimensions. In this course, students begin by developing an understanding of the concepts and operations of linear algebra, which are frequently employed in the analysis of data. Topics include: formulating and solving linear systems as matrix-vector equations, performing basic computations involving matrix algebra, orthogonal projections, and eigenvalue-eigenvector problems. Students are then exposed to important topics for data science, such as singular value decomposition and principle component analysis. The use of software to perform computations is emphasized. Prerequisite: Graduate status in the Data Science program. Corequisite: ANLT 251 Data Science Socratic Lab.