MAT 495 - Fundamentals of Data Science

College of Arts and Sciences
3 credit(s) At least 1x fall or spring

Double Numbered with MAT 695
Fundamental methods for data science, such as regression, linear discriminant analysis, k-nearest neighbors, support vector machine, k-means, principal component analysis, and nonlinear dimension reduction. Performance evaluation and model selection. Additional work required of graduate students.
PREREQ (MAT 331 AND MAT 521) OR (MAT 503 AND MAT 523)
Shared Competencies Scientific Inquiry and Research Skills; Information Literacy and Technological Agility