DATA 514 Multivariate Analytics

Concept-based introduction to applied multivariate analysis for data science students. Topics may include: multivariate normal distribution, supervised and unsupervised dimensionality reduction, principal component analysis, non-negative matrix factorization, partial least-squares, supervised principal components, multivariate feature selection, discriminant analysis, cluster analysis, and multidimensional scaling.

Credits

4

Prerequisite

DATA 511 or permission of department chair.

General Education

Offered

  • Fall