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.

Prerequisites: DATA 511 or permission of department chair.

4 Credits

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