Multivariate Statistics for Data Science

3 credits · Graduate School

This course covers several multivariate statistical topics used in data science: principal component analysis, rotation, canonical correlations, multidimensional scaling, correspondence analysis, and hierarchical clustering. Matrix algebra and basics about multivariate data will be taught at the beginning. The course has both theoretical and applied components. R will be used as the primary computing tool. Prerequisite: PSY-GS 8861 or equivalent [3]