Modern Methods of Multivariate Analysis

Catalog description

This course is designed as a first course in multivariate analysis, with emphasis on learning theoretical concepts and corresponding analytical tools in R. Topics to be covered include: discrete and continuous multivariate distributions, MANOVA, multivariate multiple regression, principal component analysis, discriminant analysis, canonical correlation analysis, dimension reduction methods such as multidimensional scaling and stochastic neighborhood embedding, data visualization, Gaussian graphical models and high dimensional inference. Upon successful completion of the course, the students will have sufficient practical knowledge for analyzing any multivariate data set. In this course, topics relevant to modern day problems in big data analysis are discussed and the multivariate tools necessary to tackle them are discussed. Prerequisite(s): DATS 7100 >= C or STAT 7630 >= C Lecture Hours: 3 Repeatability: May not be repeated for credit. Grade Mode: Normal, Audit Program Restrictions: DPHIL_BIOS, MS_BIOS, MS_DSCI Campus Restrictions: AU Online Schedule Type (Primary): Lecture Schedule Type (Additional): Asynchronous Instruction Click here for the Schedule of Classes.