DANA 4830: Dimension Reduction & Classification I

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Course Format Lecture 4.0 h + Seminar 0.0 h + Lab. 0.0 h
Credits 3.0

Course Description

A core requirement in data analytics is the classification of a large group of records (items or objects) into different subgroups based on statistical criteria. The classification can be made easier if the number of dimensions of the data used is reduced. Students learn a number of techniques in reducing the number of dimensions in a data set without losing its latent structure. They also learn how to perform statistical classification into pre-defined groups. Topics include principal component analysis, factor analysis, multiple correspondence analysis, multivariate discriminant analysis, as well as stepwise techniques in regressions.

Registration in this course is restricted to students admitted to the Post-Degree Diploma in Data Analytics.

Prerequisites: A minimum "C" grade in DANA 4810.

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