Multivariate Data Analys

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Amherst College

Making sense of a complex, high-dimensional data set is not an easy task. The analysis chosen is ultimately based on the research question(s) being asked. This course will explore how to visualize and extract meaning from large data sets through a variety of analytical methods. Methods covered include principal components analysis and selected statistical and machine learning techniques, both supervised (e.g. classification trees and random forests) and unsupervised (e.g. clustering). Additional methods covered may include factor analysis, dimension reduction methods, or network analysis at instructor discretion. This course will feature hands-on data analysis with statistical software, emphasizing application over theory.

Requisite: STAT 111 or 135. Limited to 24 students. Spring semester. Professor Wagaman.

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Permission is required for interchange registration during all registration periods.
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Course Sections

Multivariate Data Analys
Sect # Credits Instructor(s) Instructor Email Meeting Times Location
01 4.0 Amy Wagaman TTH 10:00AM-11:20AM; M 10:00AM-10:50AM SCCE E208; WEBS 102