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.
Course times and locations
Section 01
M 10:00 AM - 10:50 AM WEBS 102
Section 01
Tu 10:00 AM - 11:20 AM SCCE E208
Th 10:00 AM - 11:20 AM SCCE E208
| ISBN | Title | Publisher | Author(s) | Comment | Book Store | Price |
|---|---|---|---|---|---|---|
| An Introduction to Applied Multivariate Analysis with R. | Springer 2011 | Everitt and Hothorn | The book is available as a .pdf via the Amherst College Library. A hard copy can be obtained via various other sources if desired. | TBD |