Listed in: Mathematics and Statistics, as STAT-240
Amy S. Wagaman (Sections 01 and 02)
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.
The course is expected to include small group work, interactive labs, peer interactions such as peer review and short presentations, and a personal project, to foster student engagement in the course and with each other.
Requisite: STAT 111 or 135. Limited to 24 students. Fall semester. Professor Wagaman.
Section 01
M 11:20 AM - 12:10 PM ONLI ONLI
W 11:20 AM - 12:10 PM ONLI ONLI
F 11:20 AM - 12:10 PM ONLI ONLI
Section 02
M 02:20 PM - 03:10 PM ONLI ONLI
W 02:20 PM - 03:10 PM ONLI ONLI
F 02:20 PM - 03:10 PM ONLI ONLI
This is preliminary information about books for this course. Please contact your instructor or the Academic Coordinator for the department, before attempting to purchase these books.
| Section(s) | ISBN | Title | Publisher | Author(s) | Comment | Book Store | Price |
|---|---|---|---|---|---|---|---|
| All | An Introduction to Applied Multivariate Analysis with R | Springer 2011 | Everitt and Hothorn | TBD |