Listed in: Mathematics and Statistics, as STAT-240
Amy S. Wagaman (Section 01)
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. Omitted 2021-22. Professor Wagaman.
How to handle overenrollment: For the Fall, priority for rising sophomores and Statistics majors. For the Spring, priority for sophomores and Statistics majors.
Students who enroll in this course will likely encounter and be expected to engage in the following intellectual skills, modes of learning, and assessment: quantitative work, problem sets, quizzes or exams, group work, use of computational software, project, oral presentations
Section 01
M 11:00 AM - 11:50 AM SMUD 206
W 11:00 AM - 11:50 AM SMUD 206
F 11:00 AM - 11:50 AM SMUD 206
| ISBN | Title | Publisher | Author(s) | Comment | Book Store | Price |
|---|---|---|---|---|---|---|
| An Introduction to Applied Multivariate Analysis with R | Springer 2011 | Everitt and Hothorn | TBD | |||
| Hands-On Machine Learning with R | Chapman and Hall/CRC | Boehmke and Greenwell | TBD |