Listed in: Mathematics and Statistics, as STAT-240
Amy S. Wagaman (Section 01)
This course will explore how to extract meaning from multivariate data sets through a variety of analytical methods, chosen based on the research question(s) being asked. 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), with discussion of the modeling process. Instructors may opt to cover additional methods, such as factor analysis, dimension reduction methods, or network analysis. This course will feature hands-on data analysis with statistical software, emphasizing application over theory.
Requisite: Student has completed or is in process of completing any of the following course(s): STAT 111 or MATH/STAT 135 or STAT 136 or PSYC 122 or has a STAT 230 placement or has consent of the instructor. . Limited to 24 students. Omitted 2023-24.
Recommended: Concurrent registration in or prior completion of STAT 231 is strongly recommended.
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, interactive labs, problem sets, quizzes or exams, group work, use of computational software, projects, oral presentations
Course Materials