STT 450 - Statistical Data Mining

An introduction to the fundamental principles and applications of the most commonly used data mining techniques such as regression, classification, and clustering methods. The data mining techniques may include linear regression, classification, re-sampling methods, linear model selection and regulation, tree-based methods, Support Vector Machines (SVM), and clustering methods. Students will learn how to explore and analyze large high-dimensional real-world apllications to build effective systems for prediction by using standard programming tools.

Credit Hours: 3

Prerequisite Courses: STT 411 or STT 412 or permission of instructor.
Course Repeatability: Course may not be repeated. Maximum Repeatable Hours: 3

Click here for the Spring 2025 Class Schedule.