ITC 353 - Data Mining

Credit Hours: 3
This course explores the use of descriptive and predictive data mining techniques to solve modern business issues. Students will learn to uncover important patterns and relationships in complex organizational data. Emphasis will be placed on the evaluation, selection, and application of primary data mining methods such as cluster analysis, classification and regression trees (CART), random forest models, and association rules, among others. Students will experience the application of these techniques to business scenarios through case studies and hands-on exercises. Upon completing this course, students will be able to effectively apply data mining techniques to business decision-making, an essential skill in the data analytics profession.

Prerequisite: QBA 237 ; and undergraduate business majors must be admitted to degree program.
Lecture contact hours: 3

Typically offered: Demand