BAN 502 - Predictive Analytics

Course Description: This course explores computer-intensive methods for model selection, parameter estimation, and validation for predictive analytics. The course focuses on techniques and algorithms from the statistical and machine learning disciplines, and has a strong programming component. Example topics that could be included in this course include: ordinary least squares regression, multi-nominal logistic regression, classification and regression trees, neural networks, support vector machines, naive Bayes, principal components analysis, cluster analysis, and regularization. Each technique is accompanied with a focus on application and problem-solving.

Credit Hours: 3

Corequisite Courses: None
Prerequisite Courses: BAN 500 and MIS 503 (may be taken concurrently)
Additional Restrictions/ Requirements: None
Course Repeatability: Course may not be repeated Maximum Repeatable Hours: 3

ADDITIONAL COURSE INFORMATION

Equivalent Courses: None
Undergraduate Crosslisting: None
Additional Course Fees: None
Course Attribute: None

Click here for the Spring 2025 Class Schedule.