3.00 Credits
Description: The course introduces predictive modeling and analytics techniques in a data-rich business setting. It covers the process of formulating business objectives, data preparation and study design, followed by implementation and evaluation of predictive models for a variety of practical business applications. The set of analytical tools discussed in the course contains cluster analysis, decision trees, neural networks, regressions, and association analysis. Application examples include customer retention, customer segmentation, delinquency analytics, fraud detection, and market basket analysis. The course takes 'learning-by-doing' approach with the use of the state-of-the-art analytical software. Prerequistes: QMTH 205 and QMTH 210, or QMTH 680 and Graduate Status.