|(3 cr.) The course introduces the techniques of
predictive modeling and analytics in a data-rich
business environment. It covers the process of
formulating business objectives, data selection,
preparation and partition to successfully
design, build, evaluate and implement predictive
models for a variety of practical business
applications (such as direct marketing, cross
selling, customer retention, delinquency and
collection analytics, fraud detection, machine
failure detection and insurance underwriting).
Students also study predictive models such as
classification and decision trees, neural
networks, regressions, association analysis, link
analysis and others.
Every Year, Fall and Spring|