|(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, insurance underwriting).
Predictive models such as classification and
decision trees, neural networks, regressions,
association analysis, link analysis, and others
will be studied.
Every Year, Fall and Spring|