Data mining is the process of extracting useful information and knowledge from a set of data. Mining is typically done on data sets too large to be analyzed by hand, but the same techniques are applicable to small, complex data. This course is an introduction to the most popular methods used in managerial data mining, and provides you with experience in using commercial software to explore real data sets. Models considered include those from statistics, machine learning, and artificial intelligence, such as discriminate analysis, logistic regression, clustering, neural nets, tree/rule induction, and association rule modeling. This course is methods oriented, as opposed to being methodology oriented, so you'll learn about when and how to use techniques and how to interpret their output rather than the details about how those techniques work. A laptop computer is required. Prerequisites: BQOM 2401 Statistical Analysis.