Businesses, governments, and society leave behind massive trails of data as a by-product of their activity. Increasingly, decision makers rely on intelligent systems...More >>
Businesses, governments, and society leave behind massive trails of data as a by-product of their activity. Increasingly, decision makers rely on intelligent systems to analyze these data systematically and assist them in their decision making. In many cases, automating the decision-making process is necessary because of the speed with which new data are generated. This course connects real-world data to decision making. Cases from finance, marketing, and operations are used to illustrate applications of a number of data visualization, statistical, and machine learning methods. The latter include induction, neural networks, genetic algorithms, clustering, nearest neighbor algorithms, case-based reasoning, and Bayesian learning. The use of real-world cases is designed to teach students how to avoid the common pitfalls of data mining, emphasizing that proper applications of data mining techniques is as much an art as it a science. In addition to the cases, the course features Excel-based exercises and the use of data mining software. Real-world datasets are included as an optional data mining exercise for students interested in hands-on experimentation. The course is suitable for those interested in working with and getting the most out of data as well as those interested in understanding data mining from a strategic business perspective. It will change the way you think about data in organizations.