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Activity Details

CE_17C Mon, 7/31/2017, 8:30 AM - 5:00 PM H-Holiday Ballroom 6
Successful Data Mining in Practice (ADDED FEE) — Professional Development Continuing Education Course
ASA , Section on Statistical Learning and Data Science
This one-day seminar is a practical introduction to and an overview of the techniques and strategies of data mining. While I will discuss the models in detail, the course will be application oriented rather than theoretical. Many of the standard techniques of data mining, including a review of modern model selection strategies for multiple regression such as the lasso, elastic net etc will be presented. In addition we'll cover classification and regression trees, neural networks, principal component regression, Naïve Bayes, random forests, and boosting. The course will be problem solving based, using real case studies from industry to illustrate which methods work well, when and why. We will emphasize problem formulation, the challenges of the data and the communication back to decision makes to effect maximum impact in the organization. No prerequisites other that a knowledge of the basics of regression are assumed. The applications will come from a wide variety of industries and include some applications from my personal experiences as a consultant for companies that deal with such topics as financial services, chemical processing, pharmaceuticals, and insurance.
Instructor(s): Richard De Veaux, Williams College
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