MAA Short Course

MAA Short Course
Data Mining and New Trends in Teaching Statistics

January 3 (8:00 a.m. to 5:00 p.m.) - 4 (9:00 a.m. to 5:00 p.m.), 2009
Delaware Suite A, Lobby Level, Marriott Wardman Park Hotel
(For updated locations, click here; All locations are subject to change)

This two-day Short Course on Data Mining and New Trends in Teaching Statistics organized by Richard D. De Veaux, Williams College, and will take place on Saturday and Sunday, January 3 and 4.

There are two main themes. It will serve as a practical introduction to and an overview of data mining. It will also highlight some of the ways that technology has changed the way we practice and teach statistics.

Forty years ago the emphasis in introductory statistics was on formulas and their calculation. For example students were taught the formula for standard deviation and learned alternatives for avoiding rounding errors and short cuts for grouped data. Technology has made much of that subject matter irrelevant and obsolete. Today, we have been freed by technology to focus on the concepts of data analysis and inference. Where is this trend taking us? Computational methods in statistics are rendering some of our methods obsolete as well. How much should be introduced in the introductory statistics course?

Data mining is the exploration and analysis of large data sets by automatic or semiautomatic means with the purpose of discovering meaningful patterns. The knowledge learned from theses patterns can then be used for decision making via a process known as "knowledge discovery". Much of exploratory data analysis and inferential statistics concern the same type of problems, so what is different about data mining? What is similar? In the course I will attempt to answer these questions by providing a broad survey of the problems that motivate data mining and the approaches that are used to solve them.

The course will start with an overview of how the introductory statistics course is taught today and what the main concepts are. Examples of how technology enables us to get to the heart of the subject early will be given. Some elementary modeling concepts will be reviewed before we embark on an introduction to data mining. Then, we will use case studies and real data sets to illustrate many of the algorithms used in data mining. The applications will come from a wide variety of industries and include applications from my personal experiences as a consultant for companies that deal with such topics as financial services, chemical processing, pharmaceuticals, and insurance.


Advance registration fees are: member of the MAA or AMS -US$125; nonmember-US$175; student, unemployed, emeritus-US$50. On-site fees are: member of the MAA or AMS-US$140; nonmember-US$190; student, unemployed, emeritus-US$60. The registration/housing form can be found here.