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
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.