Computing has always been an essential ingredient of statistical practice. While probability theory provides us with a mathematical foundation for describing data and studying statistical inference, computing technologies act as a medium through which analyses are actually realized. Our ability to manipulate data and to audition new methodologies depends on and is limited by our familiarity with computing technologies. To some extent, even our notion of what constitutes "data" is a product of our background in computing.

Through a series of group projects, we will study tools for "exploratory computing." We will emphasize programming and scripting languages over point-and-click interfaces. We hope to instill a problem solving ability so that you will learn languages on your own, cull online documentation or tutorials, find books and manuals.

Upcoming Events

9/26		R Bootcamp, Department Orientation
9/30		Center for Statistical Computing Open House

This Week

In our first meeting, we will form quarter-long work groups; we will begin with Unix and "pipe" basics. We will hold extra (voluntary) lab sessions on Fridays for the first three weeks of the course to help students get acclimated to their computing environment.

Projects

Below we have a brief description of the first three projects for the course. A more complete listing of "deliverables" will be made as the course progresses (and the actual assignments are made). Each should take about two weeks, and each is a group project.

[ Map ]	Exploring geographic data
[ Connect ]	Wireless mobility on the Dartmouth Campus
[ Sent ]	Following Enron's e-mail traffic from boom to bust
[ Scan ]	Consider the "embedding" of R on a mobile robot that explores the environment, taking measurements of various quantities (light, temperature, pressure, CO2)
[ Live ]	1 year, 100 students, 350,000 hours of continuous data