2004 Statistics 202A

Fall 2005 site ]

Background

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

8/7-8/11    ASA Meeting in Minneapolis
4/21-23    SIAM Conference on Data Mining

This Week

Dangling topics: ESS (R and Emacs), debugging tools in R. A brief introduction to database management systems and SQL and connections with constructions in R.

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.

Vote ] 2004 Presidential Election
Connect ] Wireless mobility on the Dartmouth Campus
Rehearse ] Data on rehearsals of 3 Beckett plays, tracking actors' movements and dialog delivery
[ Scan ] Consider the "embedding" of R on a mobile robot that explores the environment, taking measurements of various quantities (light, temperature, pressure, CO2)
[ Nab ] Using a large (3.6M observations) dataset on telecommunications fraud, we will examine "streaming data" methods.

Instructor    Mark Hansen
8951 Mathematical Sciences Building
University of California, Los Angeles
cocteau|@|stat.ucla.edu
www.stat.ucla.edu/~cocteau

Meeting    MW 3:00-4:20
A25 Haines Hall


Office Hours    Tuesday and Thursday 2-3, Friday 1-2
(or by appointment)
8951 Mathematical Sciences


Grading   
20% Class participation
80% Projects and in-class presentations

Syllabus    PDF | HTML ]

Texts    The following books are only recommended, although will probably prove to be extremely useful references long after the course is over.
  • Unix in a Nutshell, by Robbins
  • Programming Perl,
    by Wall, Christiansen, Orwant
  • Learning Perl Programming,
    by Schwartz and Phoenix
  • Mastering Regular Expressions, by Friedl
  • Programming with Data, by Chambers
  • S Programming,
    by Venables and Ripley
Texts will be added to this list as the quarter progresses.

Resources        A list of computing resources and selected online articles is forming here.

Data    Datasets from lecture will be made available here. Students are encouraged to try some of the commands/programs/ideas discussed in lecture using these datasets. Data for the projects are available from each separate Project site.