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2004 Statistics 202A
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[ 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
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.
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[ Vote ]
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2004 Presidential Election
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[ Connect ]
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Wireless mobility on the Dartmouth Campus
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[ Rehearse ]
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Data on rehearsals of 3 Beckett plays, tracking actors' movements
and dialog delivery
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[ Scan ]
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Consider the "embedding" of R on a mobile robot that explores
the environment, taking measurements of various quantities (light,
temperature, pressure, CO2)
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[ Nab ]
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Using a large (3.6M observations) dataset on telecommunications
fraud, we will examine "streaming data" methods.
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Instructor
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Mark Hansen
8951 Mathematical Sciences Building
University of California, Los Angeles
cocteau|@|stat.ucla.edu
www.stat.ucla.edu/~cocteau
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Meeting  
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MW 3:00-4:20
A25 Haines Hall
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Office Hours  
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Tuesday and Thursday 2-3, Friday 1-2
(or by appointment)
8951 Mathematical Sciences
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Grading  
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20%
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Class participation
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80%
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Projects and in-class presentations
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Syllabus  
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[ PDF | HTML ]
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Texts  
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The following books are only recommended, although
will probably prove to be extremely useful references
long after the course is over.
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Unix in a Nutshell, by Robbins
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Programming Perl,
by Wall, Christiansen, Orwant
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Learning Perl Programming,
by Schwartz and Phoenix
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Mastering Regular Expressions, by Friedl
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Programming with Data, by Chambers
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S Programming,
by Venables and Ripley
Texts will be added to this list as the quarter progresses.
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Resources  
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A list of computing resources and selected online
articles is forming here.
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Data  
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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.
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