TO: ASA CO/WY Chapter
SUBJECT:  Chapter update

Hello - I hope this year has started off well for everyone. The purpose of
this note is to communicate some new activities and opportunities that may
be of interest to the CO/WY chapter.  This message contains the following
topics:

- POSSIBILITY OF TRAVELING SHORT COURSE IN ANALYZING CROSS CLASSIFIED CATEGORICAL DATA
- ASA WEBINAR MARCH 18 AT 3:30PM MT 5:30PM ET
- UC-DENVER TEACHING OPPORTUNITY
- UC-DENVER POST-DOC POSITION OPEN
- DATA MINING CONFERENCE (AUGUST 2009)

##### POSSIBILITY OF TRAVELING SHORT COURSE IN ANALYZING CROSS CLASSIFIED CATEGORICAL DATA

The CO/WY Chapter is being considered for the traveling short course for
Cross Classified Categorical Data.  If we are selected for this course,
seats will be limited and the cost is estimated at $25/student.  Please
email me (Christopher Nelson at chnelson@du.edu) if you would be interested
in attending this course.  If you have already emailed me expressing
interest in this specific course, there is no need to send another email.
Thanks.

##### ASA WEBINAR MARCH 18 AT 3:30PM MT 5:30PM ET

The next ASA webinar on K-12 statistics education topics will be held on
Wednesday, March 18 at 3:30 MT (5:30 ET).  

Topic: What Data Mining Teaches Me About Teaching Introductory Statistics

What's the connection between the Statistics that written about and taught
in an introductory statistics textbook and the statistical analyses that are
performed all throughout the world every day? A fact of life for the
Statistics course is that many people teaching Statistics have little
practical experience with the subject other than what they've learned by
reading about or teaching it. How close is the book version of Statistics to
real world practice?

Data mining is a process of exploratory modeling of phenomena from very
large data sets or databases. Because of the size of the data, mistakes made
by the data miner are often magnified. The consequences of those innocuous
little assumptions about models that we harp on in our introductory courses
suddenly come to life in frightening ways. Looking at real data mining
problems provides a wonderful lens to examine what we should emphasize and
what we might relax in our first courses.  

In this webinar, I'll introduce some of the challenges I see in conveying
the essence of what we do in an introductory course. Then I show some real
examples of data mining problems I've been involved with and how they can
inform us about teaching in the first statistics course, from the first
exposure to concepts in elementary or middle school through a high school or
a 2 or 4 year college course.  No previous exposure to data mining will be
assumed. 

Presenter: Dick De Veaux is Professor of Statistics at Williams College.
Dick holds degrees in Civil Engineering (B.S.E. Princeton), Mathematics
(A.B. Princeton), Dance Education (M.A. Stanford) and Statistics (Ph.D.,
Stanford).  Before Williams, Dick was an Assistant Professor at the Wharton
School and the Engineering School at Princeton. He has won numerous teaching
awards including a "Lifetime Award for Dedication and Excellence in
Teaching" from the Engineering Council at Princeton.  He has won both the
Wilcoxon and Shewell (twice) awards from the American Society for Quality
and was elected a fellow of the ASA in 1998.  Dick has been a consultant for
over 20 years for such Fortune 500 companies as Hewlett-Packard, Alcoa,
American Express, Bank One, GlaxoSmithKline, Dupont, Pillsbury, Rohm and
Haas, Ernst and Young, and General Electric. He holds two U.S. patents and
is the author of over 30 refereed journal articles. He is the co-author,
with Paul Velleman and David Bock, of the critically acclaimed textbooks
"Intro Stats", "Stats: Modeling the World" and "Stats: Data and Models" all
published by Pearson and the newly published "Business Statistics" with
Norean Sharpe and Paul Velleman. He was named the 2008 Statistician of the
Year by the Boston Chapter of the American Statistical Association.    

##### UC-DENVER TEACHING OPPORTUNITY

UC-DENVER is looking for someone to teach one of our upper undergraduate
courses in Statistics this summer. The description is included below. The
instructor will either need to have a PhD or have published a peer-reviewed
article. This would be a great opportunity for someone wanting to get a
little teaching experience. 

The class is scheduled for 1:15-345pm on Tuesdays and Thursdays starting
June 8th and ending August 1. The pay will be between $4000 and $4500 for
the full course.  If you are interested, please email
<mailto:Stephanie.Santorico@ucdenver.edu> Stephanie.Santorico@ucdenver.edu
(Phone: 303.556.2547) and be sure to include a CV. Please feel free to
forward or circulate this email.

MATH 4830 (3 credit hrs) Applied Statistics. Review of estimation,
confidence intervals and hypothesis testing; ANOVA; categorical data
analysis; non-parametric tests; linear and logistic regression.  Prereq: an
introductory course in statistics such as MATH 2830 or permission of
instructor. Cross-listed with MATH 5830

##### UC-DENVER POST-DOC POSITION OPEN

Position Title: Postdoctoral Position in Statistics

Location: Department of Mathematical and Statistical Sciences at the
University of Colorado Denver

Required degree: PhD

Deadline: Application review beings April 3, 2009

Please contact  <mailto:Stephanie.Santorico@ucdenver.edu>
Stephanie.Santorico@ucdenver.edu for more information. Phone: (Phone:
303.556.2547).

##### DATA MINING CONFERENCE (AUGUST 2009)

Salford Systems' Data Mining Conference 2009

San Diego, California

Conference Dates: August 23-25, 2009

Post-Conference Training: August 26-28, 2009 

Don't miss Salford Systems' 6th International APPLIED Data Mining
Conference, featuring real world applications and examples.

Beginners to data mining will gain expertise. Experienced data miners will
have an opportunity to exchange ideas with other experts in a variety of
industries. 

Keynotes and panel discussions will address topics of current interest in
data mining including:

**The Credit Crunch: How is Data Mining Helping?  Is Data Mining to Blame?

**Finding Needles in Haystacks: Combating Terrorism, Fraud, and Criminal
Activity.

**Advertising, Politics, Drug Discovery and Telecommunications: How Do These
Applications Benefit from Data Mining? 

Previous conference presentation topics include: credit risk modeling,
targeted marketing and campaign optimization, analytical CRM, fraud
detection, drug discovery, insurance risk, epidemiology, environmental
forecasting, clinical medicine, proteomics and genomics, and
state-of-the-art research from leading academic institutions. 

Presenters at prior conferences include: The International Monetary Fund,
Barnes and Noble, Pfizer, Union Bank, Wells Fargo, Stanford Linear
Accelerator Center, Cold Spring Harbor Laboratory, Novartis, Columbia
University School of Public Health, Harvard Medical School, HSBC,
International Steel Group, Cap Gemini, AT&T Labs-Research,
PricewaterhouseCoopers, Liberty Mutual Insurance. 

Presentation lists from previous conferences:

http://www.salforddatamining.com/prevprog.php

 Key Links:

Conference website: http://www.salforddatamining.com

Conference update
list:http://www.salforddatamining.com/conferenceupdatelist.php

Registration: http://www.salforddatamining.com/docs/2009conferenceForm.pdf

Abstract Submissions: http://www.salforddatamining.com/2009Abstract.php

Post-conference Training:
http://www.salforddatamining.com/postConferenceTraining.php 

Thanks,

 Christopher Nelson, Ph.D.
ASA CO/WY Chapter President
chnelson@du.edu

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