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Syllabus: ISQS 6347, Spring 2009 Data & Text Mining Home | Schedule | Lecture notes | Personal Records Projects
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![MCj04043250000[1]](ISQS6347-09_files/image005.gif)
This syllabus is subject to further refinement
Schedule: MW 11:00-12:20p, BA
363 (Lab) or LH005 (Sometimes for lectures)
Instructor: Zhangxi Lin, (806) 742-1926, BA
708; Office hours: MWTr 9:00-11:00a, or by appointment.
Email:
zhangxi.lin@ttu.edu,
Course
Description:
This course covers the basics of data mining and text mining, with
applications in business intelligence, customer relationship management, fraud
and terrorism detection, improvement of resource utilization, clickstream web
mining, and credit scoring for loan applications. The software SAS Enterprise Miner will be
used extensively to illustrate use of decision trees, classification
algorithms, neural nets, clustering, and other data and text mining techniques.
Participants in this course are eligible to
receive a data mining certificate from SAS Institute and
Learning objectives:
- Understanding
the general principles of data mining
- Being
able to apply the commonly used functions of SAS Enterprise Miner to solve
data mining problems
- Developing
the skills of data mining modeling and data analysis with SAS Enterprise
Miner
- Mastering
general data preparation skills and tools
Prerequisites: A basic statistics course, such as ISQS 5345
“Statistical Concepts for Business & Management” or ISQS 5347 “Advanced
Statistical Methods” (B or better), or equivalent; Programming, SAS, and/or
Database are helpful but not required.
Textbook:
Optional:
·
Data
Mining for Business Intelligence: Concepts, Techniques, and Applications in
Microsoft Office Excel with XLMiner, Galit
Shmueli, Nitin R. Patel, Peter C. Bruce, ISBN: 978-0-470-08485-4, Hardcover,
279 pages, December 2006
·
Introduction to Data
Mining, Pang-Ning Tan, Michael Steinbach, and Vipin
Kumar, Addison Wesley, 2005, ISBN: 0321321367
(Website: http://www-users.cs.umn.edu/~kumar/dmbook/index.php
)
·
Data Mining – A
Tutorial Based Primer,
Richard Roiger, Michael Geatz, 3rd edition. Addison Wesley, 2003, ISBN
0201741288
Teaching
style: Case-based
hands-on learning process
Deliverable
and Grading Policy:
- Six
quizzes out of seven (120 points)
- Exercises/Homework
(100 points)
- One
term project (100 points)
- Final
exam (open-book/open-notes, 100 points)
The total is 420 points.
Bonus credit up to 40 points.
Projects:
The project must be fulfilled individually.
References:
- Dr. Peter
Westfall’s data mining class (Fall 2004)
- Introduction to Data
Mining - Using SAS Enterprise Miner, Patricia B. Cerrito, SAS Publishing,
ISBN: 978-1-59047-829-5 (http://support.sas.com/pubs)
- Principles
of Data Mining, David J. Hand, Heikki Mannila and Padhraic Smyth,
The MIT Press, August 2001, ISBN 0-262-08290-X, 425 pp.
- Data Mining:
Concepts and Techniques, Jiawei Han, Micheline Kamber, Morgan
Kaufmann, 2000, ISBN: 1558604898
- Data
Mining Using SAS
- Predictive
Modeling with SAS
- Online
SAS references
- Selected
online resources:
·
StatLib: http://lib.stat.cmu.edu/
·
MLnet: http://www.mlnet.org/
·
KDNuggets:
http://www.kdnuggets.com/
·
Weka: http://www.cs.waikato.ac.nz/ml/weka/
·
Open
source data mining projects: http://www.kdkeys.net/forums/72/ShowForum.aspx
·
Open
source data mining tools: http://dmoz.org/Computers/Software/Databases/Data_Mining/Public_Domain_Software/