Syllabus: ISQS 6347, Spring 2008
Data & Text Mining
This syllabus is subject to further refinement
Schedule: MW 11:00-12:20p, BA 363 (Lab) or LH005 (Sometimes for lectures)
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
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.
Required: 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:
The total is 360 points.
The project must be fulfilled individually.
· StatLib: http://lib.stat.cmu.edu/
· MLnet: http://www.mlnet.org/
· KDNuggets: http://www.kdnuggets.com/
· 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/