Syllabus: ISQS 6347, Spring 2009

Data & Text Mining


Home | Schedule | Lecture notes | Personal Records

Projects | Group Sign-up | View Groups



This syllabus is subject to further refinement


Schedule: MW 11:00-12:20p, BA 363 (Lab) or BA257 (Sometimes for lectures)

Instructor: Zhangxi Lin, (806) 742-1926, BA 708; Office hours: MWTr 9:00-11:00a, or by appointment.

Email:, MSN:, Google talk ID: zhangxi.lin




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 Texas Tech University.

Learning objectives:

  • Understanding the general principles of data mining
  • Developing the skills of data mining modeling and data analysis with SAS Enterprise Miner to solve data mining problems, which include:
    1. Classification modeling
    2. Clustering
    3. Association analysis and link analysis
    4. Text mining

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.


Assessment of Learning Outcomes:

  • Knowledge of the general principles of data mining will be assessed with quizzes, and homework assignments.
  • The ability to apply SAS Enterprise Miner to solve data mining problems will be assessed by guided exercises, and term project.



Introduction to data mining – using SAS Enterprise Miner, by Patricia B. Cerrito, SAS Publishing, 2006, ISBN-13:978-1-59047-829-5 / 1-59047-829-0


·         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: )

·         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:

·         Two midterm exams and one final exam, one of the exam will not be counted whichever has the lowest score  (160 points)

·         Guided Exercises (80 points. These exercises will be initially guided in the classroom and completed at home.)

·         Homework (80 points)

·         One term project (80 points)

Homework/Exercise assignments must be completed in designated time. Late submission will result in a lower grade.

Total bonus credit will not exceed 40 points. Letter grades are based on the percentage points earned out of the total 400 points:

·         A – 90% or higher

·         B – 80-89.9%

·         C – 70 – 79.9%

·         D – 60 – 69.9%

·         F < 60%


The term project must be fulfilled with no more than three students in a group.


·         StatLib:

·         MLnet:

·         KDNuggets:

·         Weka:

·         Open source data mining projects:

·         Open source data mining tools:

Job Search: 


Note: For updating the VPN access to TTU’s campus network, see: