Syllabus

 

ISQS 7339, ADVANCED TOPICS IN MIS – Business Analytics, Fall 2009

Instructor: Zhangxi Lin

Office hours: 9:00-11:00a TTh or by appointment, BA 708

Class Meeting: 11:00-12:20p TTh, BA166 / BA 363

 

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Course Description:

This course is designed for those who intend to further improve their knowledge of data analysis in both theoretic and practical aspects. The course is to cover three topics:

 

1.    Decision trees and algorithm implementation

2.    Implementing market intelligence by clustering modeling

3.    Business optimization

Students will learn the skills of modeling using SAS Enterprise Miner 5.2. A number of specific case studies, such as risk management, market analysis, and banner advertisement allocation, will be presented.  Class discussions will be conducted to motivate the involvement of students.

 

Prerequisite: ISQS 6347, Data and Text Mining

 

Learning Outcomes:

A student who successfully completes this course should be able to:

1.    Implement advanced decision trees algorithms using SAS Enterprise Miner

2.    Apply the data mining approaches to CRM and targeted marketing using SAS Enterprise Miner. 

3.    Demonstrate optimization modeling skills using SAS/OR.

 

Assessment of Learning Outcomes:

     Learning will be assessed as follows:

1.    Knowledge of advanced decision tree algorithm implementation will be assessed with in-class exams, homework assignments, and a term project.

2.    The ability to do CRM and Targeted Marketing with data mining approaches will be assessed by in-class exams, homework assignments.

3.    Optimization modeling skills will be assessed in in-class quizzes, homework assignments, and a class project.

 

Required textbooks:

Decision Trees for Business Intelligence and Data Mining, (Preview the book)

Barry de Ville, SAS Press, October 2006, ISBN: 978-1-59047-567-6**

E-Books:

Getting Started with SAS Enterprise Miner 5.3, SAS Course Notes

Decision Tree Modeling, SAS Course Notes

Advanced Predictive Modeling Using SAS® Enterprise Miner, SAS Course Notes

Building and Solving Optimization Models with SAS/OR®, SAS Course Notes

Effective Web Mining: Attracting and Keeping Valued Cyber Consumers, SAS Course Notes

 

Optional Textbook:

CRM Segmentation and Clustering Using SAS Enterprise Miner, (Preview the book)

Randall S. Collica, SAS Press, 2007, ISBN: 978-1-59047-508-9**

Grading Policy:

  • Two midterm exams and one optional final exam (comprehensive). One of them will be dropped, whichever has the lowest score (200 points)
  • Six homework assignments (120 points)
  • Term project (120 points)
  • Attendance (60 points)

The total is 500 points.

Bonus credit is up to 50 points.

Attendance:

Previous studies showed that a student’s performance in a course is closely correlated to his/her class attendance. It is highly suggested that students attend all class meetings. The attendance is counted as 60 points and the roll check will be taken randomly. Missing one or two classes will lose 15 points for each. Missing more than two classes will result in no credit from the attendance. If a student has to skip a class meeting, he/she needs to inform the instructor in advance. If the absence was caused by an expected situation, the evidence must be presented to the instructor for the credit of the attendance points.

Examinations:

The exams will be closed book and closed notes. You are required to take the test in the class room at the scheduled date and time, unless you have disabling conditions. In which case, alternative place and time may be discussed. Most questions will be chosen from the questions at the end of the chapters and from the supplemental materials distributed in the class.

Make-up test may be available in the case that you could not take the test at the specific time due to medical emergencies or unexpected travel plans. If you need to reschedule a test, you must contact the instructor before the scheduled test and show the instructor the evidence of the excuse later, for example, a written note from a doctor for the medical problem.

Homework assignments:

Homework assignments must be completed by the duedate. Late submission will result in a lower credit.

Project:

Master students: A semester-long project is accomplished by the group by up to three students. Each group will deliver a final report for business problem solving focusing on data preparation and data analysis.

PhD students: PhD student will fulfill an academic research-oriented report/paper individually. The deliverable must contain demonstrative data analyses.

 

University Policies:

Requirements:  Please contact me if you have any special requirements, or if I need to make special accommodations for you during the semester.  I encourage you to visit with me about your progress in the course at any time.

Integrity.  Academic dishonesty will not be tolerated.  All students are required to adhere to the Texas Tech University Policy on Academic Honesty. 

Civility in the Classroom.  “Students are expected to assist in maintaining a classroom environment which is conducive to learning.  In order to assure that all students have an opportunity to gain from time spent in class, unless otherwise approved by the instructor, students are prohibited from using cellular phones or beepers, eating or drinking in class, making offensive remarks, reading newspapers, sleeping or engaging in any other form of distraction.  Inappropriate behavior in the classroom shall result in, minimally, a request to leave class.” 

ADA Requirements.  Classroom accommodations will be made for students with disabilities, if requested.

Religious Holidays.  A student who intends to observe a religious holy day should make that intention known to the instructor prior to an absence. A student who is absent from classes for the observance of a religious holy day shall be allowed to take an examination or complete an assignment scheduled for that day within a reasonable time after the absence.

 

References: