Syllabus
ISQS 7342-001, ADVANCED
TOPICS IN MIS – Business Analytics, Fall 2008
Instructor:
Office hours:
Class Meeting: 12:30-1:50p TTh, BA271 / 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
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** |
|
CRM
Segmentation and Clustering Using SAS Enterprise Miner,
(Preview the
book) Randall S. Collica,
SAS Press, 2007, ISBN: 978-1-59047-508-9** |
Applied Analytics Using SAS® Enterprise MinerTM 5, 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
Grading Policy:
The total is 480 points.
Bonus credit is up to 60 points.
Projects:
Projects must be fulfilled individually. There are two choices of project formats: Business problem solving which focuses on data preparation and data analysis, and Research-oriented which emphasizes on the research proposal with demonstrative data analysis.
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.
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: