MGS 616 – Decision Support Systems – Spring 2014
Course Syllabus
Instructor:
Office:
E-Mail:
Credit Hours:
Office Hours:
Professor Sanjukta D. Smith
325E Jacobs Management Center
sdsmith4@buffalo.edu
3 hours
Mon & Wed 1:00 – 2:30 PM or by appointment only
Required Course Materials
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Data Mining Concepts and Techniques; by Jiawei Han, Micheline Kamber and Jian
Pei; Third Edition; ISBN-10: 0123814790.
Additional materials will be posted on UBLearns.
Course Description
The course is aimed at students who want to obtain more skills in developing, managing
and employing decision support systems. We will study the design, development and
implementation of information technology based systems and analytical models that
support managerial and professional work, drawing from the fields of statistics and
machine learning. In general, decision support systems are utilized by people who are
skilled in their jobs and who need to be assisted rather than substituted by a computer
system. In other words, the cognitive element needed to understand and derive practical,
realistic and implementable action-plans from the results generated by the IT-based
analytical models is just as important as the ability to design and operate these systems.
One of our key objectives therefore will be to develop these capabilities and we will learn
how to integrate domain knowledge while deriving these actionable results.
In this course, we will learn the tools, techniques and methodologies which are mainly
used nowadays by customer-centric corporations - retail, financial, communication, and
marketing groups - to help make decisions involving complex systems. These tools allow
these businesses to establish associations amongst in-house features for example price,
product positioning, or employees’ abilities, and outside issues for instance economic
indicators, competition, and client demographics. In addition, these techniques facilitate
them to find out the influence on sales, buyer satisfaction, and the proceeds from the
business. Lastly, these methodologies permit them to "drill down" into summary
information to observe specific transactional data. For example, using data mining tools a
seller might employ point-of-sale records of consumer purchases to drive targeted
promotions derived from a person’s past purchases. By mining demographic data from
comment cards or contracts, the vendor can expand on its line of goods and promotions to
attract particular client segments.
Grading
Group Homework Assignments (Weekly)
Midterm Exam
Final Exam
Individual Class Participation
Total
30%
30%
35%
5%
100%
Homework Assignments
Homework groups should consist of at least 3 and at most 5 members. The names of all
the team members must be displayed clearly on top of each assignment. The homework
assignments are intended to emphasize decision support systems theory and methods
covered in the classes and to provide you with practical knowledge using SAS Enterprise
Miner, which is an extensively used software package for data mining. These exercises
will be assigned and graded and will together make up 30% of the final course grade. The
due dates for these assignments will be announced in the class website and at the time of
assignment of the homework. Late submissions will be marked down 20% for every
day that it is late. Assignments will not be received after grades have been returned
in class.
Homework teams will perform periodic confidential peer evaluations. I will take these
evaluations into account when deciding the final homework grade for each team member.
I will provide the template for the evaluations as the start of the semester. Please be
honest with yourself when evaluating yourself and your team members. You cannot
improve yourself unless you are honest about your contributions and you will be doing
your team members serious disservice if you are dishonest about their contributions.
Midterm and Final Exams
The exams will be open-book. Exams may likely include a mixture of multiple choice
questions and problem-solving exercises. No make-up exams will be presented apart
from well-documented cases of absence due to sickness or a family emergency.
“Extra-Credit” Work
No “extra-credit work” will be assigned to any student. No exceptions. Allowing one
student to increase their grades by assigning an “extra-credit” work is patently unfair to
the rest of the class who has put in consistent work throughout the semester. Any student
making such a request will be turned down. No exceptions. There are ample opportunities
available throughout the semester to demonstrate one’s capability in the course. Students
are urged to make use of such opportunities.
Instructions Regarding Exams
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You will need to bring YOUR OWN calculator for the exams. Cell phones
CANNOT be used as calculators. You MAY NOT ask your neighbor for his/her
calculator. This is extremely disruptive in a testing environment, however
“quietly” done. It is your responsibility to ensure that you have an acceptable
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working calculator on the day of the exam. If you forgot your calculator or the
batteries don’t work or it is the “wrong” kind, then you will take the exam without
a calculator.
Cell phones must be switched off and out of sight during the exams. No excuses
accepted. Again, cell phones cannot be used as calculators.
Failure to comply with the above will result in a ZERO (0) grade for that exam.
Class Attendance and Participation
Frequent absences will harm your participation score for the class. Having said that,
merely showing up for class, even though it is vital, is not to be equated to participation.
Students should try to add to class discussions. Students should anticipate being "cold
called" all through the course and should therefore be ready. Throughout the semester, I
will note each student's participation in class. If you would like some pointers
concerning your class participation, please make an appointment with me and I will be
pleased to meet with you separately out of class.
General Class Rules
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Except for explicit directives from the professor, all exams are to be accomplished
by the student alone.
Copying work from the Internet or from printed materials without appropriate
citations will be considered plagiarism and will be cause for punitive action as per
the regulations of SUNY at Buffalo.
Any unauthorized teamwork will be thought of as cheating and the student(s)
concerned will be punished to the fullest extent as prescribed by the university’s
regulations.
You are expected to behave professionally in the classroom – i.e., refrain from
personal chatter while in class and switch off your cell phones.
Any instance of cheating in the midterm or the final exam will result in a
score of zero in that exam for all parties involved – no second chances.
Furthermore, the assignment of an F grade for the course and/or possible
expulsion from the program will be strongly considered.
Tentative Course Outline
This course schedule provides a general plan for the class. Deviations may be
necessary.
Topics
Introduction
Data Preprocessing
Data Warehouse and OLAP Technology: An Overview
Data Cube Computation and Data Generalization
Introduction to SAS EMiner
Mining Frequent Patterns, Associations and Correlations
Association Rule Mining using SAS EMiner
Classification and Prediction
Decision Trees using SAS EMiner
Neural Networks using SAS EMiner
Regression using SAS EMiner
Cluster Analysis
Cluster Analysis using SAS EMiner
Midterm Exam: March 13
Final Exam: May 12, 2014 – 12:00 to 1:20 PM in Hoch 114