Department of Management
Advanced Business Analytics
Semester: Spring 2016
Course: MGT 4270/26911/0 (3 Hours)
Faculty: Parthasarati Dileepan, Ph.D.
Phone and Email: (865) 315-8280; Campus: 423-425-4675; Dileepan@mocs.utc.edu
Office Hours and Location: Online: 12:00 – 2:00 p.m. MW; On Campus (Room 106 Fletcher):
2:00 – 3:00 p.m. TR; and by appointment
Course Meeting Days, Time, and Location: 12:15 pm-01:30 pm TR; Room: Fletcher 314
Course Catalog Description: This course builds on the Introduction to Business Analytics course
(MGT 4260) and will describing various methodologies for describing the structured data and applying
analytics approaches to understanding unstructured data from disparate sources.
Course Student Learning Outcomes
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Understand and apply core ideas in data mining such as data exploration, data reduction,
classification, association and prediction
Learn to use XL-Miner and SAS Enterprise Miner (SAS EM) for performing data mining tasks
Perform data dimension reduction using Principal Component Analysis, Regression analysis,
classification and regression trees
Understand and use tools to evaluate the performance of classification and predictive methods
Develop predictions using the Regression Analysis modeling (XL-Miner and SAS EM)
Classify outcomes using the following predictive analytic tools (XL-Miner and SAS EM)
o K-NN Classifier
o Logistic Regression
o Neural Nets
o Discriminant Analysis
Course Pre/Co Requisites: MGT 4260 and junior standing or department head approval.
Course Materials/Resources: Data Mining for Business Intelligence: Concepts, Techniques, and
Applications in Microsoft Office Excel with XLMiner, 2nd Edition ISBN : 978-0-470-52682-8
Course Evaluation/Assessment: As given below:
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Two exams
Homework
200
100
Grading Scale: A: Average > 90%; B: Below 90% but > 80%; C: Below 80% but > 70%; D: Below 70%
but > 60%; F: Average < 60%
Policy for Late/Missing Work: As given below:
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No late homework/Quiz will be allowed more than 5 weekdays after it was due.
Two late homework/quiz completions within 5 weekdays are allowed without any penalty.
After the first two late homework/quiz, 5% penalty per weekday will be assessed for the third to
fifth late quiz completions.
No late homework/quiz will be allowed after the fifth late completion.
Course Calendar/Schedule: Weekly schedule (Subject to change)
Week
Date
Chapter
Week 1
1/11/2016
Week 2
Week 3
Week 4
Week 5
Week 6
Week 7
1/18/2016
1/25/2016
2/1/2016
2/8/2016
2/15/2016
2/22/2016
Chapter 1
Chapter 2
Excel Add-in
SAS
Chapter 3
SAS
Chapter 4
Chapter 5
Chapter 6
Week 8
Week 9
2/29/2016
3/7/2016
Week 10
Week 11
Week 12
Week 13
Week 14
3/21/2016
3/28/2016
4/4/2016
4/11/2016
4/18/2016
Important Dates:
March 14 – 20, 2016
March 20 (Sunday)
April 25, 2015
Topic
Introduction
Overview of Data Mining Process
XL-Miner
Enterprise Miner
Data Visualization
Enterprise Miner
Dimension Reduction in XL-Miner and SAS EM
Evaluating Predictive Performance
Multiple Regression Analysis
Exam 1
Must be completed before 11:59PM on 3/2/2016
Chapter 7
k-Nearest Neighbors (kNN)
Chapter 8
The Naive Bayes Classifier
Week of 3/14/2016 Spring Break -- no class
Chapter 9
Classification and Regression Trees
Chapter 10
Logistic Regression
Chapter 11
Neural Nets
Chapter 12
Discriminant Analysis
Chapter 13
Combining Methods: Ensembles and Uplift Modeling
Exam 2
Must be completed before 11:59PM on 5/1/2016
:
:
:
Spring Break
Last day to drop with a W
Last class day
Communication: The primary means of communication is via email. To enhance student services,
the University uses your UTC email address for all communications. Please check your UTC email on a
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regular basis at least twice a day. If you have problems with accessing your UTC email account, contact
the Call Center at 423/425-4000. In addition, you may use (865) 315-8280 to contact me by phone and
leave a message. In either of these cases you can expect a response within 24 hours. My campus phone
number is 423-425-4675. Responses to messages left in this number may take longer.
Honor Code Pledge (from the UTC Student Handbook): I pledge that I will neither give nor
receive unauthorized aid on any test or assignment. I understand that plagiarism constitutes a
serious instance of unauthorized aid. I further pledge that I exert every effort to insure that the
Honor Code is upheld by others and that I will actively support the establishment and continuance
of a campus-wide climate of honor and integrity.
Veterans Services Statement: The office of Veteran Student Services is committed to serving
all the needs of our veterans and assisting them during their transition from military life to that of
a student. If you are a student veteran or veteran dependent and need any assistance with your
transition, please refer to http://www.utc.edu/greenzone/ or http://www.utc.edu/records/veteranaffairs/. These sites can direct you the necessary resources for academics, educational benefits,
adjustment issues, veteran allies, veteran organizations, and all other campus resources serving
our veterans. You may also contact the coordinator of Veteran Student Programs and Services
directly at 423.425.2277. THANK YOU FOR YOUR SERVICE.
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