ISM 685- Business Analytics for Competitive Advantage
SYLLABUS
COURSE NUMBER: ISM 685
COURSE TITLE: Business Analytics for Competitive Advantage
CREDITS: 3:3
PREREQUISITES/COREQUISITES: None
FOR WHOM PLANNED:
Students in the MSITM, and Business Analytics Certificate Program.
INSTRUCTOR INFORMATION:
Dr. Hamid R. Nemati
Department of Information Systems and Supply Chain Management
Room 425 Bryan Building
nemati@uncg.edu
(336) 334-4993
CATALOG DESCRIPTION:
Data is viewed as a strategic organizational asset to gain and sustain competitive advantage.
Capabilities and infrastructure needed to achieve this along with recent advances in Business
Analytics are discussed.
STUDENT LEARNING OUTCOMES (SLOs):
Upon completion of this course, students will demonstrate a broad knowledge and clear
understanding of critical concepts, practices and issues in how Business Analytics can be used to
achieve and sustain competitive advantage. The course will make extensive use of BA software
including SAS Enterprise Guide and Enterprise Miner. Managerial, privacy and Organizational
implications of Business Analytics also discussed. The course introduces students to a number of
other emerging topics in Business Analytics. Specific course outcomes are:
1.
2.
3.
4.
5.
6.
7.
Describe and interpret the basic concepts of Business Analytics for competitive advantage.
Evaluate business problems and determine suitable analytical methods
Plan, develop and evaluate methods for pattern discovery, segmentation and clustering
Plan, develop and evaluate methods for market basket analysis and rule discovery
Evaluate the difficulties presented by massive, opportunistic and big data
Evaluate organizational, managerial and privacy issues related to business analytics.
Describe the emerging technologies in Business analytics for competitive advantage.
TEACHING METHODS AND ASSIGNMENTS FOR ACHIEVING LEARNING OUTCOMES:
This course blends research project, online discussion and online presentation.
Class assignments (SLOs 3, 4, 5, 6, and 7)
Each student is required to complete two assignments throughout the course.
• Assignment 1 deals with developing and using clustering for market segmentation and
2
•
designing market basket analysis (SLOs 3, 4)
Assignment 2 deals with using social network data to develop and use text mining and
sentiment analysis models. (SLOs 5, 6, 7)
Case Analysis (SLOs 1, 2, 3, 4, 5, 6, 7)
Each student is required to develop a comprehensive case analysis dealing with an emerging topic
in business analytics.
Class discussion and participation (SLOs 1, 2, 3, 4, 5, 6, 7)
Each student is required to regularly discuss online with the instructor on project progress.
Final Project and Report (SLO 6, 7)
Each student must present a final research proposal and a final report on a topic of approved by the
instructor.
Final Exam (SLOs 1, 2, 3, 4, 5, 6, and 7)
An on line timed final exams are required for class. The exam will test students on both the
business analytics topics and the use of software.
SOFTWARE NEEDED FOR THE COURSE:
This course makes extensive use of SAS® Enterprise Miner™ 12.1 and Enterprise Guide 5.1 for
this class. Students will have the option of downloading and installing their own copies of
software or access the software through cloud computer services provided by SAS. The
instruction for getting access to the software will be distributed to the students in the first week of
class and will be available via course BlackBoard.
EVALUATION AND GRADING:
The course will be letter graded. A student’s final grade will depend on the quality of the project
components.
Assignments (2 @ 15% each)
Class discussion and participation
Case Analysis and Report
Final Project and Report
Final Exam
30%
5%
15%
30%
20%
100%
The grade scale is based upon percent of points earned, and is as follows:
93-100%=A
73-76%=C
90-92%=A87-89%=B+
Below 73%=F
83-86%=B
80-82%=B-
77-79%=C+
TEXTS/READINGS/REFERENCES:
This course is Web based and delivered on-line. All needed teaching material including text books
and readings are available electronically via BlackBoard, Please sign into BlackBoard to gain
access to the material. In addition to the text books, and other teaching material, articles from both
academic and practitioner publications will be posted on the Blackboard.
3
1. Keeping Up with the Quants: Your Guide to Understanding and Using Analytics. By Thomas H.
Davenport and Jinho Kim, Harvard Business Review Press, ISBN-13: 978-1422187258. (Q)
2. Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, By Eric Siegel, Wiley
Press, ISBN-13: 978-1118356852. (P)
3. Competing on Analytics, Thomas Davenport. Harvard Business School Case Study, 2008.
4. Other case studies maybe added.
Additional Optional and Supplemental Reading list:
• “The Case for Investing in Business Analytics Technology,” by Dan Vesset Henry D. Morris
• “Driven by Data: The Importance of Building a Culture of Fact-Based Decision-Making,” SAS
Publishing
• “What is Data Science? The future belongs to the companies and people that turn data into
products,” by Mike Loukides
• “The Forrester Wave: Big Data Predictive Analytics Solutions, Forrester Group Report,
Available at Forrester.com
• “The Signal And The Noise: Why So Many Predictions Fail — but Some Don’t’“, by Nate
Silver.
• “Emerging Trends in Business Analytics,” by Ron Kohavi, Neal J. Rothleder, and Evangelos
Simoudis
• “Big Data: The next frontier for innovation, Competition and productivity,” McKinsey Report,
2012
• “Protecting Personal Privacy: Hauling Down the Jolly Roger,” by Michael Lesk
• “Defining Privacy for Data Mining,” by Chris Clifton Murat Kantarcioglu Jaideep Vaidya
TOPICAL OUTLINE/CALENDAR:
Week Topics
Week 1
• Introduction to Business Analytics for Competitive
Advantage
Weeks 2 and 3
•
•
Pattern Discovery
Clustering and profiling
Weeks 4
•
•
•
Association Rule Discovery
Link Analysis
Market Segmentation
Weeks 5 and 6
•
•
Social Network Analytics
Assignment 1 Due
Weeks 7, 8
•
•
•
Text Analytics
Sentiment Analytics
Case Study Due
•
Big Data Analytics
Weeks 9, 10
4
Weeks 11, 12
•
•
Privacy Implications of Business Analytics
Assignment 2 Due
Weeks 13
•
•
Cloud Computing and Business Analytics Issues
Managerial and Business Issues
Week 14
•
•
Final exam
Final Project Due
ACADEMIC INTEGRITY POLICY: Each student is required to sign the Academic Integrity
Policy on all major work submitted for the course. The Academic Integrity Policy can be found
at: http://sa.uncg.edu/handbook/academic-integrity-policy/.
FACULTY AND STUDENT GUIDELINES:
The faculty and students in the course are
expected to adhere to the faculty student guidelines stated at the following web page:
http://www.uncg.edu/bae/faculty_student_guidelines.pdf
ATTENDANCE POLICY: Since it is an online class, no physical class-room attendance is required. It is
the student’s responsibility to stay on track with readings and assignments to be successful in the
course.