PMBA 8040: Data Driven
Decision Making
Syllabus for Spring 2017
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Instructor: Dr. Satish
Nargundkar Office Hours: By
appointment |
E-Mail : snargundkar@gmail.com CRN:
16346, Buckhead Center Thursdays 6:00 – 9:00 PM |
Class Meetings: The class will meet on the following dates this semester:
|
1/12/2017, 1/26/2017, 2/9/2017, 2/23/2017, 3/9/2017, 3/23/2017, 4/6/2017, 4/20/2017 |
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Required Text
Applied
Predictive Analytics,
by Dean Abbott. Wiley, ISBN: 978-1-118-72796-6.
Course Catalog Description
Evidence based decision
making is critical to an organization’s ability to compete in the global
economy. This course explores the role of data in driving decisions made by
managers across and within functional boundaries. Specifically, students learn
to understand, visualize and present data that supports organizational decision
making process. They also learn how to
create data driven models such as regression and decision trees to make
decisions to address critical challenges faced by organizations and society.
The course features hands-on exercises with appropriate software.
Learning Objectives:
Upon successful completion of
the course, students will be able to:
1. Evaluate a business case to identify and frame the
business problem appropriately.
2. Translate the business problem into an analytics
problem that can aid decision making.
3. Identify key parameters of the problem – the dependent
variable, independent variables, and the time frame for the data collection.
4. Apply basic data cleaning methods.
5. Perform univariate analysis to examine and describe
the data.
6. Analyze the data over time – apply Time Series
Forecasting methods when necessary.
7. Perform cross-tabulations, graphs/charts to analyze
simple bivariate relationships.
8. Create a predictive model using Multiple Regression
Analysis.
9. Classify observations using Linear and/or Logistic
Regression Techniques.
10. Apply Cluster Analysis to segment data.
11. Explain in your own words the basic working of
recommendation systems.
12. Perform Market Basket Analysis to compute
probabilities of purchasing patterns.
13. Evaluate the effectiveness and validity of models
using R-square, Standard Error, K-S test measures.
14. Apply Bayes’ Theorem to compute posterior
probabilities and evaluate the impact of new information.
15. Draw decision trees and solve them to make appropriate
recommendations based on risk /return analysis.
16. Interpret the results of all mathematical analyses in
the context of the business.
17. Write reports in translate the mathematical analysis
into plain business language for converting the results into recommendations
for decision making and implementation.
Grading:
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Course Average |
Grade |
Course Average |
Grade |
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Assignments |
20% |
|
94-96,
97+ |
A,
A+ |
77-79 |
C+ |
|
Midterm |
30% |
|
90-93 |
A- |
73-76 |
C |
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Final Exam |
30% |
|
87-89 |
B+ |
70-72 |
C- |
|
Team Project |
20% |
|
83-86 |
B |
60-69 |
D |
|
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80-82 |
B- |
Less
than 60 |
F |
MBA Grading Statement:
In general, in MBA and PMBA prefix courses about 35% of the students will receive a grade of
A+, A, or A-. Generally, the majority of the remaining students will
receive grades of B+, B, or B-. Students
who significantly lag in performance shall earn grades at the C level or lower
as appropriate.
Project Teams:
Teams of 5 students each
(with some teams of either 4 or 6 if enrollment is not a multiple of 5) will be
formed before midterm for project work.
Attendance: Students are expected to attend all classes for the entire duration.
Given the compressed nature of the semester and the fact that there will be
hands-on quantitative work as well as discussion, it makes sense for you to be
in class. Please notify me if you must miss a class due to work related travel
or other reasons. In any case, you are responsible for keeping up with anything
you might have missed due to an absence.
Course Assessment:
Your constructive assessment
of this course plays an indispensable role in shaping education at
Ethics and Academic Honesty:
I encourage you to share
your work and knowledge, but draw the line at plagiarism and copying the work
of others. Work with other students or
seek assistance from another person only is specifically allowed. If you are allowed to work with another
student (or anyone) on an assignment, acknowledge the collaboration. Never copy another student’s work or
allow another student to copy your work.
Do not use any prohibited materials.
We take issues of academic honesty
very seriously. Students are expected to recognize and uphold standards of
intellectual and academic integrity in all work. The university assumes as a
basic and minimum standard of conduct in academic matters that students be
honest and that they submit for credit only the products of their own efforts.
The University policy on academic dishonesty is spelled out in Section 1350 of
the Graduate
Catalog.
The following are instances of
academic dishonesty:
Ø
plagiarism (see course and GSU website for specific
examples of what constitutes plagiarism),
Ø
cheating on examinations,
Ø
unauthorized collaboration with others
falsification of materials,
Ø
multiple submissions (i.e., submitting the same work
for credit in more than one class).
Lack
of knowledge is not an acceptable defense to any charge of academic dishonesty.
Infractions will result, at a minimum, in a zero for the assignment and
can result in expulsion from the university.