EMBA 8150: Data Analytics
Syllabus for
Spring 2019
|
Instructor: Dr. Satish
Nargundkar Office Hours: By
appointment |
E-Mail : snargundkar@gmail.com Phone: (678) 644 6838 |
Text:
Predictive Analytics: The Power to Predict Who Will
Click, Buy, Lie, or Die
by Eric Siegel
ISBN-10: 9781119145677
ISBN-13: 978-1119145677
Additional materials will be provided in class or
posted to the class website as needed.
Description
Analytics is now recognized as a major
component of business decision making to gain competitive advantage. Both
qualitative and quantitative decision making will be discussed in this course,
with an emphasis on the quantitative aspect. Students will be introduced to a
framework for using analytics for decision making, and
will work with data in various ways. The course will include application of techniques
for Decision Analysis, Time Series and Causal Models for Forecasting, and
Optimization.
Learning Objectives:
Upon successful completion of
the course, you 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. Perform univariate analysis to examine and describe
the data.
5. Create a predictive model using Multiple Regression
Analysis.
6. Classify observations using Linear and/or Logistic
Regression Techniques.
7. Evaluate the effectiveness and validity of models
using R-square, Standard Error, K-S test measures.
8. Analyze data over time – apply Time Series Forecasting
methods when necessary.
9. Formulate problems for optimization with constraints, and solve using Excel solver.
10. Interpret the results of all mathematical analyses in
the context of the business.
11. Write reports in translate the mathematical analysis
into plain business language for converting the results into recommendations
for decision making and implementation.
Methods of
Instruction:
The course will combine lectures, cases and
discussion. Assignments and a team project will be required to demonstrate the
ability to apply the concepts learned in class.
Grading:
|
|
|
|
Course Average |
Grade |
Course Average |
Grade |
|
Assignments (2) |
20% |
|
94-96, 97+ |
A, A+ |
77-79 |
C+ |
|
Quizzes
(4) |
60% |
|
90-93 |
A- |
73-76 |
C |
|
Team Project (1) |
20% |
|
87-89 |
B+ |
70-72 |
C- |
|
|
|
|
83-86 |
B |
60-69 |
D |
|
|
|
|
|
|
|
|
|
Total |
100% |
|
80-82 |
B- |
Less than 60 |
F |
General Policies:
1. Students are expected to arrive on time and
participate in class discussions. As the instructor, I am responsible for
coming to class prepared.
2. Turn off cell phones, pagers, stereos, TVs, etc. when
in class. Treat the instructor and each other with courtesy.
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:
Ř plagiarism (see course and GSU website for specific
examples of what constitutes plagiarism),
Ř cheating on examinations,
Ř unauthorized collaboration with others
falsification of materials,
Ř 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.