EMBA 8150: Data Analytics

Syllabus for Spring 2020


Class Schedule



Instructor: Dr. Satish Nargundkar 
Office: 727 College of Business 

Office Hours:  By appointment 

E-Mail : snargundkar@gmail.com  

Phone: (678) 644 6838  



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.



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.







Course Average


Course Average


Assignments   (2)



94-96, 97+

A, A+



Quizzes (4)







Team Project (1)


























Less than 60




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 Georgia State. Upon completing the course, please take the time to fill out the online course evaluation.


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.