MS Analytics Curriculum | LSU Stephenson Department of Entrepreneurship & Information Systems

MS - Analytics



Leah E Whitmire
Associate Director, Enrollment and Academic Services
Phone: 225-578-2961


The MSA curriculum emphasizes the use of advanced data management tools, applied statistics, and operations research techniques. Students use SAS, SQL, R, SPSS, Tableau, and other tools to analyze large real-world data sets to increase return on investment, improve customer retention, reduce fraud, and improve decision making.  As part of the MSA program, students complete a major team project using a large data set provided by a sponsoring organization. Students present their results and recommendations to the sponsoring organization in a formal executive decision briefing. Students with degrees in business, engineering, economics, statistics, mathematics, and the sciences will benefit the most from this degree. 

Key topics include the structured query language (SQL), multivariate statistics, clustering, data-mining, design of experiments‚Äč, optimization methods, and predictive modeling. Teamwork, written and oral communication, presentation skills and state-of-the-art visualization techniques are stressed throughout the curriculum.

Degree Requirements 

  • 36 hours of graduate level course work with a 3.0 average or above
  • Passing grade on the MSA Comprehensive Exam
  • Successful completion of a major project

A typical course list is shown below.

Full-Time Path

Summer Courses Hours
ISDS 7024 Advanced Statistical Analysis 3
ISDS 7302 Data Mining


ISDS 7070 Python for Analytics


ISDS 7510 Database Management with SQL and R





Fall Courses Hours
ISDS 7103 Operations Research


ISDS 4070 Data Mining Tools


ISDS 7075 Business Forecasting and Applications


ISDS 7990 Practicum Project 3



Spring Courses Hours
ISDS 7511 Business Intelligence 3
ISDS 7990 Practicum Project


ISDS elective Approved Elective




Practicum Project

The practicum project is a team-based effort in which students work with leading organizations to solve real analytics problems. 

  • Teams include two-three students and one faculty member.
  • The teams work to understand the business problem and then clean and analyze the data.
  • The projects begin in the fall and end in the spring with a presentation to the sponsoring organization.
  • Past projects included the following industries: automotive, energy, government, healthcare, higher education, sports, and telecommunications.


Recommended electives:

  • ISDS 7220 - Supply Chain Management
  • ISDS 7401 - Healthcare Informatics
  • ISDS 4118 - Web Analytics

Other Approved Electives:

  • CSC 4740 - Big Data Tech
  • ECON 4633 - Time Series
  • EXST 7036 - Categorical Data Analysis
  • EXST 7152 - Advanced Topics in Statistical Modeling
  • ISDS 7070 - Special Topic/Project (topics vary; permission of department)

Application Deadlines

Term Apply By: Classes Start
Summer 2021

International Students: December 15

Domestic Students: April 15

May 24, 2021



Module/Term Apply By Classes Start
Spring 1 2021 December 14, 2020 January 11, 2021
Spring 2 2021 March 1, 2021 March 15, 2021
Summer 1 2021 May 10, 2021 May 24, 2021
Summer 2 2021 June 21, 2021 July 5, 2021
Fall 1 2021 August 9, 2021 August 23, 2021
Fall 2 2021 October 4, 2021 October 18, 2021

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