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

MS - Analytics

 

 

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Curriculum

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

3

ISDS 7070 Python for Analytics

3

Total

9

 

Fall Courses Hours
ISDS 7510 Database Management with SQL and R 3
ISDS 7103 Operations Research

3

ISDS 4070 Data Mining Tools

3

ISDS 7075 Business Forecasting and Applications

3

ISDS 7990 Practicum Project 3
Total

15

 

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

3

ISDS elective Approved Elective

6

Total

12

 

Part-Time Recommended Path

Students may pursue the MSA program on a part-time basis if they are accepted into the program, recognizing that the part-time student must follow the MSA full-time course scheduling.

First Summer Courses Hours
ISDS 7024 Advanced Statistical Analysis 3
ISDS 7302 Data Mining (or to be taken in second summer)

3

Total

3 or 6

 

First Fall Courses Hours
ISDS 7510 Database Management with SQL and R 3
ISDS 7103 Operations Research

3

Total

6

 

First Spring Courses Hours
ISDS 7511 Business Intelligence 3
ISDS elective Approved Elective

3

Total

6

 

Second Summer Courses Hours
ISDS 7070 Python for Analytics 3
ISDS 7302 Data Mining (or to be taken in first summer)

3

Total

3 or 6

 

Second Fall Courses Hours
ISDS 4070 Data Mining Tools 3
ISDS 7075 Business Forecasting and Applications

3

Total

6

 

Second Spring Courses Hours
ISDS 7990 Practicum Project 3
ISDS elective Approved Elective

3

Total

6

 

Third Summer Courses Hours
ISDS 7990 Practicum Project 3
Total

3

 

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.

Electives

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)

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