Program Director: James Stamey Associate Dean for Graduate Programs: Patsy Norman
This degree is intended to prepare students for careers as professional business analysts by:
- Learning the fundamentals of information technology and statistics
- Learning tools to understand and visualize data
- Learning fundamental skills in modeling and analysis of multivariate data
- Learning tools for predictive data analysis and forecasting
- Improving programming skills to the professional level for data analytics
- Providing a framework to examine ethical implications of collecting and managing big data.
The MSBA program is a STEM-designated 36 or 37 credit-hour degree that can be completed in one calendar year.
Students will have to submit a completed application, transcript for any degrees completed from an accredited institution in the US or proof of equivalent training at a foreign university, current resume, three letters of recommendation, and for those with less than four years of work experience, an acceptable score on the GMAT or GRE. Foreign national applicants are required to provide an acceptable score from the TOEFL, IELTS, or PTE Academic test. All applicants will need to demonstrate proficiency in Python and have completed at one course in statistics/QBA.
Students will complete 27 required hours and 9 elective hours selected from content areas for a total of 36 hours.
|MIS 5322||Advanced Python for Analytics||3|
|MIS 5340||Database Management Systems||3|
|MIS 5342||Business Intelligence||3|
|MIS 5343||Seminar in Data Visualization||3|
|MIS 5390||Ethics in Data Analytics||3|
|STA 5300||Statistical Methods (Summer)||3|
|STA 5384||Multivariate Statistical Methods||3|
|STA 5V85||Practice in Statistics||3|
|STA 5303||Applied Regression Analysis||3|
|Select three courses from the following||9|
|Advanced Object-Oriented Development|
|Econometric Theory and Methods|
|Data Science I|
|Data Science II|
Advanced Causal Inference
|Causal Inference and Research Design|
|Statistical Machine Learning|
|Time Series Analysis|
|Computational Statistical Methods|
SAS Programming for Data Analytics
|Methods in Data Mining and Management|