All students are required to meet the general university admission requirements.

Additional Documentation
Your application must include the following:

• Statement of objectives: State your academic objectives in one to two pages and include the following information:
  • career and professional goals and aspirations and how being accepted to the program will specifically benefit your goals and aspirations
  • reason for interest in seeking a master’s degree in data analytics
  • evidence of motivation, leadership and strong work ethic
  • professional experiences that support application
  • international students should include a statement that you will be taking all courses through distance education and will not come to the U.S. for any coursework required for this program
• Resume/curriculum vitae
• Unofficial transcripts from each institution attended
  • Failure to list any colleges or universities from which you received a degree or enrolled in graduate coursework may result in dismissal from the university.
  • If your transcripts are not in English, you must furnish a translation by an appropriate authority.
  • You will be asked for official transcripts after notification of admission.

Graduates of the data analytics master’s program may enjoy careers as a:

• data analyst
• business intelligence analyst
• market research analyst
• analytics/management consultant
• operations analyst
• human resources analyst
• product analyst
• predictive analyst
• quantitative analyst
• data scientist
• data engineer
• data visualization specialist

While it is not required, it is highly recommended in order to be successful in the program to have a foundational understanding of math, statistics, and computer programing or coding prior to enrolling in this degree. Additional foundation courses may be taken to help prepare for the requirements of this degree.

Suggested Foundation Courses (not required)

• Math (calculus or above)
• Economics
• Statistics
• Computer programming or coding

Required (21 credits)

• CIS 731 - Programming Techniques for Data Science and Analytics (3 credits)
• ECON 630 - Intro to Econometrics (3 credits)
• IMSE 785 - Big Data Analytics (3 credits)
• MIS 665 - Business Analytics and Data Mining (3 credits)
• MIS 670 - Social Media Analytics and Web Mining (3 credits)
• MIS 830 - Information Technology Strategy and Application (3 credits)
• MKTG 880 - Applied Marketing Analytics (3 credits))

Track Electives (9 credits)

Track 1: Data Science

• CIS 730 - Principles of Artificial Intelligence (3 credits)
• CIS 732 - Machine Learning and Pattern Recognition (3 credits)
• CIS 751 - Computer and Information Security (3 credits)
• CIS 833 - Information Retrieval and Text Mining (3 credits)
• IMSE 680 - Quantitative Problem Solving Techniques (3 credits)
• MATH 725 - The Mathematics of Data and Networks I (3 credits)
• MATH 726 - The Mathematics of Data and Networks II (3 credits)
• STAT 730 - Multivariate Statistical Methods (3 credits)

Track 2: Applied Analytics

• ACCTG 856 - Accounting Analytics (3 credits)
• CIS 732 - Machine Learning and Pattern Recognition (3 credits)
• GENBA 890 - Business Capstone (3 credits)
• GENBA 894 - Data Analytics Capstone (3 credits)
• IMSE 680 - Quantitative Problem Solving Techniques (3 credits)
• MANGT 662 - Procurement, Logistics and Supply Chain Design (3 credits)
• MKTG 881 - Advanced Marketing Analytics (3 credits)
• STAT 730 - Multivariate Statistical Methods (3 credits)