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USU Master of Data Analytics (MDATA)

MDATA Students reviewing data files

The USU Master of Data Analytics (MDATA) program trains the next generation of statisticians, business analysts, and computer scientists to meet the increased demand for analytic professionals. The program integrates coursework in Statistics, Management Information Systems, Economics & Finance, and Computer Science. An MDATA degree gives graduates a broad but focused set of knowledge and tools applicable to data management and analysis for use in a variety of organizations.

Management Information Systems and Economics and Finance Departments, in the Huntsman School of Business, and the Department of Mathematics & Statistics, in the College of Science, have created a program to produce graduates with the skills and competencies needed to support effective decision making in this era of big data.

MDATA Specialization Options

Management Information Systems

MIS track students are experts in data management and analysis, including data warehousing, visualization, and machine learning. Their analysis drives business decision-making with a forward-thinking focus: “What’s next?” and “What should we do?” Using predictive techniques, they provide operational and strategic direction to executives.

Economics and Finance

Economics and Finance track students are experts in applying data analysis for decision making in all areas of applied finance which includes asset pricing, portfolio analysis, risk management, and trading.

Students learn to apply:

  • Monte Carlo simulation for derivative pricing and back-testing trading strategies
  • Financial econometrics for volatility modeling and asset price prediction
  • Financial computing in the Python, R, and SAS programming languages

MDATA Economics and Finance track students are prepared for some of the most attractive jobs in modern quantitative economics and finance.


The Statistics specialization is taught by faculty with expertise in areas that are critical for modern analytics -- such as statistical computing and programming, visualization, high-dimensional data analysis, classification and neural networks, and data dredging and management.