The M.S. in applied economics and predictive analytics degree is designed to help meet the burgeoning demand for analytics training on the part of business, government and nonprofits. This degree focuses on economic analysis and decision-making along with strong quantitative training, and brings additional skills not typically provided in analytics programs found in statistics, engineering or business programs. “Predictive analytics” refers to the process of building models that predict consumer behaviors under different circumstances and help customize product offerings that better suit the tastes and preferences of consumers; “predictive analytics” also refers to building models to predict time series variables of importance to businesses and governments (e.g., product sales and tax revenues) and to evaluate competing government programs and business strategies. The M.S. is a 30 credit hour degree.
The minimum admission requirements for the M.S. in applied economics and predictive analytics are as follows:
Undergraduate cumulative GPA of at least 3.000 (on a 4.000 scale).
Twelve credit hours of undergraduate economics, including two intermediate theory courses, one in microeconomics and one in macroeconomics.
An introductory course in statistics.
One term of calculus.
Satisfactory GRE graduate school admission test scores if the undergraduate GPA is lower than 3.000.
The M.S. in applied economics and predictive analysis is a 30 credit hour degree.