Capstone Projects

The MS in Data Analytics Engineering is designed to provide students with an understanding of the technologies and methodologies necessary for data-driven decision-making. This course provides an environment within which MS students can apply learning and demonstrate mastery of the data analytics problem identification and conversion to results by integrating tools, methods, experience, and data to deliver and professionally communicate -- results, insights and value. In preparation for this course \f1\endash\f0 and as part of the applied seminar capstone format -- students study topics such as data mining, information technology, statistical models, predictive analytics, optimization, risk analysis, and data visualization. The capstone projects will be selected jointly by student teams and faculty mentors both for technical merit and to meet student goals become data scientists and analysts in finance, marketing, operations, business intelligence and other information intensive groups generating and consuming large amounts of data. The focus of the degree is on technologies and methodologies of data analytics problem solving, especially as applied to problems in areas of subject matter expertise both within the Volgenau School and across other GMU academic units collaborating in the DAEN multi-disciplinary program.