Harrisburg University of Science and Technology, M.S. in Analytics

Harrisburg University’s Master of Science (M.S.) in Analytics degree program is designed for students with a strong background in mathematics, computer science, engineering, or economics who are looking to learn the specific techniques and tools involved in analytics and the business skills to apply this knowledge effectively and strategically. Harrisburg’s M.S. in Analytics will provide its students the practical skills and real-world experience that they need to pursue a rewarding career in big data. Supplemented by industry supplied projects, HU graduates will be exceptionally well equipped to harness and communicate the full value of data to the organizations they serve. This Harrisburg University master-of-science degree program is a powerful new entrant into the field of data analytics and is critical to developing of the next wave of analytics professionals who will find themselves well-positioned to launch their careers. HU’s Master-of-Science in Analytics combines mathematical and statistical study with instruction in advanced computational and data analysis. Students learn to identify patterns and trends; interpret and gain insight from vast quantities of structured and unstructured data; and communicate their findings in practical, useful terms. The program is designed for students with a strong background in mathematics, computer science, engineering or economics who seek the specific techniques and tools involved in analytics and the business skills to apply this knowledge effectively and strategically. All three areas of data analysis are studied: predictive (forecasting), descriptive (business intelligence and data mining), and prescriptive (optimization and simulation). HU’s comprehensive curriculum provides a broad perspective, teaching the skills necessary to be highly effective in solving real-world business challenges. Students learn that simple data analysis can be misleading, that large-scale problems are not amenable to naive solutions, and that working with unstructured field data is different than working with the results of a design experiment.