Slippery Rock University of Pennsylvania offers an online Master of Science in Data Analytics degree program that targets the quantitative modeling techniques and information technologies used to collect, format, synthesize, and interpret big datasets in a business environment. The program addresses an array of proficiencies in areas like statistical computing, time-series forecasting, and regression methods. It requires the completion of 33 credits of coursework, which can be done by full-time students in 10 months. Students who opt for part-time enrollment can expect to complete the degree in approximately two years. The program is 100% online and does not require any campus visits.
The online courses at Slippery Rock University utilize asynchronous instruction. This means that students have on-demand access to recorded video lectures and other course materials 24/7. Peer-to-peer interaction is encouraged through the use of Google hangouts, online discussion forums, and course-related blogs. In addition, students can communicate with classmates and instructors via email.
The Data Analytics curriculum at Slippery Rock University is built around nine core courses. These include: Statistical Methods; Data Mining and Data Analysis; Regression Methods; Optimization Models; Statistical Computing; Advanced Statistical Methods; Big Data Analytics; Model Analysis; and Forecasting and Time Series. Students then enroll in two Data Analytics Capstone courses, during which they choose a real-world data problem to examine and explore in a way that tests their ability to apply the knowledge they’ve learned in the program. Slippery Rock’s MS in Data Analytics is designed to provide students with the necessary training for the Certified Analytics Professional exam, and students who successfully complete the program automatically receive Statistical Analysis System certification from the SAS Institute for advanced analytics.
Slippery Rock University of Pennsylvania is accredited by the Middle States Commission on Higher Education.