This program will begin enrolling students in Fall 2017 pending final approvals by the Higher Learning Commission (HLC) and the West Virginia Higher Education Policy Commission (WV-HEPC).
The Master of Science, Data Analytics and Information Systems is a 36 credit-hour program that is interdisciplinary in nature, and is designed for students to utilize their understanding of data in in the field of data analytics and in the computer-oriented study of information systems. The program develops students who have advanced capacity to derive knowledge from data, communicate an understanding of that knowledge, and to develop and manage computer information systems that are used in data analytics and other supporting areas. These include skills in data collection, preparation (‘munging’), representation using mathematics, data analysis using custom or developed software, as well as storage and retrieval. This process involves selection of and processing with appropriate methods, development and analysis of algorithms in a modern distributed and cloud-based computing environment, and implementation in computer programming languages.
All students in the M.S., Data Analytics and Information Systems program take a 12-credit Data Science core and a 12-credit Information Systems Requirements core. The remaining 12-credit electives and special topics courses provide students with opportunities to conduct research projects with their mentoring professors or internship projects to gain real-world experiences.
Program outcomes include the following:
- Integrate components of data analytics to produce knowledge-based solutions for real-world challenges using public and private data sources.
- Evaluate data management methods and technologies used to improve integrated use of data within the framework on information sciences.
- Construct data files using advanced statistical and data programming techniques to solve practical problems in data analytics.
- Develop team skills to ethically research, develop, and evaluate analytic solutions to improve organizational performance.
- Evaluate machine learning methods and strategies for advanced data mining.