Master of Science in Data Science and Business Analytics
Analytics is a fast-growing STEM field with a high demand for individuals who possess the skills and expertise necessary to navigate the process of transforming data into insight for making sound business decisions. It's the reason that the Wayne State University College of Engineering and the Mike Ilitch School of Business launched an innovative and interdisciplinary master's program in data science and business analytics. Leaders in this field use data to fundamentally rethink all facets of business in many sectors, including manufacturing, supply chain, finance, and healthcare.
You'll learn from nationally renowned faculty experts and benefit from our high impact practices of student success: team-based learning, global perspective, undergraduate research, internships and co-ops, and community service.
The Master of Science in Data Science and Business Analytics (MSDSBA) is a novel interdisciplinary degree program that leverages the strengths of Wayne State in statistics, operations research, computing, and business by combining the world-class expertise of the College of Engineering and the Mike Ilitch School of Business. This program can be completed in one year by students committed nearly full-time to the program and two years for part-time students. By blending the strengths of these nationally ranked programs, graduates will learn to integrate skills in a unique and interdisciplinary way that yields deep insights into data science and business analytics problems. Concentrations are available in data-driven business, advanced analytics, and data computing.
Why an interdisciplinary master's in data science and business analytics?
Data science and business analytics is a relevant, fast-growing field that has quickly become an essential facet of business strategy. There is an increasing need for data science and analytics-savvy employees who can think uniquely across disciplines to transform data into relevant insights for making better business decisions.
Wayne State's interdisciplinary approach to data science allows students to learn directly from top international authorities on business intelligence, developers of cutting-edge analytics techniques in statistics and operations research, and world leaders in big data and high-performance computing. Students will use advanced resources across campus such as Wayne State's state-of-the-art high-performance computing infrastructure for massive-scale data analytics, work in cross-disciplinary teams to solve real analytics problems for a range of companies and organizations, and more. It all adds up to a unique ability to generate more in-depth insights into data science and analytics problems.
Developed in collaboration with a robust advisory board, MSDSBA graduates will enter the workplace with the computing, business, statistics, and operations research skills needed to immediately identify, analyze and solve analytics problems for better business intelligence and decision support.
Networking and career placement
One of the central objectives of the program will be to produce and place graduates ready to make both immediate and long-term impacts in business, industry, and government. In addition to making contacts with leading analytics organizations during the program, students will be supported in their job search by dedicated professionals.
The curriculum will also facilitate internal connections. To establish a strong professional network within each cohort, students will take several courses together, developing interdisciplinary working relationships and forging connections that can be relied upon throughout their career.
To assist you in making an informed decision about where to pursue your graduate degree in data science and business analytics, the Mike Ilitch School of Business and the College of Engineering host online presentations and on-campus information sessions. Click on the graphic below to register for the event that is most convenient for you. Please check back often as more dates are added regularly.