The MS Analytics curriculum is structured to be completed in a single year (fall, spring, and summer), with a total of 36 credit-hours required for each student. Trained by world-class faculty, students will learn identification and framing of problems; acquisition, management, and utilization of large and fast-moving streams of data; creation, analysis, solution, and interpretation of mathematical models using appropriate methodology; and the integration of these interdisciplinary skills to enable graduates to successfully develop and execute analytics projects.
The interdisciplinary core includes 15 hours of coursework across business, computing, statistics, and operations research. On top of this integrated breadth of study covering the core areas of analytics, each student has 15 hours of electives to satisfy one of the specialized tracks to give them depth in an analytics area of specialization: Analytical Tools, Business Analytics, and Computational Data Analytics. Each student's elective choices can be personalized to support their individual career goals. The final piece of the curriculum is a required applied analytics practicum, in which each student works with a company or organization on a real analytics problem.
To see the specific list of topics covered in the interdisciplinary core and electives, see the Topics Covered page.
- MGT 8803 Big Data Analytics in Business (3 hours)
- CSE 6242 Data and Visual Analytics (3 hours)
- One operations research course (3 hours)
- Two statistics courses (6 hours)
- 5 Elective/core courses (15 hours; includes 3 introductory core courses in computing, business, and statistics/OR areas, each of which may be waived depending on individual student backgrounds; most students are expected to need 2, leaving 3 remaining elective course slots)
- 6 hours of applied analytics team practicum or internship
- Each student’s course choices must satisfy the requirements of at least one of the defined tracks (Analytical Tools, Business Analytics, Computational Data Analytics).
MS Analytics Track Options
The Analytical Tools track provides students with a greater knowledge and understanding of the quantitative methodology of descriptive, predictive, and prescriptive analytics: how to select, build, solve, and analyze models using methodology such as parametric and non-parametric statistics, regression, forecasting, data mining, machine learning, optimization, stochastics, and simulation.
The Business Analytics track provides students with a deeper understanding of the practice of using analytics in business and industry: how to understand, frame, and solve problems in marketing, operations, finance, management of information technology, human resources, and accounting in order to develop and execute analytics projects within businesses.
Computational Data Analytics
The Computational Data Analytics track provides students with a deeper understanding of the practice of dealing with so-called “big data”: how to acquire, preprocess, store, manage, analyze, and visualize data arriving at high volume, velocity, and variety.
Capstone Experience: Applied Analytics Practicum
At the conclusion of the program, in the summer semester, each student will complete a 6-credit-hour applied analytics practicum. For the practicum courses, cross-disciplinary teams of students will work with companies and organizations on real analytics projects. Teams will consist of MS Analytics students from each track, to bring each of their specializations to bear in an integrated solution, and the teams will be advised by appropriately-selected faculty in each of the disciplines. In this way, the interdisciplinary learning will be emphasized in practice as well as in the classroom.
It is expected that some students may want to pursue an applied analytics internship in place of the practicum course. When such cases are approved, the internship would substitute for the applied analytics practicum requirement.