Informed business decisions rely on significant and current data to help executives make decisions. A business analytics master's degree gives you the skills and training you need to make these choices. Because data is prolific and continual, business analytics professionals who know how to interpret and use that data are in high demand.
When data is accurately and efficiently understood with an eye for business needs and technology-driven platforms, organizational leaders can make strategic decisions based on up-to-the-minute trends and results. Without trained analysts and decision makers who know how to use analytics methods and interpret the results for business results, great opportunities are lost.
WPI’s STEM-designated Master's in Business Analytics provides a solid foundation in business analytics, with applications in a variety of business areas, plus a real-world, team-based project, which ensures that students are prepared to be successful in a data-driven, technological business environment.
MS in Business Analytics Information Request
This program is currently offered full-time and part-time on campus. If you are interested in pursuing the program part-time online when available, join the mailing list to be notified of the start.
The Business Analytics MS is a 33-credit-hours program including applied analytics courses that address business problems and include a realistic capstone project. Courses in this professional MS in Business Analytics degree program do not provide options related to a research degree (e.g., no provision for research credits, qualifying exams, thesis option, or research seminars).
Requirements for the MS in Business Analytics (MSBA)
Business analytics MS students must complete a three-course core, as follows:
MIS 584 Business Intelligence
OIE 552 Modeling and Optimizing Processes
MIS 502 Data Management for Analytics
Business analytics MS students must complete two three-course specialties, selected from the following specialties:
Specialty in Advanced Business Analytics Methods
MIS 587 Business Applications in Machine Learning
OIE 559 Optimization for Business Analytics
MKT 562 Marketing Research
Specialty in Marketing Analytics
Select one of the following two courses (the other two courses are required):
MKT 500 Marketing Strategy (recommended for students with no marketing background)
MKT 561 Consumer Behavior and Analytics
MKT 565 Digital Marketing
MKT 568 Data Mining Business Applications
Specialty in Operations Analytics
Select three of the following courses:
OIE 501 Designing Operations for Competitive Advantage (recommended for students with no operations background)
OIE 542 Risk Management and Decision Analysis
OIE 544 Supply Chain Analysis and Design
OIE 548 Performance Analytics
OIE 558 Designing and Managing Lean Six Sigma Processes
Specialty in Financial Analytics
FIN 500 Financial Information and Management
FIN 522 Financial Institutions, Markets, and Technology
Note: Additional specialties are likely to be developed in the future.
Business analytics MS students must complete a two-course capstone project experience, as follows:
OBC 505 Teaming and Organizing for Innovation
BUS 596 Master of Science Capstone Project
Additional Recommendation
On-campus international students are encouraged to complete up to three additional credits of internship to ensure their readiness for employment in the U.S.
Tuition for the master's in business analytics program is based on the current curriculum for the MS program of 33 credits. The current price of tuition is $1,566 per credit making the total cost for the program $51,678 in tuition only (AY 2019–20). For more detailed information on tuition—including fees—see the complete breakdown of costs on the Financial Aid page.
Are you ready to elevate your career in business analytics? Pursuing a business analytics masters is a step in the right direction. As a MS business analytics student, you will learn advanced business analytics methods in market research, machine learning, and more. A career in business analytics is more than just analyzing and collecting data. You will gain the expertise to make data-driven decisions that can render real organizational impact.
I enjoy teaching at WPI because students bring a lot of energy and enthusiasm to their learning, and I am often inspired by their optimism. My teaching also reflects topics that motivate me: detailed process design in our Industrial Engineering program, improving healthcare delivery as part of the Health Systems Innovation certificate, and exploring the implications of operational excellence in our MBA program.
Dr. Kordzadeh joined the faculty of the Foisie Business School at WPI in July 2017. He received his Ph.D. in Business Administration with an emphasis on Information Technology from the University of Texas at San Antonio. His research interests include health informatics, social media, information privacy, and business intelligence and analytics.
Purvi has 'done' marketing and now she 'teaches' marketing at the WPI Foisie Business School. She has a diverse industry experience in the marketing/brand management function of mid-size to large corporations across consumer goods, retail, and real estate verticals. In academics, she aspires to play her role as a researcher (knowledge creation) and teacher (knowledge dissemination). Her research questions, in the context of discovery, attempt to explain and understand interesting and challenging marketing phenomena that have not received much research attention.
WPI provides an environment that values both teaching and research, which is ideal for me. I enjoy teaching at WPI because students are interested in learning and willing to work hard. My teaching focuses on how business, healthcare, and nonprofit organizations can best use computing technologies, such as database systems, electronic health records systems, and mobile apps. Students in my classes learn to design computing applications that meet the needs of organizations.
Decision-making is becoming increasingly complex as data expands and resources decrease. My research centers on using prescriptive (integer optimization) and predictive (machine learning) analytics, together with algorithms, to effectively allocate scarce resources. My work employs mathematical modeling and the development of methods and tools to benefit vulnerable and marginalized individuals, groups, and populations.
