Business Analytics MBA Track

At the intersection of business and technology, business analytics reflects a longstanding, core strength of the Tepper School of Business.

The technological advances of the last decades have impacted our world, and the business world in particular, in fundamental ways. Massive amounts of data are being gathered and stored, from individual medical records to every single truck movement via GPS for large logistics providers. 

Moreover, ever faster computers and optimization methods have become available to transform this data into information for better decision making. All of this makes it now possible to apply advanced analytical methods to business problems that were impossible 10 or 15 years ago — ranging from detailed supply chain optimization to health care applications and the service industry.

The over-arching methodology that refers to the skills and technologies to explore past business performance to make better decisions is called Business Analytics (BA). BA uses data, statistical and quantitative analysis, predictive modeling, and optimization to make businesses work better. 

There has been extensive popular interest in BA over the past few years, including popular business press books like Competing on Analytics and Predictive Analytics. This reflects the significant business interest in translating analytics into business advantage. Employment forecasts predict a huge growth for managers that understand analytics to lead big data initiatives in the U.S. over the next decade. Our graduates have accepted jobs from companies such as Microsoft, Amazon, Johnson & Johnson and Bank of America, and top consulting firms like Deloitte, IBM and McKinsey, that are leading efforts to bring analytics to the forefront in business. 

Being at the intersection of business and technology, business analytics is at the heart of the Tepper School. It practically defines our approach to operations research and information systems, and also finds application in marketing, operation management and other fields.


Who Should Apply

This track is appropriate for MBA students interested in transforming large amounts of data into better decisions. Possible careers include consulting in data-rich environments, analytical marketing, information technology and financial data analysis. 

Students interested in the track should have a strong interest in analytical approaches to management, as shown by aptitude in courses such as Optimization, Probability and Statistics, and Statistical Decision Making.


Business Analytics Track Courses

Business Analytics track students must take three required courses and complete the capstone project. In addition, students must complete at least five additional electives. Students must take at least one course from the descriptive area, two from the predictive area, two from prescriptive and one from an application area. Two additional electives may come from any area of our MBA curriculum.

Download an example overview of the Business Analytics track curriculum (pdf) to learn more. Faculty may revise the curriculum and course offerings at any time.


Business Analytics Capstone Course

Students choosing a track complete their required capstone course as part of the track. The Business Analytics capstone course comprises a project which exposes students to a real business problem which they will solve using visualization, data mining and optimization techniques. The problems are solicited from businesses, although students are welcome to propose projects as well. Past projects have worked with financial services organizations, technology companies, retailers, marketers, manufacturers and distribution services to solve these types of problems:

  • Construction of a pricing strategy using marketing transaction data
  • Creating a customer loyalty program that monitors customer response to marketing efforts
  • Optimizing a delivery distribution network
  • Planning a new distribution channel or production system
  • Customization of promotional strategies to a micro-market level
  • Design a decision support system to aid managers in using analytical models

Faculty Coordinator

Willem-Jan van Hoeve
Carnegie Bosch Associate Professor of Operations Research
vanhoeve@andrew.cmu.edu