- 15.567 - The Economics of Information: Strategy, Structure, and Pricing
- 15.575 - Economics of Information and Technology in Markets and Organization
- 15.572 - Analytics Lab (A-Lab)
Instructors: Sinan Aral, Erik Brynjolfsson
Course Coordinator: Susan Young <syoung @mit edu>
In the MIT Analytics Lab (A-Lab) student teams select and deliver a project using analytics, machine learning, and other methods of analysis to develop results that diagnose, enable, or uncover solutions to real business issues and opportunities.
The course, which runs each fall semester, is spearheaded by the MIT Initiative on the Digital Economy (IDE) and is part of MIT Sloan School of Management’s suite of Action Learning offerings.
During its first five years, A-Lab has attracted a total of 300 students from a dozen MIT programs to work on over ninety projects spanning IoT, digital technology, platforms, finance, e-commerce, retail, manufacturing, medical supply chains, workplace safety, and global health.
Some projects are tightly focused on dilemmas organizations currently face, which requires students to quickly understand particular business circumstances and domains before performing their descriptive, predictive, or causal analysis. Other projects are more open-ended, and students must think entrepreneurially about how to bring new value to existing data and suggest frontiers for future business opportunity.
The course culminates with presentations of final project results to an audience including experts, entrepreneurs and executives.
For Project Sponsors:
Project sourcing for the Fall semseter begins in June. For questions about the course, please contact Susan Young.
Admissions for Analytics Lab 2019 open in early May.
Admission by application only; evaluated on coursework or experience in analytics, statistics, computer science, management, and economics; relevant learning, experience, and motivation toward data analytic work, with extra weight given to data analytic courses taken and to data analytic project and job experience; attention given to a representation of students with technical and computational experience, managerial experience, experience implementing analytical models, and entrepreneurial work using analytics. No bidding necessary.
Fall 2019 Schedule: Thursdays 4:00-5:30pm, plus (extended) project pitch session on Thursday 9/19/19 and final presentation all-day session on Friday 12/13/19. The course is not open to listeners and in-person attendance at all sessions is mandatory.
For questions about the course, please contact Susan Young.
- Teaching-oriented web sites: