This past semester I was involved in an interesting course at MIT’s Sloan School of Management, - Analytics Labs (A-Lab). A-Lab’s objective is to teach students how to use data sets and analytics to address real-world business problems. Companies submit project proposals prior to the start of the class, including the business problem to be addressed and the data on which the project will be based. Students are then matched with the project they’re most interested in and grouped into teams of 3-4 students.
A-Lab received over 20 project proposals from different companies, of which 13 were selected by the students. Each project team was assigned a research mentor to provide guidance as appropriate. I mentored a 3-student team that worked on a project sponsored by MasterCard. The students explored the possibility of improving on predictions of the economic performance of emerging markets by coupling existing economic indicators data with consumer behavior based on MasterCard’s transaction data. This is a particularly interesting project because economic data in emerging markets is often not as reliable as the data in more advanced markets.
But more important for the students, the various A-Lab projects served as a concrete learning experience on what data science is all about, - how to leverage messy, incomplete, real-world data to shed light on a complex and not-so-well-defined problem.