Analytics Lab

Analytics Lab: Action Learning Seminar on Analytics, Machine Learning and the Digital Economy (15.572, Fall 2020, 9 Units)

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. The course is led by IDE faculty Sinan Aral and Abdullah Almaatouq.

During its first six years, A-Lab has attracted a total of 400 students from a dozen MIT programs to work on projects spanning IoT, digital technology, platforms, finance, marketing, 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.

For Students

Student Applications are now closed for the Fall 2020 semester.

Admission for MIT students by application only; evaluation based on coursework and experience in analytics, statistics, computer science, management, and economics. No bidding necessary. Students should apply for the course as individuals; student-driven team formation will take place in September. 

The course includes an extended project pitch session and a final presentation event, in addition to normally scheduled class meetings.  The course is not open to listeners and attendance at all sessions is mandatory

Special Events:

Pitch day, September 18, 1:00 - 5:00 pm EST (virtual format)

Final Presentations,  December (More info to come)

For questions about the course, please contact

View 2019 syllabus and posters of select projects.


For Project Sponsors

View the Call for Proposals and Sample Proposal.  To begin a new project proposal application, click here.

For questions about the course, please contact


Fighting Fires with Analytics: A-Lab Teams Up With Dallas FD (2019)

A-Lab Winners Put Analytics Front and Center (2018)

A-Lab Teams Bring their A-Game to Final Presentations (2017)

A-Lab Puts Analytics and Machine Learning into Action (2016)

Data Helps Keep Trains Running (via MIT Action Learning, 2016)

Example Projects

The “Myth of the Crystal Ball”: Understanding Forecasting Errors at Amazon (Amazon)

  • Challenge: Help Amazon quantify the impact of supply chain forecasting errors to better prioritize forecast improvements in the future
  • Data: 75 million rows containing daily demand and forecast data for 206 thousand products over two weeks
  • Analysis: Defined different kinds of costs associated with forecasting errors and their magnitudes. Used statistical methods in R running on a cloud computing system to quantify lost profit due to forecast error
  • Recommendation: Incorporate indirect costs into the evaluation of forecasting errors. Look for variation across product categories

Understanding Successful eBay Sale Prices (eBay)

  • Challenge: Find the factors that best predict successful prices for new and used eBay items in different categories and under a variety of sales conditions
  • Data: 3 months of sales data, totaling over 147 million separate transactions (about 24 gb with some preprocessing required)
  • Analysis: Using machine learning and a “bag-of-words” model, looked into inclusion of special characters and its effect on price, drivers of the difference in prices between new and used items, and price differences between auctions and Buy-It-Now goods
  • Recommendations for further analysis: Define a “feature space” for different goods on eBay, perform seller network analysis, and use timing to better predict prices

Predicting Hospital Readmission (Dell Services)

  • Challenge: Use analytics to find the factors that best predict 30-day hospital readmission
  • Data: 1500 patient admissions at one US hospital, with 26 fields describing each case
  • Analysis: Generated additional features, then used logistic regression, support vector machines, and classification trees to predict readmission
  • Recommendation: Expand analysis to more hospitals and incorporate data from new sources (e.g. wearables) to help reduce readmission risk

Predictive Maintenance in the Elevator and Escalator Industry (Schindler)

  • Challenge: Help Schindler use predictive analytics to revise its maintenance strategy and better perform preventative intervention
  • Data: 1000 elevator-specific files describing elevator operation and maintenance needs
  • Analysis: Used regression techniques to predict potential need for future maintenance and likelihood of service trips for different elevator codes
  • Recommendation: Determine the appropriate priority for elevator maintenance given limited resources. Error codes can be predicted, but potentially more important is efficient allocation of resources

Using Geospatial Data to Develop a New Kind of Football Analytics (Telemetry Sports)

  • Challenge: Use a new source of geospatial NFL data to classify plays, evaluate players, and design football strategy
  • Data: Real NFL game data from selected Indianapolis Colts plays, as well as over 10,000 simulated football plays from EA’s Madden NFL game
  • Analysis: The team used machine learning and regression techniques to identify player positions on the field, isolate player routes in game, classify plays, calculate new measures of “player elusiveness”, and project expected yardage per play
  • Recommendation: Geospatial data offers significant opportunities for evaluating success in sports. This type of analysis would be particularly useful for optimal play selection


2014-2019 Project Sponsors



Analog Devices



The Boston Globe

Boston Public Schools

Burning Glass Technologies


C6 Bank


Capital One

Cell Signaling Technology

Center for Digital Business

Christian Science Monitor


College Pulse

Dell Services 

Deloitte Digital



Evercore ISI



Gates Foundation

GE Transportation

Graduate Management Admission Council

Green Cargo 



HG Data


Interasia Lines

IBM Watson



JPMorgan Chase

Legendary Entertainment

Lineage Logistics



Marathon Data Systems


MIT Sloan


Nomura Research Institute


NXP Semiconductors



Raise Marketplace



Schneider Electric

Stellar Loyalty


Telemetry Sports

Toyota Mobility Foundation and PublicRelay