Analytics Lab

The MIT Analytics Lab (A-Lab) is the IDE's flagship educational offering. In this graduate seminar, student teams engage with project host companies to 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.

A-Lab is a graduate level seminar course, spearheaded by the 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.

During its first eight years, A-Lab has attracted a total of 700 students from a dozen MIT programs to work on 180 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 Project Host Companies

Propose Projects for A-Lab

We invite organizations from all sectors to participate as A-Lab Project Hosts. We are looking for projects that fit in a variety of industries and sectors; address a diversity of problem types; require advanced depth of analysis; and have ensured availability of clean and rich data at the outset of the project. We also seek to closely match the students’ capabilities and variety of skills, experience, and interests.

If you are interested in hosting a project and submitting a proposal for the 2023-2024 academic year, please reach out to us at a-lab@mit.edu.

View the 2023 Analytics Lab Call for Proposals

View a Sample Project Proposal

Submit a Proposal

Submit an A-Lab Proposal

Organizations are invited to propose projects and become project hosts.

Submit a Proposal

For Students

Apply to A-Lab

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 2023.

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.

More Details from Sloan Action Learning

Students Apply Here

Students: Apply to A-Lab

Student applications are due by July 17th.

Student Application

Past Projects

- 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
- 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
- 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
- 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
- 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

Read About A-Lab

News 2022 Analytics Lab Winners Announced December 5, 2022

Blog A-Teams Deliver Data-driven Results January 7, 2022

Blog Fighting Fires with Analytics: A-Lab Teams Up With Dallas FD February 10, 2020

Blog A-Lab Winners Put Analytics Front and Center January 11, 2019

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Analytics Lab

Propose Projects for A-Lab

Submit an A-Lab Proposal

Apply to A-Lab

Students: Apply to A-Lab

Past Projects

Read About A-Lab

Analytics Lab

Propose Projects for A-Lab

Submit an A-Lab Proposal

Apply to A-Lab

Students: Apply to A-Lab

Past Projects

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

Understanding Successful eBay Sale Prices (eBay)

Predicting Hospital Readmission (Dell Services)

Predictive Maintenance in the Elevator and Escalator Industry (Schindler)

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

Read About A-Lab