Analytics Lab: Action Learning Seminar on Analytics, Machine Learning and the Digital Economy (15.572, Fall 2019, 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 Erik Brynjolfsson and Sinan Aral.
During its first five years, A-Lab has attracted a total of 300 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.
Admission for Fall 2019 is now closed.
Note: Sloan Fellows application period: June 21-July 8.
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.
Fall 2019 Schedule: Thursdays 4:00-5:30pm, plus (extended) project pitch session on September 19 and final presentation session on December 13. 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.
For Project Sponsors
Project sourcing for the Fall semseter begins in June. For questions about the course, please contact Susan Young.
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