Analytics Lab

15.572 Analytics Lab

Action Learning seminar on analytics, machine learning and the digital economy

In this seminar, 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.

During its first nine years, A-Lab has attracted a total of 800 students from a dozen MIT departments to work on projects spanning IoT, digital technology, platforms, finance, marketing, e-commerce, retail, manufacturing, medical supply chains, workplace safety, and global health. Project sponsors have included Amazon, Boston Public Schools, Christian Science Monitor, Dell Services, eBay, Gates Foundation, GE Transportation, IBM Watson, LinkedIn, MasterCard, and Nasdaq.

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.

A-Lab 2024 course flyer

The course is open by permission to all MIT graduate students with relevant coursework or experience in analytics, statistics, computer science, management, and economics.

For questions, contact the A-Lab team at a-lab@mit.edu.

Analytics Lab is spearheaded by the MIT Initiative on the Digital Economy (IDE).

Visit the IDE Website

Changing industries with data

A-Lab projects are all about tackling business challenges using data and machine learning in a wide variety of industries, including healthcare, e-commerce, sports, and many others. Below is a sampling of some of the incredible ways that students have used analytics to address crucial issues that companies face.

Past projects 

Challenge: Determine the effectiveness of a leading paint manufacturer’s marketing strategy across South America using thousands of raw unlabeled images of storefront displays.

• SOLUTION: The student team built a ready-to-use, customizable, image recognition tool to measure in-store brand presence. The tool will inform how the company can better allocate its marketing budget and allow decision makers to further explore the effect of visual presence on B2B and B2C sales, product presence, and pricing.

Challenge: Leverage spending data to recommend personalized interest rates of credit card loans from a large retail and financial services group.

• SOLUTION: The team identified predictive variables to pre-emptively detect the transition to loaner behavior and to improve loan conversion efficiency. Given the robust nature of the dataset, the students recommended that the company collaborate with government to identify and assist non-creditworthy individuals susceptible to predatory loans.

Challenge: Programmatically measure the success of and improve a new customer service chatbot.

• SOLUTION: Using natural language processing tools, the team automated the classification of successful and unsuccessful customer interactions and developed a method to identify specific areas where the bot failed. This allows the company to improve bot performance by creating the required volume of data and allowing better training on specific tasks.

The class

Analytics Lab is a graduate-level, fall-only course, open by selective admission to students within Sloan and across other schools at MIT. 

All admitted students have completed relevant coursework drawing from statistics, computer science, management, and economics. Students are proficient in at least one programming language (typically Python or R) and have a strong background in machine learning and predictive analytics.

Student teams consist of three to four students. The course is not open to listeners and in-person attendance at all sessions is mandatory.

Each team is mentored by an MIT IDE-affiliated faculty member, research scientist, postdoc, or PhD student, and teams have regular check-in meetings with their project sponsor across the semester.

Application process

Application for Fall 2024 is due July 17

Students with a background in analytics, statistics, computer science, management, and economics may apply here.

After the initial deadline, applications are considered on a rolling basis and as space allows. 

Students come from a wide variety of programs, including

• MBA
• EMBA
• Sloan Fellows
• MFin
• SDM
• IDM
• LGO
• ORC
• MSMS
• EECS
• Urban Studies
• Required for MBAn

The benefits of becoming a host organization 

Organizations are invited to provide their data, time, and insights to enable student teams to develop actionable solutions and impactful findings that provide value far beyond the fall semester. We encourage interested organizations to take advantage of this opportunity and join in what has proven to be one of the most popular courses among MIT students pursuing careers in data science. There is no fee for proposing a project, but project sponsors are responsible for covering any project-related expenses.

Interested in submitting a proposal? 

In 2023, preliminary proposals are due July 1; final proposals are due August 15.

If your organization would like to submit a proposal for consideration, view the Call for Proposals and contact Devin Cook for additional information.