Broaden your analytics skillset through a semester-long experiential learning project and work with actual companies using real-world datasets
Benefits
Important Dates
Students must be able to commit a minimum of 10 hours a week for 8 weeks.
Fall 2026 Update
Applications for the Fall 2026 AI & Analytics Accelerator are now open!
Fall 2026 AI & Analytics Accelerator Projects
Building Context for AI Agents
Project Details
Accenture aims to identify and compare effective approaches for capturing tacit, contextual knowledge embedded in organizational work processes, and evaluate whether the captured context is sufficient to support effective AI agent deployment.
Agentic Products for Travel
Project Details
Expedia Group is seeking to develop a multimodal evaluator agent for travel-focused agentic products. They will assess the quality, accuracy, relevance, and presentation of both image and text outputs.
Leveraging Member Data
Project Details
Frieda Partners will work with a student team on a data initiative involving a founding cohort of the country's most exclusive private clubs. For the first time, these clubs are opening years of member behavioral data to outside researchers, offering access to closed populations with minimal churn and clearly observable outcomes.
Science-Based Reduction in Impaired Driving
Project Details
Heineken is developing a science-based approach to reducing drinking and driving through targeted behavioral interventions. They also seek to design and evaluate interventions to identify strategies with the greatest potential for impact.
Evaluating Investments in Enterprise AI
Project Details
RxSense looks to develop a forecasting model to determine the optimal investment in enterprise AI tools. They will use trial data to identify an approach that minimizes inefficiency while maximizing adoption and ROI.
AI-Driven Decision Support Framework
Project Details
Spencer will develop an AI-driven decision-support framework that uses retail store t-shirt data to connect planogram design to measurable business outcomes. The team aims to quantify the relationship between space allocation, planogram design, and business performance, with all measures evaluated against t-shirt sales, productivity, and conversion metrics.
How it Works
Who Is Eligible?
Penn and Wharton undergraduate and graduate students who show a demonstrated interest in analytics and possess relevant skills ranging from project management, to client relations, to technical expertise. Technical skills are not required but are a plus.
Apply
Submit your application through a highly competitive selection process to become a Wharton Analytics Fellow and get assigned to a company based on your skillset and their needs.
Launch
Kick off the project, meet with your assigned company, and discuss their real-world business challenge.
Analyze
Working closely with a Wharton mentor, analyze the dataset using programming languages and tools of your choice (i.e., Python, SQL) and create a statistical model that helps to solve the business challenge.
Present
Share your findings and recommendations with your company at the AI & Analytics Accelerator Summit.
Role Requirements
Business Lead
Business Leads are responsible for leading and managing the project team, coordinating with clients, and managing the finer details of the engagement. They are usually MBAs or upperclassmen and often have extensive work and leadership experience in business and consulting.
Technical Lead
Technical Leads are data science experts with high-level programming skills who are responsible for the technical aspects of the projects. They build the most complex models and mentor analysts throughout the project. Technical Leads are usually PhD or MSE students with extensive industry experience.
Senior Analyst
Senior Analysts are top undergraduates with advanced skills in programming, statistics, and modeling. They usually have high-level coursework in the CIS and STAT departments under their belt in addition to significant internship and project experience.
Junior Analyst
Junior Analysts typically have intermediate data science skills at roughly the level of STAT 102, STAT 477, and/or WUDAC’s Analytics 101 and 201 courses. They are notable for their willingness to learn and work hard, and many of them progress into leadership roles within WAF in later semesters.
“Having the opportunity to work with real datasets, I was able to gain a deeper understanding of many of the statistical concepts I had learned in my coursework. After working on three AI & Analytics Accelerators, I feel confident in my ability to tackle a real-world, unstructured, data science problem.”
– Ashley Clarke, W’23
AI & Analytics Accelerator Case Studies
Current & Previous Partners
Questions? Contact Us
