Math and the Mouse

Program: Furman University MayX
Location: Walt Disney World, Orlando
Duration: 3 weeks
Faculty: Dr. John Harris, Dr. Kevin Hutson, Dr. Liz Bouzarth

Summary

Math and the Mouse is a three-week MayX course at Furman University held at Walt Disney World. The program explores how applied mathematics shows up in real park operations: ticket pricing, guest experience, workforce scheduling, ride throughput, and data-driven decision making.

What stood out most was how realistic the weekly projects felt. Each assignment was not a textbook exercise—it mirrored problems Disney teams actually work on every day: where to position mobile food carts, how to staff a restaurant across a long operating day, and how loading choices affect wait times and ride capacity. Students work on group projects, meet Disney professionals, and document the process through public blog posts and private reflective journals.

Course Topics

1. Data Analytics

Disney uses analytics across ticket pricing, custom experiences, and survey data. The course introduces simple ways to gather and analyze data, including clustering, classification, and similarity. Experiences include meetings with Disney department representatives and the Keys to the Kingdom behind-the-scenes tour at Magic Kingdom.

First project: K-means clustering to determine where to route a mobile Mickey Bar stand throughout the day in Magic Kingdom—the same kind of placement question operations teams weigh when deciding how to reach guests across a busy park.

2. Decision Modeling and Optimization

Optimization models real-world scenarios and searches for the best feasible solution. Topics include resource allocation, workforce scheduling (such as assigning characters to locations), network flow (modeling tours of the park), and transportation/assignment models. The course also covers linear optimization techniques and heuristic approaches for harder models.

Second project: Workforce scheduling with constraints similar to those Disney faces when assigning cast members to daily jobs—covering demand across long shifts, breaks, and changing guest volume, much like staffing decisions made behind the scenes at resorts and restaurants.

Guest speaker: Len Testa, author of The Unofficial Guide to Walt Disney World and founder of touringplans.com, which generates optimal touring plans given constraints like arrival time, lunch preferences, and chosen attractions. I also get to meet professionals from Disney World from different departments to learn more about their jobs and career paths.

Knapsack challenge: Early in the course, we worked through a small knapsack problem framed around the park—choosing which experiences to fit into limited time and capacity, each with a “value” and a cost. It was a compact way to see how optimization shows up in everyday Disney planning: you cannot do everything, so the question becomes what to prioritize under real constraints. That exercise connected cleanly to the larger touring-plan ideas we discussed later.

3. Incorporating Uncertainty

Probability and statistics help analyze processes with randomness. The course covers binomial, geometric, and normal distributions; hypothesis testing (for example, whether Space Mountain wait times changed after Tron opened); and simulation or agent-based modeling for complex guest-behavior scenarios.

Third project: Model wait-time buildup at Tron and Big Thunder Mountain during the first hour of park operations—a throughput question that connects directly to how Disney monitors lines, capacity, and guest flow when the parks open.

4. Final Project

Students form groups of three or four, design a research question, gather data, and perform analyses to reach a conclusion based on processes and experiences observed throughout the course.

Posts

These weekly notes connect course topics to specific projects and reflections. Each one tackles an operational question Disney faces in practice—not a simplified classroom version of the problem.

Presentations & Professional Growth

Throughout the course, each project included a presentation component, often to Disney professionals as well as faculty and peers. Because the weekly work addressed problems Disney actually tries to solve every day, presenting felt less like a class demo and more like sharing a plausible operational recommendation. That repeated practice shaped my presentation skills and confidence in ways that coursework alone rarely does.

Tools and Technical Skills

Working through real datasets and project deliverables strengthened my Excel and Tableau skills. I used spreadsheets for modeling and analysis, and Tableau to make patterns in the data easier to explain in a live setting.

Feedback and Storytelling

The professors gave consistently constructive feedback on how we framed results, not just whether the math was correct. That pushed me to improve storytelling and presentation significantly: clearer structure, stronger visuals, and language that matched who was in the room.

Audience-Aware Presenting

By the end of the program, I felt much more comfortable presenting in front of people and more deliberate about what to emphasize for a given audience. For example, when presenting to Disney stakeholders with strong math backgrounds, we leaned further into the models and analytical depth behind our work. We still closed by positioning ourselves as a business partner—evaluating outcomes, trade-offs, and practical recommendations rather than stopping at the technical result.

That balance, technical rigor when the audience can use it, business framing when decisions matter, is something I carried out of the course and into how I think about communicating data work.