Hacking Your Vacation: Using Data for Fun

To get the perfect Disney hotel room, the final step is sending a fax. Here's the data science that gets you there.

#1about 4 minutes

Introduction to data-driven vacation planning

The core goals for a successful Disney vacation are to avoid crowds, save money, and maximize enjoyment by using a data-driven approach.

#2about 5 minutes

Why planning a Disney vacation is a complex data problem

Manually planning a trip is nearly impossible due to the vast number of variables like park capacity, attendance, ticket costs, and attraction popularity.

#3about 6 minutes

Identifying key data sources for vacation optimization

Effective predictions rely on diverse data sources including school calendars, economic trends, scraped wait times, and user-submitted feedback.

#4about 8 minutes

How to predict park crowds and attraction wait times

A crowd calendar provides a high-level view, but the core predictions are granular wait time curves for every attraction on five-minute increments.

#5about 7 minutes

Creating a step-by-step optimized park itinerary

A live demo shows how to use a web tool to generate an optimized touring plan that minimizes waiting by sequencing attractions intelligently.

#6about 2 minutes

Why a mobile app is crucial for real-time optimization

A static printed plan is fragile, so a mobile app is used to re-optimize the itinerary throughout the day using real-time wait time data.

#7about 6 minutes

Using data to decide if Genie+ is worth the cost

By analyzing time saved versus cost, you can determine the actual value of upcharges like Genie+ and Individual Lightning Lane for different parks and crowd levels.

#8about 7 minutes

Choosing hotels and restaurants based on cost vs satisfaction

Scatter plots comparing user satisfaction ratings against average cost help identify the best value hotels and restaurants, avoiding expensive disappointments.

#9about 2 minutes

Future ideas for personalized vacation planning

The presentation concludes by exploring future possibilities, such as a recommendation engine that learns user preferences in real-time to suggest the next attraction.