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WeAreDevelopers LIVE • Jun 1, 2023

Hacking Your Vacation: Using Data for Fun

Becky Gandillon

Official theme park wait times are artificially inflated. Learn how to use Python and random forest algorithms to outsmart the crowds and dynamically optimize your next big vacation.

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#1 about 4 min

Approaching data problems with an engineering and strategy mindset

How a background in biomedical engineering informs a structured approach to solving complex data problems.

#2 about 6 min

Defining optimization goals and constraints for theme park vacations

Identifying key variables like park capacity, attendance, and ticket costs to formulate a machine learning problem.

#3 about 7 min

Identifying public and proprietary data sources for predictive modeling

Leveraging school calendars, economic data, and real-time wait times to forecast anticipated crowd behaviors.

#4 about 9 min

Predicting theme park wait times using random forest algorithms

Applying Python and random tree forests to aggregate five-minute wait time increments into daily crowd levels.

#5 about 8 min

Optimizing attraction itineraries to minimize walking and waiting intervals

Using an interactive platform to calculate an efficient touring plan based on anticipated crowd curves.

#6 about 3 min

Integrating real-time dynamic updates via interactive mobile applications

Adjusting predicted itineraries on the fly by replacing static plans with live API polling and user submissions.

#7 about 7 min

Evaluating variable pricing and premium queues to optimize spending

Analyzing the cost-benefit ratio of lightning lanes and variable tickets to maximize time saved per dollar.

#8 about 8 min

Mapping satisfaction metrics against median costs for vacation amenities

Plotting restaurant and hotel data in quadrants to find options with above-average satisfaction and below-average cost.

#9 about 2 min

Envisioning recommendation systems for personalized theme park attraction routing

Conceptualizing a feedback-loop application that suggests subsequent attractions based on real-time user ratings and current wait times.

#10 about 9 min

Extrapolating data collection methods and cloud architecture data pipelines

Clarifying how wait times are continuously scraped and stored directly into cloud databases for ongoing analysis.

Matching moments

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2:18 min

Introduction to data science applications in the retail sector

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3:18 min

Utilizing AI and public data sharing for urban planning

Christian Wiegand +3 · World Congress 2024

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World Congress 2026 North America

September 24, 2026 · 14:50–15:20

Stage 5

Travel’s AI-Powered Shift: How Expedia Group Is Building in the AI Era

Debashis Saha, Julia Elliott

Debashis Saha
Julia Elliott
Open session

World Congress 2026 North America

September 24, 2026 · 14:50–15:20

Stage 9

How to generate business value through performance optimizations

Nikolai Sidiropulo

Software Engineer at Meta

Nikolai Sidiropulo
Open session

World Congress 2026 North America

September 23, 2026 · 15:40–16:10

Stage 2

Lean Intelligence: Lessons from GitHub Copilot Data Science Efforts

Rahul Pandita

Researcher and Technical Advisor at Microsoft

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Open session

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September 25, 2026 · 09:40–10:10

Stage 2

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LinkedIn, Senior Staff Technical Program Manager

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Open session

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September 25, 2026 · 16:50–17:20

Stage 4

Engineering Moneyball: How We Benchmarked Google vs Meta

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Co-Founder & CEO

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