Premium Project

TV, Halftime Shows, and the Big Game

Load, clean, and explore Super Bowl data in the age of soaring ad costs and flashy halftime shows.

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  • 10 tasks
  • 42,361 participants
  • 1,500 XP

Project Description

Whether or not you like football, the Super Bowl is a spectacle. There's drama in the form of blowouts, comebacks, and controversy in the games themselves. There are the ridiculously expensive ads, some hilarious, others gut-wrenching, thought-provoking, and weird. The half-time shows with the biggest musicians in the world, sometimes riding giant mechanical tigers or leaping from the roof of the stadium. And in this project, you will find out how some of the elements of this show interact with each other. You will answer questions like:

  • What are the most extreme game outcomes?
  • How does the game affect television viewership?
  • How have viewership, TV ratings, and ad cost evolved over time?
  • Who are the most prolific musicians in terms of halftime show performances?

This project gives you an opportunity to apply the skills from Intermediate Python for Data Science.

The dataset used in this project was scraped and polished from Wikipedia. It is made up of three CSV files, one with game data, one with TV data, and one with halftime musician data for all 52 Super Bowls through 2018.

Project Tasks

  • 1TV, halftime shows, and the Big Game
  • 2Taking note of dataset issues
  • 3Combined points distribution
  • 4Point difference distribution
  • 5Do blowouts translate to lost viewers?
  • 6Viewership and the ad industry over time
  • 7Halftime shows weren't always this great
  • 8Who has the most halftime show appearances?
  • 9Who performed the most songs in a halftime show?
  • 10Conclusion
David Venturi

Curriculum Manager at DataCamp

David is a Curriculum Manager at DataCamp. After majoring in Chemical Engineering and Economics, David created a personalized data science master's program using online resources. He has studied hundreds of online courses and created several himself. To connect, reach out on LinkedIn.

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  • Python LogoPython
  • Topics

    Data ManipulationData VisualizationImporting & Cleaning Data