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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Project Description
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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 Joining Data in SQL and its prerequisite, Intro to SQL for Data Science.
The data used in this Project was scraped and polished from Wikipedia. The database it is stored in is made up of three tables, one with game data, one with TV broadcast data, and one with halftime musician performance data for all 52 Super Bowls through 2018.
Project Tasks
- 1TV, halftime shows, and the Big Game
- 2Data issues in the broadcasts table
- 3Data issues in the performances table
- 4The highest- and lowest-scoring Super Bowls
- 5The blowouts and nailbiters
- 6Do blowouts translate to lost viewers?
- 7Viewership and the ad industry over time
- 8Halftime shows weren't always this great
- 9Who has the most halftime show appearances?
- 10Who performed the most songs in a halftime show?
- 11Conclusion
Technologies
SQL
Topics
Data ManipulationDavid Venturi
See MoreData Science Educator
David graduated from Queen's University with a dual degree in Chemical Engineering and Economics. After working for a year, he discovered online education (in the early MOOC era) and became enamored with its potential. He has since created content to help people navigate the space, including a DIY data science master's program, Class Central's Data Science Career Guide, courses for Udacity's Data Analyst Nanodegree program, and several DataCamp courses and projects. Visit his website to say hi!