Introduction
Welcome to the Jam! Basketball champion and global icon LeBron James goes on an epic adventure alongside timeless Tune Bugs Bunny in the animated/live-action event "Space Jam: A New Legacy," from director Malcolm D. Lee and an innovative filmmaking team including Ryan Coogler and Maverick Carter.
This transformational journey is a manic mashup of two worlds that reveals just how far some parents will go to connect with their kids. When LeBron and his young son Dom are trapped in a digital space by a rogue A.I., LeBron must get them home safely by leading Bugs, Lola Bunny, and the whole gang of notoriously undisciplined Looney Tunes to victory over the A.I.'s digitized champions on the court: a powered-up roster of basketball stars as you’ve never seen them before. It's Tunes versus Goons in the highest-stakes challenge of his life, that will redefine LeBron's bond with his son and shine a light on the power of being yourself. The ready-for-action Tunes destroy convention, supercharge their unique talents and surprise even "King" James by playing the game their own way.
Inspired by the film, this module explores the connection between sports (specifically basketball) and data science and machine learning. If you're a sports fan, you know that statistics (stats) about players are a huge part of the game. You might have even used stats to build fantasy leagues. This module gives you insight into how coaches and teams can use stats.
In this module, you get to explore stats related to basketball players (both human and animated). You'll learn how various stats result in a PER (player efficiency rating), which is the per-minute productivity rating for each player. Your goal is to use machine learning practices to create a clean dataset of players. The dataset is used during games to make quick decisions and optimize a team's chance of winning.
Tip
This module is part of a multimodal learning experience. Follow along with a video walkthrough of the module in a new tab.
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Learning objectives
In this module, you will:
- Use Python, pandas, and Visual Studio Code.
- Cleanse and manipulate data that requires critical analysis.
- Use machine learning to cleanse and complete data.
- Prepare data to be used for multiple customer-facing needs.