Techniques for Collecting, Prepping, and Plotting Data: Predicting Social Media-Influence in the NBA

This article provides insight on the mindset, approach, and tools to consider when solving a real-world ML problem. It covers questions to consider as well as collecting, prepping and plotting data. A complementary Domino project is available. Collecting and prepping data are core research tasks. While the most ideal situation is to start a project with clean well-labeled data, the reality is that data scientists spend countless hours on obtaining and prepping data. As Domino is committed to supporting data scientists and accelerating research, we reached out to Addison-Wesley Professional (AWP) Pearson for the appropriate permissions to excerpt "Predicting Social-Media Influence in the NBA" from the book, Pragmatic AI: An Introduction to Cloud-Based Machine Learning by Noah Gift. The excerpt dives into techniques for collecting, prepping, and plotting data. Many thanks to AWP Pearson for providing the permissions to excerpt the work as well as providing the data and code for us to include in a complementary Domino project. Sports is a fascinating topic for data scientists because there is always a story behind every number. Just because an NBA player scores more points than another player, it doesn't necessarily mean [they] add more value to the team. As a result, there has been a recent explosion in individual statistics that try to measure a player's impact.