Data Show and Tell: Crafting the Ideal Basketball Team with Python
Discover how to analyze player statistics using Python. This course will demonstrate a unique project that selects the best players for the season using key basketball metrics.
What you'll learn
Analyzing player statistics to build a competitive team is both engaging and relevant for data professionals interested in sports analytics.
In this course, Data Show and Tell: Crafting the Ideal Basketball Team with Python, you'll see how to fetch player statistics, clean and filter data, and apply criteria to identify the top performers for each position. You’ll learn the significance of key metrics like points per game (PTS), rebounds per game (TRB), assists per game (AST), steals per game (STL), blocks per game (BLK), field goal percentage (FG%), three-point percentage (3P%), and free-throw percentage (FT%). This hands-on demo aims to equip you with the knowledge to make informed analyses for building a competitive team. The course also includes visualizing team strengths using a pie chart for a comprehensive understanding.
Table of contents
About the author
Pinal Dave is an SQL Server Performance Tuning Expert and independent consultant with over 22 years of hands-on experience. He holds a Master of Science degree and numerous database certifications. Pinal has authored 14 SQL Server database books and 75 Pluralsight courses. To freely share his knowledge and help others build their expertise, Pinal has also written more than 5,800 database tech articles on his blog at https://blog.sqlauthority.com.