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Course Description
This 6–8 week course provides a comprehensive introduction to basketball analytics in R, organized around key pillars of modern sports data science. You will learn to install and configure R, RStudio, and the Tidyverse; interpret core basketball metrics (like PER, TS%, Win Shares, and BPM); collect and wrangle NBA data; and develop both linear and logistic models to evaluate players, inform game strategy, and forecast future performance.
Each lesson concludes with a brief quiz to reinforce important concepts, leading up to a 20-question final exam. You will also complete three real-world projects—Player Evaluation, Game Strategy, and Forecasting Future Performance—that serve as portfolio pieces, demonstrating your mastery of data analytics in a basketball front-office context. No prior programming or basketball analytics experience is required to enroll.
What You’ll Learn?
- Foundations of Basketball Analytics: Set up R, RStudio, and Tidyverse, and understand how analytics fits into the modern NBA ecosystem.
- Introduction to Advanced Metrics: Gain a working knowledge of PER, TS%, Usage Rate, Win Shares, and other metrics that drive player valuation.
- Data Collection & Analysis Tools: Learn to access NBA data (via CSV, APIs, or scraping), wrangle and clean datasets, and create compelling EDA visualizations.
- Player Evaluation Techniques: Use linear regression to analyze factors affecting player performance (e.g., points, minutes, usage), and interpret model outputs.
- Game Strategy Analysis: Explore logistic regression for binary outcomes, such as All-Star predictions or win/loss, and apply confusion matrices for model evaluation.
- Forecasting Future Performance: Combine regression methods and advanced metrics to predict next-season breakouts and guide data-driven front-office decisions.
- Portfolio Projects & Final Exam: Complete three practical projects—Player Evaluation, Game Strategy, and Forecasting Future Performance—and solidify your understanding with a 20-question comprehensive final exam.
Course Content
Intro to Basketball Analytics Curriculum
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Foundations of Basketball Analytics
14:01 -
Lesson 1 Quiz
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Introduction to Advanced Metrics
33:39 -
Lesson 2 Quiz
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Data Collection and Analysis Tools
31:12 -
Lesson 3 Quiz
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Player Evaluation Techniques
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Lesson 4 Quiz
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Game Strategy Analysis
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Lesson 5 Quiz
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Forecasting Future Performance
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Lesson 6 Quiz
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Final Exam
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