Introduction to Sports Analytics with R
( 20 Modules )

Module #1
Introduction to Sports Analytics
Overview of the field of sports analytics, its applications, and importance

Module #2
Introduction to R
Basics of R programming, installation, and setup

Module #3
Importing and Manipulating Sports Data
Importing data from various sources, data types, and basic data manipulation in R

Module #4
Data Visualization in R
Introduction to data visualization in R using ggplot2 and base graphics

Module #5
Descriptive Statistics in Sports
Calculating and interpreting descriptive statistics in sports, including means, medians, and standard deviations

Module #6
Data Wrangling for Sports Data
Cleaning, transforming, and preparing sports data for analysis

Module #7
Introduction to Regression Analysis
Basic concepts of regression analysis, simple and multiple regression in R

Module #8
Regression Analysis in Sports
Applying regression analysis to sports data, including predicting player performance and game outcomes

Module #9
Working with Sports Data from APIs
Importing data from sports APIs, including NBA, NFL, and MLB APIs

Module #10
Understanding Advanced Metrics in Sports
Introduction to advanced metrics in sports, including plus/minus, WAR, and PER

Module #11
Player Performance Analysis
Analyzing player performance using advanced metrics and data visualization techniques

Module #12
Team Performance Analysis
Analyzing team performance using advanced metrics and data visualization techniques

Module #13
Game Outcome Prediction
Predicting game outcomes using machine learning algorithms and regression analysis

Module #14
Clustering and Segmentation in Sports
Applying clustering and segmentation techniques to sports data, including Identifying player types and team styles

Module #15
Survival Analysis in Sports
Applying survival analysis to sports data, including analyzing player careers and team longevity

Module #16
Network Analysis in Sports
Applying network analysis to sports data, including analyzing player and team networks

Module #17
Communicating Results in Sports Analytics
Effective communication of sports analytics results, including data visualization and storytelling

Module #18
Case Studies in Sports Analytics
Real-world case studies of sports analytics applications, including player evaluation and game strategy

Module #19
Special Topics in Sports Analytics
Advanced topics in sports analytics, including computer vision and machine learning applications

Module #20
Course Wrap-Up & Conclusion
Planning next steps in Introduction to Sports Analytics with R career

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Introduction to Sports Analytics with R ( 20 Modules )

Introduction to Sports Analytics with R
( 20 Modules )