Module #1 Introduction to Sports Data Visualization Overview of the importance of data visualization in sports and course objectives
Module #2 Fundamentals of Data Visualization Basic principles of data visualization, including data types, visualization types, and best practices
Module #3 Sports Data Sources and Collection Overview of common sports data sources, including APIs, databases, and scraping techniques
Module #4 Data Cleaning and Preprocessing Techniques for cleaning, transforming, and preparing sports data for visualization
Module #5 Introduction to Tableau Hands-on introduction to Tableau, including data connection, dashboard creation, and basic visualizations
Module #6 Visualizing Game Statistics Creating visualizations for game statistics, such as scores, shots, and possession
Module #7 Visualizing Player Performance Creating visualizations for player performance, including metrics like points, rebounds, and assists
Module #8 Visualizing Team Performance Creating visualizations for team performance, including metrics like wins, losses, and strength of schedule
Module #9 Introduction to D3.js Hands-on introduction to D3.js, including data binding, SVG elements, and basic visualizations
Module #10 Interactive Visualizations with D3.js Creating interactive visualizations with D3.js, including hover effects, tooltips, and animations
Module #11 Visualizing Spatial Data in Sports Visualizing spatial data, including player tracking, shot charts, and possession maps
Module #12 Storytelling with Sports Data Visualization Using sports data visualization to tell compelling stories and answer complex questions
Module #13 Visualizing Advanced Analytics in Sports Visualizing advanced analytics, including expected possession value, true shooting percentage, and plus/minus
Module #14 Introduction to R for Sports Data Visualization Hands-on introduction to R, including data manipulation, visualization, and ggplot2
Module #15 Sports Data Visualization with Python Using Python libraries like Matplotlib, Seaborn, and Plotly for sports data visualization
Module #16 Creating Interactive Dashboards with Power BI Creating interactive dashboards with Power BI, including data modeling, visualization, and deployment
Module #17 Sports Data Visualization in the Real World Case studies of sports data visualization in practice, including examples from teams, leagues, and media outlets
Module #18 Ethics and Bias in Sports Data Visualization Considering ethics and bias in sports data visualization, including issues of fairness, transparency, and accountability
Module #19 Best Practices for Communicating Sports Data Insights Effective communication strategies for conveying sports data insights to non-technical stakeholders
Module #20 Designing for Mobile and Web Design principles for creating responsive and interactive sports data visualizations for mobile and web
Module #21 Working with Large Datasets in Sports Handling and visualizing large datasets in sports, including big data storage, processing, and visualization techniques
Module #22 Machine Learning for Sports Data Visualization Applying machine learning techniques to sports data visualization, including predictive modeling and clustering
Module #23 Sports Data Visualization in the Cloud Deploying sports data visualization applications in the cloud, including AWS, Azure, and Google Cloud
Module #24 Course Wrap-Up & Conclusion Planning next steps in Sports Data Visualization career
