Module #1 Introduction to Data Visualization in Sports Overview of the importance of data visualization in sports, and how Python can be used to create interactive and informative visualizations
Module #2 Installing Python and necessary libraries Step-by-step guide to installing Python, Pandas, Matplotlib, Seaborn, and other necessary libraries for data visualization
Module #3 Loading and Cleaning Sports Data How to load and clean sports data from various sources, including APIs, CSV files, and databases
Module #4 Exploratory Data Analysis (EDA) in Sports Using Pandas and Matplotlib to perform exploratory data analysis on sports data, including summary statistics and data visualization
Module #5 Visualizing Sports Data with Matplotlib Basic visualization techniques using Matplotlib, including line plots, scatter plots, and bar charts
Module #6 Visualizing Sports Data with Seaborn Advanced visualization techniques using Seaborn, including heatmaps, pair plots, and swarm plots
Module #7 Interactive Visualizations with Plotly Creating interactive visualizations using Plotly, including 3D plots, scatter plots, and line plots
Module #8 Visualizing Player Performance Data Visualizing individual player performance data, including statistics and trends
Module #9 Visualizing Team Performance Data Visualizing team performance data, including statistics and trends
Module #10 Visualizing Game Data Visualizing game data, including play-by-play data, possession charts, and shot charts
Module #11 Geospatial Visualization in Sports Visualizing geospatial data in sports, including stadium locations, player origins, and game schedules
Module #12 Network Analysis in Sports Visualizing network data in sports, including player connections, team networks, and game schedules
Module #13 Advanced Visualization Techniques Advanced visualization techniques, including animated visualizations, interactive dashboards, and 3D visualizations
Module #14 Storytelling with Data Visualization in Sports Using data visualization to tell stories and communicate insights in sports
Module #15 Sports Data Sources and APIs Overview of available sports data sources and APIs, including NFL API, NBA API, and Opta Sports
Module #16 webscraping for Sports Data Introduction to web scraping using Python and BeautifulSoup for collecting sports data
Module #17 Working with Large Sports Datasets Techniques for working with large sports datasets, including data compression, data sampling, and data aggregation
Module #18 Sports Data Visualization Best Practices Best practices for creating effective and informative sports data visualizations
Module #19 Creating Interactive Dashboards Using tools like Dash and Flask to create interactive dashboards for sports data visualization
Module #20 Deploying Sports Data Visualizations Deploying sports data visualizations to the web using GitHub Pages, Heroku, and other platforms
Module #21 Case Study:Visualizing NBA Data Real-world case study of visualizing NBA data using Python and various visualization libraries
Module #22 Case Study:Visualizing NFL Data Real-world case study of visualizing NFL data using Python and various visualization libraries
Module #23 Case Study:Visualizing MLB Data Real-world case study of visualizing MLB data using Python and various visualization libraries
Module #24 Case Study:Visualizing Soccer Data Real-world case study of visualizing soccer data using Python and various visualization libraries
Module #25 Course Wrap-Up & Conclusion Planning next steps in Data Visualization in Sports Using Python career
