Module #1 Introduction to Sports Data Visualization Overview of the importance of data visualization in sports, course objectives, and setup of Python environment
Module #2 Python Fundamentals for Data Visualization Review of basic Python concepts, data types, and libraries needed for data visualization
Module #3 Introduction to Popular Python Data Visualization Libraries Overview of popular data visualization libraries in Python, including Matplotlib, Seaborn, and Plotly
Module #4 Working with Sports Data Introduction to common sports data sources, data cleaning, and preprocessing techniques
Module #5 Visualizing Sports Data with Matplotlib Basic data visualization techniques using Matplotlib, including line plots, scatter plots, and bar charts
Module #6 Customizing Matplotlib Visualizations Customizing visualization appearance, adding labels, titles, and legends, and saving visualizations
Module #7 Introduction to Seaborn Overview of Seaborn, a visualization library built on top of Matplotlib, and its advantages
Module #8 Visualizing Sports Data with Seaborn Creating informative and attractive statistical graphics with Seaborn, including heatmaps and swarm plots
Module #9 Interactive Visualizations with Plotly Creating interactive visualizations with Plotly, including scatter plots, bar charts, and 3D plots
Module #10 Working with Geospatial Data in Sports Introduction to geospatial data in sports, including working with latitude and longitude coordinates
Module #11 Visualizing Sports Data on Maps Creating maps with Plotly and Folium to visualize sports data, including stadium locations and player movements
Module #12 Analyzing Team Performance Analyzing team performance metrics, including scoring rates, possession rates, and win/loss ratios
Module #13 Visualizing Player Performance Analyzing player performance metrics, including scoring rates, passing accuracy, and shot charts
Module #14 Visualizing Game Flow and Possession Visualizing game flow and possession metrics, including passing networks and possession charts
Module #15 Working with Advanced Sports Data Working with advanced sports data, including sports tracking data and player wearable data
Module #16 Visualizing Advanced Sports Data Visualizing advanced sports data, including player tracking data and sports analytics metrics
Module #17 Storytelling with Sports Data Visualization Effective storytelling techniques using sports data visualization, including creating interactive dashboards
Module #18 Best Practices for Sports Data Visualization Best practices for sports data visualization, including data quality, visualization choices, and color schemes
Module #19 Case Studies in Sports Data Visualization Real-world case studies of sports data visualization, including applications in team strategy and player development
Module #20 Final Project:Visualizing a Sports Dataset Applying course concepts to a final project, visualizing a sports dataset of choice
Module #21 Advanced Topics in Sports Data Visualization Advanced topics in sports data visualization, including machine learning and deep learning applications
Module #22 Sports Data Visualization Tools and Resources Overview of sports data visualization tools and resources, including APIs, datasets, and libraries
Module #23 Career Development in Sports Data Visualization Career development in sports data visualization, including job opportunities and professional networking
Module #24 Ethics in Sports Data Visualization Ethical considerations in sports data visualization, including bias, privacy, and data responsibility
Module #25 Group Project:Visualizing a Sports Analytics Metric Collaborative group project, visualizing a sports analytics metric of choice
Module #26 Advanced Visualization Techniques with Python Advanced visualization techniques with Python, including 3D visualization and virtual reality
Module #27 Sports Data Visualization with Other Tools Sports data visualization with other tools, including Tableau, Power BI, and R
Module #28 Creating Interactive Dashboards with Python Creating interactive dashboards with Python, including tools like Dash and Bokeh
Module #29 Deploying Sports Data Visualizations Deploying sports data visualizations, including web development and cloud deployment
Module #30 Course Wrap-Up & Conclusion Planning next steps in Sports Data Visualization with Python career
