Module #1 Introduction to Sports Data Analytics Overview of sports data analytics, its importance, and applications
Module #2 Python for Sports Data Analytics Introduction to Python, setup, and essential libraries for sports data analytics
Module #3 Data Sources for Sports Analytics Exploring data sources for sports analytics, including APIs, web scraping, and datasets
Module #4 Data Cleaning and Preprocessing in Sports Analytics Handling missing data, data normalization, and feature scaling in sports datasets
Module #5 Data Visualization for Sports Analytics Introduction to data visualization using popular Python libraries (Matplotlib, Seaborn, Plotly)
Module #6 Descriptive Statistics in Sports Analytics Calculating and interpreting descriptive statistics for sports data (mean, median, mode, correlation)
Module #7 Inferential Statistics in Sports Analytics Introduction to inferential statistics, hypothesis testing, and confidence intervals
Module #8 Regression Analysis in Sports Analytics Simple and multiple linear regression, interpretation of coefficients, and model evaluation
Module #9 Machine Learning in Sports Analytics Introduction to machine learning, supervised and unsupervised learning, and model evaluation metrics
Module #10 Supervised Learning in Sports Analytics Building predictive models using Python libraries (Scikit-learn, TensorFlow) for sports data
Module #11 Unsupervised Learning in Sports Analytics Clustering, dimensionality reduction, and anomaly detection in sports data
Module #12 Working with Sports APIs Accessing and manipulating sports data from popular APIs (e.g., NFL API, NBA API)
Module #13 Web Scraping for Sports Data Extracting sports data from websites using Python libraries (BeautifulSoup, Scrapy)
Module #14 Sports Database Management Designing and implementing databases for sports data using Python and relational databases (e.g., MySQL)
Module #15 Data Visualization for Sports Storytelling Creating interactive and dynamic visualizations to tell stories with sports data
Module #16 Advanced Sports Analytics Topics Exploring advanced topics in sports analytics, including spatial analysis and network analysis
Module #17 Sports Analytics with Python Libraries Using specialized Python libraries for sports analytics (e.g., sportsipy, pybaseball)
Module #18 Building a Sports Analytics Project Guided project development, from data collection to insights and visualization
Module #19 Sports Analytics for Fantasy Football Applying sports analytics concepts to fantasy football, including data collection and predictive modeling
Module #20 Sports Analytics for Basketball Applying sports analytics concepts to basketball, including advanced metrics and machine learning models
Module #21 Sports Analytics for Baseball Applying sports analytics concepts to baseball, including sabermetrics and advanced statistics
Module #22 Sports Analytics for Soccer Applying sports analytics concepts to soccer, including expected possession value and advanced metrics
Module #23 Sports Analytics for Tennis Applying sports analytics concepts to tennis, including rally analysis and serving strategy
Module #24 Best Practices for Sports Data Analytics Tips and best practices for working with sports data, including data validation and model interpretation
Module #25 Course Wrap-Up & Conclusion Planning next steps in Sports Data Analytics with Python career
