Module #1 Introduction to Advanced Sports Analytics Overview of the field of sports analytics, importance of advanced techniques, and course objectives
Module #2 Review of Fundamentals Review of statistical concepts, data types, and data sources in sports analytics
Module #3 Machine Learning for Sports Data Introduction to machine learning, supervised vs. unsupervised learning, and regression analysis
Module #4 Advanced Regression Techniques Generalized linear models, regularization, and model selection
Module #5 Decision Trees and Random Forests Tree-based models, decision trees, random forests, and ensemble methods
Module #6 Clustering and Dimensionality Reduction K-means clustering, hierarchical clustering, PCA, and t-SNE
Module #7 Text Analytics for Sports Text data sources, sentiment analysis, and topic modeling
Module #8 Network Analysis for Sports Network data structures, centrality measures, and community detection
Module #9 Time Series Analysis for Sports ARIMA, Exponential Smoothing, and Prophet for forecasting and trend analysis
Module #10 Player and Team Performance Evaluation Advanced metrics for evaluating player and team performance, including advanced sabermetrics
Module #11 Game Theory and Strategic Decision Making Introduction to game theory, Prisoners Dilemma, and Nash Equilibrium
Module #12 Sports Data Visualization Effective visualization of sports data, including heat maps, scatter plots, and interactive dashboards
Module #13 Big Data in Sports Analytics Handling large datasets, distributed computing, and big data tools like Hadoop and Spark
Module #14 Predictive Modeling for Game Outcomes Building predictive models for game outcomes, including logistic regression and probability estimation
Module #15 Injury Prediction and Risk Assessment Using machine learning for injury prediction and risk assessment
Module #16 Player Tracking and Motion Analysis Using GPS, accelerometers, and computer vision for player tracking and motion analysis
Module #17 Sports Business Analytics Applying analytics to sports business, including revenue management and fan engagement
Module #18 Competitive Balance and Scheduling Analyzing competitive balance and scheduling in sports leagues
Module #19 Sports Betting and Odds Analysis Introduction to sports betting, odds analysis, andexpected value calculation
Module #20 Sports Journalism and Storytelling Using analytics to tell compelling stories in sports journalism
Module #21 Ethics in Sports Analytics Ethical considerations in sports analytics, including bias, privacy, and transparency
Module #22 Advanced Data Sources and APIs Using advanced data sources, including APIs, social media, and wearable data
Module #23 Cloud Computing for Sports Analytics Using cloud computing for sports analytics, including AWS, Google Cloud, and Azure
Module #24 Case Studies in Advanced Sports Analytics Real-world applications and case studies in advanced sports analytics
Module #25 Course Wrap-Up & Conclusion Planning next steps in Advanced Techniques in Sports Analytics career
