Module #1 Introduction to Predictive Analytics in Sports Overview of predictive analytics, its applications in sports, and the importance of data-driven decision making in the industry.
Module #2 Sports Data Sources and Collection Exploring various sports data sources, including APIs, datasets, and collection methods.
Module #3 Data Preprocessing and Cleaning Best practices for preprocessing and cleaning sports data, including handling missing values and outliers.
Module #4 Introduction to Statistical Modeling Foundations of statistical modeling, including regression analysis and probability theory.
Module #5 Regression Analysis in Sports Applying regression analysis to sports data, including linear and logistic regression.
Module #6 Machine Learning Fundamentals Introduction to machine learning, including supervised and unsupervised learning, and model evaluation metrics.
Module #7 Decision Trees and Random Forests in Sports Using decision trees and random forests to predict sports outcomes and identify key factors.
Module #8 Clustering Analysis in Sports Applying clustering algorithms to segment sports data and identify patterns.
Module #9 Dimensionality Reduction Techniques Using PCA and t-SNE to reduce dimensionality and visualize high-dimensional sports data.
Module #10 Predicting Game Outcomes Building predictive models to forecast game outcomes, including point spreads and win probabilities.
Module #11 Player Performance Evaluation Using advanced statistics to evaluate player performance, including metrics like WAR and BPM.
Module #12 Team Performance Analysis Analyzing team performance, including metrics like pace, efficiency, and-defense.
Module #13 In-Game Decision Analysis Using data to inform in-game coaching decisions, including timeout usage and lineup optimization.
Module #14 Sports Betting and Odds Analysis Using predictive models to inform sports betting decisions and analyze odds.
Module #15 Player Valuation and Contract Analysis Using data to evaluate player value and inform contract negotiations.
Module #16 Roster Construction and Team Building Using predictive analytics to inform roster construction and team building decisions.
Module #17 Advanced Analytics in Specific Sports Applying predictive analytics to specific sports, including baseball, basketball, football, and hockey.
Module #18 Working with Advanced Data Sources Exploring advanced data sources, including tracking data and wearable technology.
Module #19 Data Visualization in Sports Using data visualization to communicate insights and tell stories with sports data.
Module #20 Model Deployment and Maintenance Deploying and maintaining predictive models in a sports context.
Module #21 Ethical Considerations in Sports Analytics Exploring ethical considerations in sports analytics, including bias and fairness.
Module #22 Case Studies in Sports Analytics Real-world case studies of predictive analytics in sports, including success stories and challenges.
Module #23 Career Development in Sports Analytics Career paths and development opportunities in sports analytics, including networking and professional development.
Module #24 Industry Trends and Future Directions Exploring current industry trends and future directions in sports analytics, including AI and machine learning advancements.
Module #25 Course Wrap-Up & Conclusion Planning next steps in Predictive Analytics in Sports career
