Module #1 Introduction to Sports Analytics Overview of the field of sports analytics, its history, and its applications in various sports industries.
Module #2 Data Sources in Sports Analytics Exploring different data sources in sports analytics, including play-by-play data, tracking data, and survey data.
Module #3 Data Cleaning and Preprocessing in Sports Analytics Best practices for cleaning and preprocessing sports data, including handling missing values and data normalization.
Module #4 Descriptive Analytics in Sports Using summary statistics and data visualization to understand sports data, including metrics such as mean, median, and correlation.
Module #5 Inferential Analytics in Sports Using hypothesis testing and confidence intervals to make inferences about sports data, including tests for means and proportions.
Module #6 Regression Analysis in Sports Applying linear regression to model the relationship between variables in sports data, including simple and multiple regression.
Module #7 Machine Learning in Sports Analytics Introduction to machine learning concepts and algorithms in sports analytics, including supervised and unsupervised learning.
Module #8 Classification Models in Sports Using classification models such as logistic regression and decision trees to predict outcomes in sports.
Module #9 Clustering Analysis in Sports Applying clustering algorithms such as k-means and hierarchical clustering to identify patterns in sports data.
Module #10 Sports Data Visualization Using data visualization tools such as Tableau, Power BI, or D3.js to effectively communicate insights in sports data.
Module #11 Advanced Sports Data Visualization Creating interactive and dynamic visualizations using programming languages such as R or Python.
Module #12 Sports Analytics in Team Sports Applying sports analytics concepts to team sports such as basketball, football, and soccer.
Module #13 Sports Analytics in Individual Sports Applying sports analytics concepts to individual sports such as tennis, golf, and boxing.
Module #14 In-Game Strategy and Decision Making Using sports analytics to inform in-game strategy and decision making, including play calling and player substitution.
Module #15 Player Evaluation and Valuation Using sports analytics to evaluate and value player performance, including metrics such as WAR and ERP.
Module #16 Roster Construction and Salary Cap Management Applying sports analytics to optimize roster construction and salary cap management in professional sports.
Module #17 Fan Engagement and Sports Marketing Using sports analytics to understand fan behavior and optimize sports marketing strategies.
Module #18 Sports Analytics in Fantasy Sports Applying sports analytics concepts to fantasy sports, including player projection and lineup optimization.
Module #19 Ethics and Bias in Sports Analytics Discussing the ethical considerations and potential biases in sports analytics, including issues of fairness and transparency.
Module #20 Case Studies in Sports Analytics Exploring real-world case studies of sports analytics applications in various sports industries.
Module #21 Communication and Storytelling in Sports Analytics Developing skills for communicating complex sports analytics insights to non-technical audiences.
Module #22 Advanced Topics in Sports Analytics Exploring advanced topics in sports analytics, including computer vision, natural language processing, and deep learning.
Module #23 Sports Analytics Tools and Technologies Surveying various tools and technologies used in sports analytics, including R, Python, and SQL.
Module #24 Career Development in Sports Analytics Discussing career paths and development opportunities in sports analytics, including networking and portfolio building.
Module #25 Final Project:Applied Sports Analytics Applying sports analytics concepts to a real-world problem or scenario, including data collection, analysis, and presentation.
Module #26 Special Topics in Sports Analytics Exploring special topics in sports analytics, including sports psychology, sociology, and sports technology.
Module #27 Sports Analytics in Emerging Markets Examining the role of sports analytics in emerging markets, including esports, womens sports, and international sports.
Module #28 Sports Analytics and Social Responsibility Discussing the intersection of sports analytics and social responsibility, including issues of diversity, equity, and inclusion.
Module #29 Sports Analytics and Storytelling Using sports analytics to tell compelling stories and narratives, including data journalism and sports broadcasting.
Module #30 Course Wrap-Up & Conclusion Planning next steps in Introduction to Sports Analytics career
