Module #1 Introduction to Sports Data Analysis Overview of the importance of statistical analysis in sports, types of sports data, and the role of the sports data analyst.
Module #2 Descriptive Statistics for Sports Data Review of descriptive statistics, including summary statistics, data visualization, and exploratory data analysis for sports data.
Module #3 Inferential Statistics for Sports Data Introduction to inferential statistics, including hypothesis testing and confidence intervals for sports data.
Module #4 Regression Analysis for Sports Data Introduction to simple and multiple linear regression, including model building, estimation, and inference for sports data.
Module #5 Dimension Reduction Techniques for Sports Data Introduction to dimension reduction techniques, including principal component analysis (PCA) and clustering for sports data.
Module #6 Time Series Analysis for Sports Data Introduction to time series analysis, including ARIMA, Exponential Smoothing, and prophet for sports data.
Module #7 Machine Learning for Sports Data Introduction to machine learning, including supervised and unsupervised learning, and model evaluation for sports data.
Module #8 Predicting Game Outcomes with Regression Application of regression models to predict game outcomes, including model building and evaluation.
Module #9 Player Performance Analysis Introduction to player performance metrics, including advanced statistics and visualization techniques.
Module #10 Team Performance Analysis Introduction to team performance metrics, including advanced statistics and visualization techniques.
Module #11 Sports Data Visualization Introduction to data visualization techniques for sports data, including plotting and charting.
Module #12 Sports Data Mining Introduction to data mining techniques for sports data, including pattern discovery and knowledge representation.
Module #13 Injury Risk Prediction Application of statistical models to predict injury risk, including regression and machine learning techniques.
Module #14 Player Valuation Models Introduction to player valuation models, including regression and machine learning techniques.
Module #15 Game Strategy Analysis Introduction to game strategy analysis, including decision trees and game theory.
Module #16 Sports Data Case Studies Real-world case studies of sports data analysis, including examples from various sports.
Module #17 Sports Data Ethics Discussion of ethical considerations in sports data analysis, including privacy, fairness, and bias.
Module #18 Advanced Topics in Sports Data Analysis Exploration of advanced topics in sports data analysis, including deep learning and natural language processing.
Module #19 Sports Data Communication Best practices for communicating sports data insights to stakeholders, including visualization and storytelling techniques.
Module #20 Sports Data Tools and Technologies Overview of popular tools and technologies for sports data analysis, including R, Python, and Tableau.
Module #21 Working with Sports Data APIs Introduction to working with sports data APIs, including data retrieval and preprocessing.
Module #22 Sports Data Storage and Management Best practices for storing and managing sports data, including database design and data warehousing.
Module #23 Sports Data Team Management Best practices for managing sports data teams, including project management and collaboration techniques.
Module #24 Sports Data Project Development Guided project development, including project scoping, data collection, and deliverable creation.
Module #25 Course Wrap-Up & Conclusion Planning next steps in Advanced Statistical Methods for Sports Data career
