The “Basics of Sports Analytics” Course consists of 3 tracks, plus a Capstone project.
Sports Track
- Asking questions, and fine-tuning the questions
- Finding Data needed for analysis
- Comparative analysis - Between players, teams, venues
- Visualization - Sports-specific plots and graphs
- Predictive Models in Different sports
Statistics Track
- Descriptive Statistics
- Bi-variate or comparitive statistics
- Regression to the mean
- Basics of Probability
- Hypothesis testing - t-tests, statistical significance
- Win Probabilities
Tech Track
- Python variables, strings
- Python Lists & Dictionaries
- Python: Conditional statements, Boolean arithmetic
- Reading from and writing to files
- Visualization using Python (Matplotlib/Seaborn)
- Using Pandas for Data Analysis
Advanced Tech Topics
- Databases (optional, advanced)
- Using API’s
- Webscraping (covered in the intermediate course)
- Colab and/or Jupyter Notebooks
- GitHub
- Excel
Capstone Project
- 1-page proposal
- Data procement
- Data clean-up and re-formatting data
- Exploratory analysis
- Plotting
- Model Building
- Presentation - Communicating the results