This week will use NBA data to introduce basic and important Python codes to conduct data cleaning and data preparation. This week also discusses summary and descriptive analyses with statistics and graphs to understand the distribution of data, the characteristics and pattern of variables as well as the relationship between two variables. At the end of this week, we will introduce correlation coefficients to summarize the linear relationship between two variables.

This module introduces some ways of representing data using examples from MLB, the NBA and Indian Premier League. MLB data is used to analyze the spatial distribution of different hits. NBA data is used to generate heatmaps to illustrate the different ways in which players contribute. IPL data is used to show how team performances can be compared graphically.

This week introduces the fundamentals of regression analysis. We will discuss how to perform regression analysis using Python and how to interpret regression output. We will use NHL data to estimate multiple regression models to identify the team level performance factors that affect the team's winning percentage. We will also use cricket data from the Indian Premier League to run regression analyses to examine whether player performance impacts player salary.

This module uses regression analysis to investigate the relationship between team salary spending and team performance in the NBA, NHL, EPL and IPL. The module explores different ways of defining the regression model, and how to interpret competing regression model results.

This week studies an interesting topic in sport, the hot hand. We will introduce the concept of hot hand and discuss the academic research that examines whether the hot hand is a phenomenon or a fallacy. We will demonstrate how to analytically test the hot hand using the NBA shot log data. We will test whether NBA players have hot hand by computing conditional probabilities and autocorrelation coefficients as well as performing regression analyses.