This course introduces the essential mathematical, statistical, and data-handling concepts required to work effectively in football analytics. Learners will build a solid foundation by exploring measures of central tendency, variability, probability distributions, standard deviations, and confidence intervals, the core concepts that underpin all analytical reasoning in sport. Through football-specific examples, the course explains when to use different estimators, how to interpret uncertainty, and why choosing the right distribution is critical when modeling performance and match events.

Beyond statistics, learners discover the ecosystem of football data itself, including data on counts, GPS tracking, event data, and skeletal tracking, and understand how each type is collected, structured, and used in professional analysis. The course also introduces key analytical tools such as APIs, web scraping, Python, data structures, and visualization principles using Tableau, Power BI, and Matplotlib. By the end of this course, learners will be equipped with the mathematical intuition, technical fundamentals, and data-literacy skills needed to analyze football effectively and to transition smoothly into more advanced analytical and modeling techniques.