Compulsory modules

This module will provide foundational skills in python and R to clean, prepare, and visualise sports data including spatial distributions, heatmaps, and other visual representations. Foundational statistical skills will also be introduced to understand data patterns and relationships between variables. Module examples will be derived using sporting data from major leagues such as the NBA, MLB, EPL, and IPL.

The aim of this module is to give students an opportunity to explore grand challenges facing our global society and to propose imaginative solutions to specific challenges in one or more country.

Students will critically reflect on the United Nations Sustainability Development Goals and think about how Loughborough University's Creating Better Futures. Together Strategy might contribute to them.

Students will engage with ideas and approaches to possible solutions from their own programme and gain diverse insights from Loughborough University London's interdisciplinary ecosystem. This will involve solution-oriented thinking and a balance between criticality and possibility, leading to a deep understanding of grand challenges and imagining creative responses to them.

Optional modules

The aims of this module are to:

  • Introduce students to the foundational concepts of data processing and their use in Artificial Intelligence (AI).
  • Enable students to gain background knowledge necessary to understand and develop different algorithms in AI and Data analytics.

This module will focus on regression-based analysis as a primary tool in sport analytics and the various applications in sport. Correlation and regression analysis in python will be introduced, followed by examples of multiple regression model applications, such as what variables impact half-marathon finishing time for runners, or examining the variables that impact player valuation in football.