Compulsory modules

The aim of this module is to provide the students with an understanding and programming skills for for solving practical problems in different applications.

This module will introduce various techniques associated with sport analytics and team performance. Students will employ Python to learn and develop analytical tools such as data visualisation, narrative storytelling and introductory regression analysis to analyse player performance data.

Students will be able to critically evaluate the relevant sports literature, interpret sports data, and report to a lay audience.

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.

The aims of this module are to provide the foundation for the statistical principles utilised in sport analytics to analyse team and player performance data, as well as the development of predictive models from historical sports data.

Upon completion of the module students will have foundational programming skills in python, as well as underlying statistical knowledge to successfully progress to learning machine learning based models needed in advanced performance analysis in sport.