This module introduces students to the general concepts of statistics and data analysis and the role of data-generated insight for sports performance, participation and business functions. It will provide the students with the appropriate tools to generate, summarise, visualise and analyse sports-related datasets and report the results in an appropriate format. It provides the students with the ability to apply appropriate statistical techniques to data sets gathered during project work and also to comment on, and rate techniques used in existing studies.

1. Explain and critique the theory, growth and application of data-based intelligence in sports performance, sports participation and sports business
2. Ethically apply appropriate skills and techniques in the areas of data set generation, data mining, dataset evaluation and classification for participation, performance and other sports related data.
3. Use appropriate techniques to visualise sports participation, performance and business data.
4. Describe and justify the selection and application of appropriate statistical techniques for the analysis of sports related data.
5. Use appropriate software to automate the mining, analysis and visualisation of sports related data.

This module aims to introduce basic concepts, principles, methods and techniques of data analytics and AI for sport. It will develop skills and techniques for practical applications of data analytics using AI and machine learning through engagement in pattern discovery with data. The importance of pattern discovery with applications for data analytics will be explored. Data mining processes including data cleaning, preparation, and exploration algorithms will be introduced along with machine learning algorithms for clustering, classification, regression, and prediction.

1. Explain the concept of ‘big data’ and critically analyse data strategy and procedures in the context of a human-centred, ethical and inclusive approach to data
2. Critically evaluate the quality and potential limitations of sports data sets and identify strategies to clean and structure datasets appropriately
3. Manage data incorporating data architecture, cloud computing, databasing, and data mining
4. Apply and evaluate data mining techniques and models for knowledge discovery in sport related datasets
5. Apply machine learning algorithms and appraise the potential benefits and challenges of their use within sporting contexts