University of Strathclyde

What you'll study

The programme blends core data analytics techniques with sport-specific applications. You will study modules covering statistical modelling, machine learning, time-series analysis, and data visualisation, alongside modules that focus on sport performance data, wearable sensors, biomechanics data processing and applied sport analytics workflows. Teaching combines lectures, hands-on labs and project work using real datasets drawn from match events, tracking systems and wearable devices.

Typical module topics include:

• Applied Statistical Methods – inference, regression, mixed models and methods for longitudinal data.
• Machine Learning for Sports – supervised and unsupervised learning approaches, model validation and interpretability for performance and talent identification.
• Time Series and Tracking Analysis – analysis of positional and sensor-derived time-series data, event detection and workload monitoring.
• Data Engineering and Visualisation – data acquisition, cleaning, SQL/databasing, reproducible workflows and communicating insights to coaches and stakeholders.
• Sensors, Biomechanics and Physiology – principles of wearable technology, signal processing and linking physiological measures to performance outcomes.
• Project / Dissertation – an independent, substantial applied research or industry project that integrates analytical methods with sport-specific questions.

Assessment is by a mix of practical coursework, group projects, presentations and a final project. Emphasis is on transferable skills: programming (commonly Python or R), scripting for data pipelines, experiment design, and presenting results to non-technical audiences.

Entry requirements

Applicants are normally expected to hold a good honours degree (equivalent to a UK 2:1 or above) in a numerate or relevant analytical discipline such as sport science with quantitative content, computer science, mathematics, statistics, engineering, or a related subject. Candidates with a lower second-class honours plus substantial relevant professional experience or a strong portfolio of analytical work may be considered.

Practical experience or demonstrable ability in at least one programming language (Python, R or similar), basic statistics and handling datasets is expected. Applicants without direct programming experience may be required to complete preparatory study or online modules prior to or at the start of the programme.

International applicants whose first language is not English must meet the University's English language requirements (for example, an approved academic English test or equivalent qualification). Specific entry enquiries should be directed to the admissions team for personalised guidance.

Career prospects

Graduates move into a variety of roles at the intersection of sport and data, including performance analyst, data scientist for clubs and federations, analytics roles with sports technology companies, and commercial data roles with broadcasters and rights holders. Other common destinations include roles in athlete monitoring and strength & conditioning support, consultancy for sports organisations, and product or business analytics in companies providing GPS, wearables and performance platforms.

The programme also provides preparation for research careers and PhD study in areas such as sports analytics, biomechanics, or health data science for sport-related applications. The applied project experience and emphasis on communication equip graduates to translate technical results into actionable recommendations for coaches, support staff and stakeholders.

Why study at University of Strathclyde

Studying at Strathclyde offers access to interdisciplinary teaching that draws on strong computing, engineering and sport science expertise. The university’s central Glasgow location provides proximity to professional sports clubs, technology firms and a vibrant sporting community, creating opportunities for applied projects and industry links.

Students benefit from practical lab-based sessions and an emphasis on industry-relevant skills—programming, data pipeline development and stakeholder communication—within a department experienced in data science teaching. The programme’s applied focus and ties with local partners help students build a portfolio of real-world analytics work that supports employability in the growing sports analytics sector.