University of Strathclyde

What you'll study

This programme combines core modules in statistics, machine learning and data engineering with applied topics in data visualisation, business analytics and ethical issues in data use. Teaching typically covers:

• Statistical foundations and applied inference — probability, regression, hypothesis testing and applied statistical methods for real-world datasets.
• Machine learning and predictive modelling — supervised and unsupervised techniques, model evaluation and modern algorithms for classification and regression.
• Data engineering and big data technologies — database design, SQL, data pipelines, cloud services and distributed processing approaches used for large-scale analytics.
• Programming for data analysis — practical work in languages and tools such as Python, R and relevant libraries for data manipulation and analysis.
• Data visualisation and communication — principles and tools for presenting insights to technical and non-technical audiences, dashboarding and storytelling with data.
• Applied and domain-specific analytics — modules or project work addressing analytics in domains such as finance, health, marketing or public policy.
• Professional practice and ethics — data governance, privacy, reproducible research and the ethical implications of automated decision-making.

The degree culminates in a substantial project or dissertation that emphasises applied problem solving, often with an industry partner or using real datasets, allowing you to build a portfolio of work to demonstrate your skills to employers.

Entry requirements

Applicants are normally expected to hold a good honours degree (commonly a UK 2:1 or equivalent) in a numerate discipline such as computer science, mathematics, engineering, statistics, economics or a related subject. Candidates with a lower undergraduate classification but with relevant professional experience, programming ability or prior postgraduate study may be considered.

All applicants whose first language is not English must demonstrate proficiency in English through an approved qualification or test. You may also be asked to provide a CV, references and a personal statement outlining your interest in data analytics and any relevant project or work experience.

Career prospects

Graduates from this programme go on to roles such as data analyst, data scientist, business intelligence developer, machine learning engineer, analytics consultant and specialist roles within sectors including finance, health, energy, retail and the public sector. The combination of practical programming, statistical training and applied project work prepares you for both technical positions and roles that require translating analytical insight into business decisions.

Alumni also progress to further research or doctoral study in data science, machine learning or applied statistics, while the project/dissertation provides material for portfolios and case studies used in job applications and interviews.

Why study at University of Strathclyde

Strathclyde is known for its strong emphasis on applied research and close links with industry, offering opportunities to work on practical, employer-relevant projects. The university hosts active research groups and centres in data science, artificial intelligence and information management, providing access to research-informed teaching and specialist seminars.

Based in Glasgow, the university benefits from a vibrant tech and business ecosystem, regular industry engagement, and dedicated careers and enterprise services that support internships, placements and employer events. Students also have access to modern computing facilities, labs and training in current tools and cloud platforms, helping to ensure learning is hands-on and directly relevant to contemporary data roles.