University of Nottingham

What you'll study

This MSc combines core statistical and computational methods with applied modules and a substantial research or industry-facing project. Teaching blends lectures, practical lab sessions and group work, with emphasis on hands-on use of common tools and environments for data cleaning, modelling, deployment and visualisation.

• Core computing and programming: Python/R programming for data analysis, software engineering practices for reproducible data science, and working with databases.
• Statistical foundations: probability and statistical inference, regression and multivariate methods, experimental design and causal inference.
• Machine learning and AI: supervised and unsupervised learning, deep learning basics, model evaluation and selection, and scalable learning methods.
• Big data and data engineering: distributed data processing, cloud-based workflows, working with streaming data and data pipelines.
• Data visualisation and communication: principles of effective visualisation, dashboarding tools, and communicating findings to non-specialist stakeholders.
• Applied modules and electives: options typically allow specialisation in areas such as natural language processing, time-series analysis, bioinformatics data analysis, or econometrics depending on availability.
• Individual project or dissertation: an extended piece of independent work where you apply techniques learned to a substantial problem; projects can be research-led or carried out with industrial partners.

Assessment is a mixture of coursework, practical projects, presentations and a final project/dissertation. The programme encourages use of real datasets and exposure to contemporary tools used in industry.

Entry requirements

Applicants are normally expected to hold a good undergraduate degree (or equivalent) in a quantitative or computing-related subject such as mathematics, statistics, computer science, engineering, physics, economics or a closely related discipline. Candidates with other backgrounds but demonstrable programming ability and quantitative skills may be considered, often with the recommendation to complete preparatory modules.

Relevant professional experience or prior study in statistics, programming or machine learning can strengthen an application. All applicants whose first language is not English will need to meet the university's English language requirements (for example an approved IELTS/TOEFL score or an equivalent qualification).

Career prospects

Graduates typically move into roles such as data scientist, data analyst, machine learning engineer, business intelligence developer, analytics consultant or data engineer. The combination of technical skills and applied project experience also provides a good foundation for further academic study at PhD level or research roles in industry.

The programme develops both technical competencies (modelling, programming, data engineering) and professional skills (communication, project management, presenting to stakeholders), which are attractive to employers across finance, healthcare, retail, manufacturing, government and technology sectors.

Why study at University of Nottingham

The University of Nottingham offers strong research-led teaching in data science and related areas, benefiting from interdisciplinary expertise across mathematics, computer science, statistics and domain-specific research centres. Students have access to computing facilities, high-performance computing resources and modern data science software.

The School has established links with industry and research partners, enabling project collaborations and guest talks from practitioners. Support services include academic supervision, a careers service with employer engagement and opportunities for internships or placement-style projects where available.

Studying at Nottingham also provides a campus environment with dedicated learning spaces, student societies related to computing and data, and opportunities to engage with a broad academic community across the university.