Keele University

What you'll study

The degree combines core computing and mathematical foundations with applied data science techniques. In early years you study programming (commonly Python and R), statistics and probability, databases, and fundamental discrete and continuous mathematics. Core themes across the course include data cleaning and wrangling, exploratory data analysis, supervised and unsupervised machine learning, data visualisation, and principles of experimental design.

• Programming and software development for data analysis
• Statistical inference, regression and time series analysis
• Databases, SQL and data management
• Machine learning algorithms and model evaluation
• Big data approaches, cloud-based tools and scalable processing
• Data visualisation and communication of results to non-technical audiences
• Data ethics, legal issues and responsible use of algorithms
• Individual or group research project/dissertation in a data-focused topic

Students progress from structured taught modules to more independent, project-led work, with opportunities to apply skills to real datasets. Keele often offers pathways or optional units that draw on domain areas such as healthcare, social science, or business analytics to emphasise interdisciplinary application.

Entry requirements

Applicants are normally expected to have qualifications that include mathematics at a level demonstrating competence in quantitative reasoning. Typical offers include A-levels, equivalent international qualifications, or a recognised access course. Practical experience or qualifications in computing, statistics, or a related subject strengthen an application.

Other typical requirements and considerations:

• GCSE mathematics at a standard grade or equivalent is usually required.
• Applicants with non-traditional backgrounds may be considered if they demonstrate appropriate numerical and problem-solving skills.
• Personal statement and references should evidence motivation for data science and any relevant programming or analytical experience.
• For international applicants, proof of English language competence is required where applicable.

Career prospects

Graduates move into a broad range of analytical and technical roles. Common destinations include data analyst, junior data scientist, business intelligence developer, machine learning engineer, and roles in data engineering, software development and consultancy. The degree also provides a strong foundation for postgraduate study such as MSc courses in data science, artificial intelligence, statistics or related research degrees.

Keele’s emphasis on practical projects and communication skills helps graduates present complex results to non-technical stakeholders, a skill valued across sectors such as finance, healthcare, public services, technology firms and more.

Why study at Keele University

Keele offers a campus-based environment that supports interdisciplinary learning and close contact with academic staff. The Data Science programme benefits from small-group teaching in computing and statistics, well-equipped computing labs, and opportunities to work on applied projects with real datasets. The university’s emphasis on research-led teaching and student support helps prepare graduates for both employment and further study.

Students can also access career services, industry links and optional placement or work-experience opportunities to gain practical experience. Keele’s community-focused campus and breadth of subject areas provide additional chances to combine data science skills with domains such as health, social science and business.