Keele University

What you'll study

The programme covers foundational and advanced topics in artificial intelligence, machine learning and data science, delivered through a mix of lectures, practical labs and a substantial individual project. Teaching emphasises both the mathematical/statistical principles underpinning AI and the software engineering skills required to develop deployable systems.

• Core modules typically include: Machine Learning (supervised and unsupervised methods), Deep Learning, Statistical Methods for Data Science, Data Mining and Knowledge Discovery, and Research Methods and Professional Practice.
• Applied and systems modules: Big Data Technologies and Scalable Data Processing, Data Visualisation and Communication, Cloud and Distributed Computing, and Applied Natural Language Processing or Computer Vision (availability may vary).
• Ethics, governance and professional issues: Responsible AI, data protection, fairness and interpretability of models.
• Individual project or dissertation: A major piece of independent work that can be a research project or an industry‑facing development project, supervised by academic staff. Projects allow students to specialise in an area such as healthcare analytics, finance, robotics, or social data analysis.

Entry requirements

Applicants are normally expected to hold a good honours degree in computer science, mathematics, statistics, engineering or a closely related discipline. Candidates with a second‑class degree and relevant professional experience or strong quantitative background may be considered. Prior experience of programming (for example Python) and foundational knowledge of linear algebra, probability and statistics is highly recommended.

International applicants must meet the University's English language requirements; commonly accepted evidence includes an IELTS score at an appropriate level. Applications are considered on an individual basis and academic references and a personal statement outlining motivation and relevant experience are typically required.

Career prospects

Graduates move into a wide range of technical and analytical roles across industry, public sector and research. Typical career paths include:

• Data scientist or data analyst roles, building predictive models and extracting insights from complex datasets.
• Machine learning or AI engineer positions, focused on developing and deploying models into production systems.
• Business intelligence and analytics roles in sectors such as finance, healthcare, retail, consulting and government.
• Specialist roles in natural language processing, computer vision, robotics and Internet of Things (IoT) analytics.
• Further research or doctoral study in AI, data science or related areas for those interested in academic or advanced R&D careers.

Why study at Keele University

Keele combines a focused computing and mathematics community with an interdisciplinary campus environment that encourages collaboration with other schools such as health sciences and business. The School provides access to computing facilities, specialist software and hands‑on lab work, and students benefit from supervision by staff active in AI and data science research.

The programme is designed to be vocationally relevant, with applied projects and opportunities to engage with external organisations. Small group teaching and a supportive campus culture help students develop both technical expertise and professional skills such as communication, project management and ethical awareness, equipping graduates for a variety of careers in the expanding field of AI and data science.