Loughborough University

What you'll study

The programme combines core topics in artificial intelligence and data analytics with practical hands-on work and a substantial individual project. Teaching typically covers machine learning (supervised and unsupervised methods), deep learning, probabilistic modelling and Bayesian methods, and statistical inference. You will also study data engineering subjects such as database systems, big data processing, data cleaning and pipeline design, together with applied techniques in natural language processing and computer vision.

Coursework emphasises practical implementation using contemporary tools and languages (for example Python and widely used ML libraries), software engineering best practice for data projects, and the use of cloud or high-performance compute resources for large-scale analytics. Modules usually include lab sessions, case studies and group coursework to develop both technical and professional skills.

The degree culminates in an independent research or industry-focused project/dissertation where you apply methods learned to a substantial data problem, developing end-to-end solutions from data collection and preprocessing through model development, validation and deployment considerations.

• Core topics: machine learning, deep learning, statistical methods, data engineering, optimisation and model evaluation.
• Applied topics: NLP, computer vision, time-series analytics, anomaly detection and recommender systems.
• Professional skills: data ethics and governance, reproducible research, project management and communication of results to non-technical stakeholders.
• Project: substantial individual project or industry placement project producing a defensible dissertation and code artefacts.

Entry requirements

Applicants are normally expected to hold a good honours degree (typically a UK 2:1 or international equivalent) in computer science, artificial intelligence, engineering, mathematics, statistics or a closely related numerate discipline. Applicants with a lower second-class degree and strong relevant work experience or professional certifications may be considered on an individual basis.

Admissions committees look for demonstrable programming experience (commonly Python), familiarity with probability and linear algebra, and evidence of analytical problem solving. Where applicants lack specific technical prerequisites, some programmes recommend or require completion of bridging modules or online preparatory work.

International applicants must meet the University's English language requirements; commonly an overall IELTS score around the mid-6 band with no lower than 6.0 in any component is acceptable, though higher scores or alternative qualifications may be requested—check the University's official guidance for current thresholds.

Career prospects

Graduates go on to technical roles where they design, build and evaluate AI and analytics systems. Typical job titles include data scientist, machine learning engineer, AI developer, data engineer, analytics consultant and quantitative analyst. Employers span technology companies, finance and insurance firms, healthcare and pharmaceutical organisations, manufacturing and supply chain companies, and government and public sector bodies.

The degree also prepares students for research careers and doctoral study in machine learning, data science and related fields. Many alumni leverage the programme’s project work and industry links to move into specialist roles focusing on areas such as computer vision, natural language processing, predictive maintenance and personalised medicine.

Why study at Loughborough University

Loughborough offers a well-established computing and data-science teaching environment with strong links to industry and applied research groups. The Department provides practical computing facilities, access to high-performance and cloud computing resources, and opportunities to collaborate on real-world projects with businesses and research centres.

Students benefit from a curriculum that balances theoretical foundations with hands-on experience and from career support services that help with employer engagement, internships and graduate recruitment. The university’s emphasis on employability and close contact between staff and students helps ensure graduates are prepared to contribute immediately in professional AI and analytics roles.