University of Bradford

What you'll study

This programme blends taught modules with hands-on practice and an independent research or industry project. You will learn foundational and advanced AI methods alongside applied data-analytics techniques, emphasising practical implementation, evaluation and responsible deployment.

• Core AI and machine learning: supervised and unsupervised learning, model evaluation, ensemble methods, and neural networks.
• Deep learning and specialised techniques: convolutional and recurrent architectures, transfer learning and modern optimisation strategies.
• Data analytics and engineering: data cleaning and preprocessing, feature engineering, scalable data pipelines, and working with structured and unstructured data.
• Big data and cloud technologies: distributed processing, use of cloud platforms for model training and deployment, and consideration of performance and cost.
• Natural language processing and computer vision: practical approaches to text and image understanding used in industry applications.
• Data visualisation and communication: techniques for communicating insights to technical and non-technical stakeholders, dashboards and reporting.
• AI governance and ethics: fairness, transparency, privacy, regulatory considerations and the social impact of AI systems.
• Research/Industry Project: an extended individual project working on an applied problem with either a research focus or an industry partner, demonstrating end-to-end skills from problem definition to deployed solution or evaluation.

Teaching is typically a mix of lectures, practical labs, workshops and group work. Assessment methods include coursework, project work, presentations and a substantial dissertation or industry project where you apply techniques to a real dataset or challenge.

Entry requirements

Applicants are normally expected to hold a good undergraduate degree (equivalent to a UK second-class honours, upper division / 2:1) in computer science, mathematics, engineering, statistics or a closely related discipline. Candidates with a lower classification plus relevant professional experience or evidence of quantitative computing skills may be considered.

Typical evidence of preparedness includes programming experience (for example Python), familiarity with basic probability and statistics, and exposure to algorithms or data structures. If you do not have all technical prerequisites, the University may suggest preparatory modules or short courses.

International applicants whose first language is not English will need to meet the University's English language requirements; equivalents of commonly accepted tests (such as an overall IELTS score) are considered.

Applications from professionals with substantial industry experience in data, analytics or software development are welcomed and assessed on an individual basis; applicants may be asked to provide a CV and a personal statement outlining their motivation and relevant experience.

Career prospects

Graduates are prepared for a range of technical and applied roles where AI and data analytics are central. Common job destinations include:

• Data scientist or senior data analyst roles, focusing on extracting insight from large datasets.
• Machine learning or AI engineer positions, developing and deploying models into production.
• Data engineering roles building and maintaining data pipelines and scalable systems.
• Specialist roles in NLP, computer vision or predictive analytics within sectors such as healthcare, finance, manufacturing, retail and the public sector.
• Consultancy and advisory roles helping organisations adopt AI responsibly and effectively.
• Progression to research or doctoral study for those interested in advanced study and innovation.

The programme’s practical focus, project experience and links with industry help graduates demonstrate applied skills to employers, and the University’s careers and placement services support job search, CV development and interview preparation.

Why study at University of Bradford

The University of Bradford emphasises applied learning and employer engagement, making it well suited to practitioners who want to build job-ready AI and analytics skills. The department offers access to computing labs, specialised software and high-performance resources for model training and data processing.

Students benefit from staff who combine academic research with industrial collaboration, guest lectures from sector professionals and opportunities to work on live projects with local and regional partners. The University also provides dedicated support services for postgraduate students including academic guidance, careers advice and options for industry placements or project sponsorships.

Overall, the MSc in Applied Artificial Intelligence and Data Analytics is structured to give you the technical depth, practical experience and professional awareness needed to apply AI responsibly and effectively in real-world settings.