University of Hull

What you'll study

This modern master's blends theoretical foundations with practical, hands-on experience. You will study modules that cover machine learning, deep learning, data mining, and probabilistic modelling alongside data engineering topics such as databases, big data processing and cloud-based deployment. The programme emphasises applied project work, where you will use real datasets and contemporary tools to solve authentic problems.

• Core topics: supervised and unsupervised learning, neural networks and deep learning architectures, reinforcement learning, statistical inference and Bayesian methods.
• Data engineering and systems: relational and NoSQL databases, data warehouses, ETL pipelines, distributed computing (e.g. Hadoop/Spark concepts) and cloud platforms for scalable analytics.
• Applied analytics: data visualisation, exploratory data analysis, time series analysis, natural language processing and computer vision applications.
• Professional and research skills: reproducible research, software engineering best-practices, ethical and legal considerations in AI, and project management for data science projects.
• MSc dissertation or industry project: an extensive independent project that applies AI and data science methods to a substantial problem, often undertaken with industry partners or drawing from research groups in the Faculty.

Teaching methods include lectures, lab sessions, coding workshops, group coursework and a supervised research or industry project. You will develop competence in programming (commonly Python and relevant libraries such as NumPy, pandas, scikit-learn, TensorFlow/PyTorch), SQL, and use of data platforms and version control systems.

Entry requirements

Applicants are normally expected to hold a good honours degree (or equivalent) in a relevant subject such as computer science, mathematics, engineering, statistics, or a related discipline. Demonstrable programming experience and knowledge of undergraduate-level statistics or linear algebra are usually required. Candidates with substantial professional experience in data-related roles but without a conventional degree are considered on an individual basis.

• Academic entry: an undergraduate degree at a 2:2 or above in a suitable subject is typical; higher classification may be required depending on applicant background and competition.
• Non-standard entry: applicants with relevant industry experience, coding bootcamp completion plus supporting evidence of quantitative skills may be considered.
• English language: international applicants whose first language is not English must meet the University of Hull's recognised English language requirements.
• Supporting materials: a CV, references and a personal statement outlining your motivation and any relevant project or work experience are required as part of the application.

Career prospects

Graduates are prepared for a wide range of roles in industry, the public sector and research. Typical job titles include data scientist, machine learning engineer, data engineer, AI specialist, business intelligence analyst and research assistant. The programme also provides a strong foundation for those wishing to pursue doctoral research in AI, machine learning or data science.

Hull's programme focuses on employability through practical projects and engagement with employers, helping students build a portfolio of applied work and technical proficiency valued by recruiters in finance, healthcare, manufacturing, retail, government and technology start-ups.

Why study at University of Hull

The University of Hull offers a supportive learning environment with accessible research-active staff working in computing and data-related disciplines. The School provides modern computing laboratories, cloud and high-performance computing resources, and opportunities to engage with interdisciplinary research groups.

Students benefit from a programme designed to balance theory and practice, frequent contact hours with academic supervisors, and links with local and national employers for placements or collaborative projects. Hull's campus and city provide a cost-effective base for study with a close-knit student community and career support services to help with internships, CV development and interview preparation.