Bournemouth University

What you'll study

This programme blends foundational modules in statistics, programming and data engineering with specialist AI topics. Core subjects commonly include:

• Programming for Data Science – practical Python (and sometimes R) skills, software engineering practices, version control and reproducible workflows.
• Statistical Methods and Data Analysis – probability, inferential statistics, experimental design and hypothesis testing for data-driven decision making.
• Machine Learning and Deep Learning – supervised and unsupervised learning, neural networks, model selection and evaluation, and practical use of ML frameworks.
• Big Data and Cloud Computing – scalable data processing, distributed systems, databases (relational and NoSQL), and cloud-based deployment patterns.
• Natural Language Processing and Computer Vision – applied modules that cover text analytics, language models, image processing and convolutional architectures.
• Data Visualisation and Communication – techniques and tools for communicating insights to technical and non-technical audiences.
• Ethics, Governance and Responsible AI – legal, ethical and societal considerations, fairness and explainability in AI systems.

The programme culminates in a substantial dissertation or industry-focused project where you apply methods to a real dataset, implement a prototype or conduct original research under academic supervision. Teaching methods typically include lectures, practical labs, group work, and guest lectures from industry partners.

Entry requirements

Applicants usually need a UK first or second class honours degree (1st or 2:1/2:2) in a numerate discipline such as computer science, mathematics, statistics, engineering, physics or another subject with demonstrable quantitative and programming experience. Applicants with non-technical degrees may be considered if they can show relevant professional experience or prior learning in programming, statistics and mathematics.

Typical expectations include:

• Prior experience with programming (Python recommended) and basic data analysis.
• Understanding of core mathematical concepts such as linear algebra, calculus and probability.
• Proof of English language proficiency if you are an international applicant (for example, an appropriate IELTS or equivalent score as specified by the university).

Shortlisted applicants may be invited to provide a portfolio, take a short coding assessment, or attend an academic interview. Applicants with substantial relevant work experience are assessed on a case-by-case basis.

Career prospects

Graduates from this programme go into a wide range of technical and analytical roles across sectors. Common job titles include:

• Data Scientist or Senior Data Scientist
• Machine Learning Engineer
• Data Analyst or Business Intelligence Analyst
• AI Engineer or Applied AI Specialist
• Data Engineer or Big Data Developer
• Research Assistant or PhD candidate in AI and related fields

Alumni work in industries such as finance, healthcare, retail, media and creative technologies, tourism and public sector organisations. The programme’s emphasis on practical projects and industry engagement helps graduates move directly into technical roles or progress within their current organisations.

Why study at Bournemouth University

Bournemouth University offers an applied, career-focused approach with strong engagement between teaching staff and industry. The university’s Faculty of Science & Technology provides specialist computing and data labs, access to high-performance computing resources, and opportunities to work on live briefs with local and national employers.

The course is designed to balance theory and practice: you will gain hands-on experience with current tools and software used by professionals, supported by academics with research and industry links in data science and AI. The university also offers career and placement services to support employability, help build professional networks and prepare students for job applications and interviews.

Small-group teaching and project supervision help ensure personalised feedback on technical work, while collaborative modules develop the communication and project-management skills sought by employers in data-driven roles.