Bournemouth University

What you'll study

This programme covers the foundations of computing, statistics and mathematics alongside specialist modules in machine learning, artificial intelligence and data engineering. Teaching is project-led and emphasises applied skills: programming in Python and relevant libraries, database design and SQL, data visualisation, statistical modelling, and deployment of ML models.

• Year 1: core computing and maths for data science, introductory programming, basic statistics and probability, databases, and a skills module on professional practice and research methods.
• Year 2: intermediate machine learning, data mining, data engineering and big data concepts, optimisation and linear algebra for ML, applied data visualisation, and ethics and governance of AI. Small-group projects introduce end-to-end data workflows.
• Final year: advanced topics such as deep learning, natural language processing or computer vision (depending on elective choices), model interpretability and fairness, cloud-based deployment, and a substantial individual honours project or industry-led capstone.
• Optional placement or sandwich year: students can usually take a professional placement or industry internship between years to gain workplace experience and apply learning to real-world problems.

Assessment typically combines coursework, individual and group projects, practical coding labs, presentations and a final project. The course emphasises reproducible workflows, version control, unit testing for data pipelines, and communicating technical results to non-specialist audiences.

Entry requirements

Bournemouth University looks for applicants with a strong interest in mathematics, computing or a related subject. Typical offers require completion of secondary qualification equivalent to A-levels, often including mathematics and/or a computing-related subject. Alternative qualifications such as BTEC, T-level, Access to Higher Education diplomas or recognised international equivalents are considered.

• GCSE-level passes in English and mathematics (or recognised equivalents) are normally required.
• Applicants without traditional qualifications but with relevant work experience or a portfolio of programming/data projects may be considered through contextual admissions or interview.
• International applicants must demonstrate English language proficiency through an accepted English test or qualification equivalent to UK requirements.

Career prospects

Graduates are prepared for a broad range of technical and analytical roles. Common career destinations include data scientist, machine learning engineer, data engineer, data analyst, AI consultant and business intelligence developer. Graduates also move into specialist areas such as natural language processing, computer vision, healthcare analytics, finance analytics and roles in public sector data teams.

Because the course emphasises applied projects and professional practice, students frequently progress into graduate schemes, technology start-ups, consultancies and in-house analytics teams. The optional placement year and industry-linked projects enhance employability and give students experience of working with real-world datasets, cloud platforms and production pipelines.

Why study at Bournemouth University

Bournemouth University combines a focused, vocational approach with opportunities for research-led teaching and industry engagement. The programme is delivered with a strong emphasis on hands-on labs, live projects and industry guest speakers, helping you develop practical skills sought by employers.

• Practical facilities and computing labs that support data analysis, machine learning experiments and cloud deployment.
• Opportunities for professional placements, industry projects and collaborations that build a graduate-level portfolio.
• A curriculum that includes ethical, legal and societal aspects of AI to prepare you to build responsible, explainable systems.
• Personalised support from academic staff with expertise in data science, AI and interdisciplinary applications, plus careers and enterprise services to help with CVs, interviews and employer engagement.