University of Nottingham

What you'll study

This programme blends core subjects in statistics, optimisation and machine learning with applied modules that focus on business decision‑making and implementation. Typical topics include data mining and predictive modelling, supervised and unsupervised learning, natural language processing, deep learning, big data technologies, optimisation and simulation, data visualisation and dashboarding, and the governance and ethics of AI.

• Core analytical methods and machine learning foundations
• Big data architectures and practical tools (programming in Python/R, databases, cloud computing concepts)
• Prescriptive analytics: optimisation, simulation and decision analysis
• Business-focused modules: strategy for analytics, project management and change management for data projects
• Responsible AI, data privacy and ethical issues in deployment
• Individual or group major project (dissertation, industry project or consultancy assignment) applying analytics/AI to a real business problem

Teaching typically combines lectures, hands‑on labs, case studies and group work. Assessment is through a mix of coursework, practical projects, presentations and a substantial independent project that demonstrates the ability to translate technical results into business value.

Entry requirements

Applicants are normally expected to hold a good honours degree in a quantitative or numerate discipline (for example mathematics, statistics, economics, engineering, computer science, or a related subject). Candidates with non‑standard backgrounds who can demonstrate strong quantitative skills through prior study, professional experience or conversion courses may also be considered.

• Evidence of competence in mathematics, statistics and programming is important; prior modules or professional experience in these areas strengthen an application.
• Applicants whose first language is not English must meet the University’s English language requirements; acceptable proof of proficiency is required at the point of offer.
• The selection process may take account of academic transcripts, references, a personal statement outlining motivations and experience, and where relevant an interview.

Career prospects

Graduates move into roles that require both technical expertise and commercial awareness. Common destinations include data scientist, machine learning engineer, business or analytics consultant, data analyst, AI product manager and analytics manager roles across industries such as finance, consulting, retail, healthcare, manufacturing and the public sector.

The programme prepares students to design models, build and deploy analytics solutions, interpret results for senior stakeholders, and lead data‑driven change. Alumni also go on to pursue doctoral research in data science, AI or related fields.

Why study at University of Nottingham

The University of Nottingham offers this programme through its Business School with close links to computer science and engineering research groups, providing a balance of business context and technical rigour. Students benefit from access to computing facilities, specialist analytics software and opportunities for industry projects and placements facilitated by the School’s employer partnerships.

Support services include dedicated careers and employability teams that run employer events, skills workshops and one‑to‑one guidance to help students secure roles in industry. The University’s international community and campus environment also provide strong networking opportunities and a collaborative learning experience.