Nottingham Trent University

What you'll study

This programme integrates core finance subjects with applied data analytics and computing. Teaching emphasises hands-on use of programming, databases and statistical methods to model financial problems and derive actionable insight.

• Foundations of Finance and Markets — financial instruments, markets, valuation and market microstructure to ground analytical work in real-world finance.
• Programming for Finance — practical coding skills (typically using Python and relevant libraries) for data ingestion, cleaning, visualisation and basic modelling.
• Financial Data Analytics and Machine Learning — supervised and unsupervised learning methods applied to time series, credit scoring, fraud detection and algorithmic trading.
• Databases and Big Data Technologies — relational and non-relational databases, SQL, data pipelines and use of cloud or distributed tools appropriate to large financial datasets.
• Risk Management and Regulatory Analytics — measurement and management of market, credit and operational risk, stress testing and regulatory reporting considerations.
• FinTech, Blockchain and Digital Payments — overview of emerging digital finance technologies and their commercial and regulatory impact.
• Quantitative Methods for Finance — probability, statistics and econometrics applied to financial modelling and inference.
• Professional and Research Project — a substantial individual project or dissertation that applies analytic methods to a finance-related dataset; options often include industry-linked projects or consultancy-style briefs.

Delivery typically combines lectures, computer lab sessions, seminars and project supervision. Assessment mixes coursework, practical coding exercises, group-based projects and the final dissertation or project.

Entry requirements

Typical applicants hold a UK upper second-class (2:1) honours degree or international equivalent in finance, economics, mathematics, statistics, computer science or a related discipline. Applicants with a lower second-class degree and relevant professional experience or strong quantitative demonstrable skills may be considered.

Applicants should be comfortable with quantitative methods and basic programming concepts; prior exposure to statistics and either some spreadsheet modelling or introductory coding is recommended. International applicants must meet the university's English language requirements required for postgraduate study (evidence such as IELTS or approved alternatives is normally requested).

Applications normally require a degree transcript, a CV, a personal statement outlining motivation and experience, and at least one academic or professional reference.

Career prospects

Graduates move into roles where financial understanding is combined with data skills. Typical destinations include:

• Financial data analyst or data scientist in banks, asset managers and hedge funds
• Quantitative analyst or modelling analyst
• Risk analyst or regulatory reporting analyst
• FinTech product analyst or analytics consultant
• Business intelligence analyst or commercial analytics roles in corporate finance teams

The programme’s applied project and industry-facing activities support networking with local and national employers, and graduates often progress to roles that lead to chartered or professional qualifications in finance, risk and analytics.

Why study at Nottingham Trent University

Nottingham Trent University delivers this programme through its business and computing faculties with an emphasis on employability and applied learning. Students benefit from practical computing facilities, specialist data teaching spaces and access to financial and market data sources for coursework and projects.

The university has strong links with regional and national employers in financial services and FinTech which feed into guest lectures, project briefs and careers support. The course is designed to equip students with immediately applicable technical skills and a portfolio of applied work to demonstrate to recruiters.