Financial Data Analytics

Entry requirements

A UK 2:2 degree (or international equivalent) in a quantitative subject such as Mathematics, Statistics, Physics, Engineering, Computer Science, Quantitative Economics, or a closely related discipline. Applicants with degrees in other subjects may be considered on a case-by-case basis if they can demonstrate sufficient quantitative background. Relevant professional experience in finance, data analytics, or a related field may be taken into account in support of an application.

Months of entry

January

Course content

Build the knowledge and skills to analyse and interpret financial data in a rapidly evolving global sector with our MSc Financial Data Analytics. You’ll combine a strong foundation in financial theory with cutting-edge techniques, including machine learning, AI-driven forecasting, and computational methods using R and Python.

You’ll learn through a research-informed, practice-oriented approach using real financial datasets. The programme prepares you for careers in financial services, fintech, risk management, or further doctoral study.

Programme Highlights

• Build a strong understanding of financial markets, instruments, and emerging asset classes, supported by advanced analytical tools.
• Develop in-demand technical expertise in machine learning, forecasting, and computational modelling using R and Python.
• Apply what you learn to real financial challenges through coding tasks, applied projects, and industry-relevant assessments.
• Gain practical experience working with complex datasets, from data collection and management to analysis and visualisation.
• Explore key trends shaping the future of finance, including artificial intelligence, digital assets, and sustainability.

What you'll study
On this programme, you’ll gain a strong combination of financial knowledge, advanced data analytics skills, and hands-on experience working with real-world financial data.

You’ll start by developing a solid understanding of financial data analytics and financial instruments and markets, helping you understand how financial systems operate and how data supports decision-making in practice.

At the same time, you’ll develop practical skills in working with complex financial data. You’ll learn how to store, manage, and visualise datasets, preparing you to handle the types of large-scale data used across financial services and fintech.

As you progress, you’ll deepen your analytical expertise through modules in computational statistics using R, machine learning with Python, and AI-driven forecasting. These will equip you to identify patterns, build predictive models, and evaluate their effectiveness in financial contexts.

You’ll also explore areas at the forefront of the industry, such as digital and real asset analytics, giving you insight into emerging developments including digital assets, sustainability, and innovation in finance.

The programme takes a practical, application-driven approach to learning. You’ll work with real financial datasets, complete coding tasks, and take part in applied projects that mirror professional practice.

By the time you graduate, you’ll have the confidence to combine financial theory with advanced data analytics to tackle complex, real-world problems.