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Financial Mathematics with Data Science MSc

Develop your quantitative, mathematical, and computational skills ready for a career in the modern financial sector.

Gain skills sought after by the finance industry

Finance is a dynamic industry, with fast-growing areas like FinTech driving innovation. Advances in machine learning and increased availability of data are allowing organisations to make better decisions and improve their services, and skills in data science are being increasingly sought after by industry.

Our new course will reinforce your mathematical skills across a wide range of topics. You'll gain a broad education in the mathematical and data science methods relevant to modern financial institutions, equipping you with the quantitative skills desired by employers. You’ll also develop a practical and theoretical understanding of the mathematics behind machine learning and data science tools, and software skills to successfully implement them.

Course highlights

  • Increase your employability with the numerical and computational knowledge desired by financial institutions
  • Be introduced to cutting-edge machine learning and data science topics and gain the programming skills to successfully implement solutions using these tools
  • Benefit from the experience of practitioners and experts, and see how mathematical methods and data science tools are used in real-world settings
  • Apply your practical knowledge in a range of coursework activities and receive personalised support for your learning through our regular drop-in sessions
  • Carry out an investigative research project exploring a problem of importance to the financial industry
A black screen showing financial figures in red and green.

Increase your employability

Mathematics and data science are fundamental to emerging sectors and many industries are looking for graduates who can work across these fields. By gaining further skills and knowledge in these areas, a wide range of career pathways will be open to you when you graduate. You'll also have acquired the essential foundation for further postgraduate study and research within related fields.

Recent graduates from the Department are working in a range of roles at:

  • J.P. Morgan
  • Allianz
  • The Bank of America
  • and a wide range of specialised FinTech, Data Science and Financial Services companies, such as Shift Technology, Creditsights, Zopa and Fincad
An image showing a hand resting on a laptop. The screen displays a chart depciting financial data

Course structure

The year is split into two taught semesters and an in-depth investigative research project

Throughout the course, you will study a broad selection of units giving you a solid understanding of the most up-to-date mathematical and data science methods used by modern financial institutions. The units covered in the course include, but are not limited to:

  • Advanced mathematics and data science techniques for finance: This unit will explore contemporary issues in finance, looking at recent examples of relevant mathematical or data science solutions to problems in the financial industry. These could include topics such as blockchain technologies, market microstructure problems and fraud detection.
  • Risk, randomness and optimisation: In this unit, you will gain a solid understanding of mathematical concepts such as probability, statistics and optimisation. The content will be discussed in the context of a range of important applications in finance such as utility maximisation, risk management, and insurance.
  • Applied machine learning: You’ll learn about machine learning algorithms, their implementation and applications. By the end of the unit, you'll be able to critically analyse and implement machine learning algorithms in Python, apply machine learning algorithms to real-world data, evaluate their performance, and write technical reports to summarise your findings.
  • Individual research project: You will also apply your mathematical and data science knowledge to investigate a problem of importance in the finance industry by completing an individual research project. You will carry out an in-depth investigation into a relevant topic and produce a written dissertation summarising existing research and analysing mathematical and data science techniques and their relevance to solving the problem. Support will be available throughout your project in the form of weekly drop-in sessions and scheduled meetings with your academic supervisor.

Entry requirements

Entry information for Financial Mathematics with Data Science MSc

You should have a first or strong second-class undergraduate degree or international equivalent.

To apply for this course, your undergraduate degree should be in a programme that incorporates substantial elements of both mathematics and computing such as mathematics, statistics, computer science, physics, chemistry, engineering or economics.

We will also consider other subjects, for example, geography or biology, which may meet the criteria depending on their specific mathematical and computing content.

We may make an offer based on a lower grade if you can provide evidence of your suitability for the degree.

If your first language is not English but within the last 2 years you completed your degree in the UK you may be exempt from our English language requirements.

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