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.