Programming for data science
Credits: 12 (equivalent to 24 CATS credits)
Develop your skills in numerical programming for data science and machine learning applications. After completing this unit you will be able to implement and critically evaluate low-level data science functionality from scratch, and complex methodologies using relevant libraries. You will gain experience in methods and structures to help you handle, process and analyse large datasets, and have an understanding of sustainable software development.
Discrete time finance
Credits: 6 (equivalent to 12 CATS credits)
Develop tools to model important financial topics using discrete time methods. You will be introduced to key tools in probability and statistics and learn to apply them to various financial problems. Problems will include, among others, optimal investment in discrete time models, pricing and hedging of financial derivatives in a discrete time, and modelling of credit risk.
Applied machine learning
Credits: 6 (equivalent to 12 CATS credits)
Gain a general foundation in machine learning, covering a range of methodologies for supervised and unsupervised learning. By the end of the unit, you should be able to critically analyse and implement machine learning algorithms, apply them to real-world data, evaluate their performance, and write technical reports to summarise your findings.
Risk, randomness and optimisation
Credits: 6 (equivalent to 12 CATS credits)
This unit will provide you with a solid background in mathematical concepts and methods, in particular relevant concepts in probability, statistics and optimisation. You will learn how to utilise them in a range of important applications in Finance such as utility maximisation, risk management, and insurance.
Mathematics of machine learning
Credits: 6 (equivalent to 12 CATS credits)
Develop a deeper understanding of modern machine learning, focusing on the underlying mathematics and numerical realisation of neural networks. By the end of the unit, you should be able to critically analyse the mathematical formulation of deep learning algorithms, implement them computationally and appreciate the importance of algorithm efficiency.
Continuous time finance
Credits: 6 (equivalent to 12 CATS credits)
Complementing the discrete time models introduced in the previous semester, this unit will introduce you to continuous time models for random processes and how to apply them in a financial context. Potential topics covered will include derivative pricing in stock markets and for fixed income products, insurance modelling, and optimal investment.
Advanced mathematics and data science techniques for finance
Credits: 6 (equivalent to 12 CATS credits)
Learn about the contemporary issues facing the finance industry in this flexible unit. You’ll explore recent examples of relevant mathematical or data science solutions in the finance industry, and topics will include blockchain technologies, market microstructure problems and high-frequency trading. You will also discuss the ethical issues arising in the financial industry and the need for accurate and responsible modelling.
Monte Carlo methods for finance
Credits: 6 (equivalent to 12 CATS credits)
Develop an understanding around the theory and practice of Monte Carlo-based numerical methods for applications in finance. Monte Carlo methods are powerful and flexible methods allowing you to simulate complex modelling situations. This unit will highlight how they can be applied to a range of financial problems.
Research project preparation
Credits: 6 (equivalent to 12 CATS credits)
Develop the skill you need for writing scientific reports, papers and your final dissertation. In this unit, you will learn how to critically analyse and review previous work in a chosen subject area and conduct a detailed literature review. By the end of the unit, you’ll have developed a feasible project proposal for your summer dissertation and will understand the principles of structuring your dissertation.
Individual research project
Credits: 30 (equivalent to 60 CATS credits)
Develop your skills in problem-solving, research, data analysis and written presentation. During your individual research project, you will carry out and present an in-depth investigation into a topic relevant to Mathematics and/or Data Science, as applied to a problem of importance in the financial industry. Your project will be based on the reading and preliminary work you’ll have carried out in Semester 2 during the research project preparation unit.