Course summary

Developments in fields such finance, physics and engineering are increasingly driven by experts in computational techniques. The financial services sector has always been at the forefront of data analytics, and those with the skills to write code for the most powerful computers in the world and to process the biggest data sets can give a company a competitive edge. Our suite of Masters in Scientific Computing and Data Analysis (MISCADA) offers an application-focused course to deliver these skills with three interwoven strands: Computer Science underpinnings of scientific computing (algorithms, data structures, implementation techniques, and computer tool usage) Mathematical aspects of data analysis and the simulation and analysis of mathematical models Implementation and application of fundamental techniques in an area of specialisation (as well as Financial Technology we offer options in Astrophysics, Computer Vision and Robotics, or Earth and Environmental Sciences) The MISCADA specialist qualification in Financial Technology introduces you to the mathematical principles behind modern financial markets, and elements of programming and communication in the context of the financial industry. Financial technology draws on tools from probability theory, statistics and mathematical modelling, and is widely used in investment banks, hedge funds, insurance companies, corporate treasuries and regulatory agencies to solve such problems as derivative pricing, portfolio selection and risk management. You can find out more here. There’s great synergy between the modules and you will be given plenty of opportunities to put your learning into practice from the start of the course. Our research-led approach allows you to take some of the newest theoretical ideas and directly translate them into working codes in their respective application areas. If you have an undergraduate degree in a science subject with a strong quantitative element, including computer science and mathematics and want to work at the highest level in financial technology, either in academia or in industry, then this could be the course you’re looking for. Course Structure Core modules: Introduction to Machine Learning and Statistics provides knowledge and understanding of the fundamental ideas and techniques in the application of data analysis, statistics and machine learning to scientific data. Introduction to Scientific and High Performance Computing provides knowledge and understanding of paradigms, fundamental ideas and trends in High Performance Computing (HPC) and methods of numerical simulation. Professional Skills provides training in areas such as collaborative coding, project management and entrepreneurship. It will build the skill you need to communicate novel ideas in science, and reflect on ethical issues around data and research. The Project is a substantive piece of research into an area of financial technology, scientific computing or data analysis, or a related area in cooperation with an industry partner. The project will develop your research, analysis and report-writing skills. Financial Technology: Algorithmic Trading and Market Making in Options develops your knowledge of financial theory, with a particular emphasis on asset valuation, portfolio management and derivative pricing. In this module you will also develop a critical understanding and appreciation of current research in financial theory and its applications to professional practice. Financial Mathematics introduces the mathematical theory of financial products and provides advanced knowledge and critical understanding of the pricing of financial products and derivatives.