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.
Plus optional modules which may include:
Advanced Statistical and Machine Learning: Foundations and Unsupervised Learning
Advanced Statistics and Machine Learning: Regression and Classification
Data Acquisition and Image Processing
Performance Modelling, Vectorisation and GPU Programming
Advanced Algorithms and Discrete Systems
Computational Linear Algebra and Continuous Systems
This degree is organised by the Department of Computer Science with specialisations offered in collaboration with the Department of Mathematical Sciences, the Business School, the Department of Physics and the Department of Earth Sciences. Teaching and learning methods are varied, they include a combination of lectures, practical classes/computer labs, independent study, research and analysis, a project (dissertation) and coursework. Some modules also include group and individual presentations.
You will also be given the opportunity to work with a wide variety of high-quality computer kit and software. This includes HPC systems such as GPU clusters, systems with heterogeneous architectures and specialist software installations (such as performance analysis tools), AI tools and data acquisition tools.
Assessment
Assessment takes a combination of forms including coursework, presentations and a project which is worth one-third of your total mark. You will complete your dissertation-style project on a topic of your choice from within the contributing academic departments. In the financial technology steam this will usually be Mathematical Sciences or Computer Science, or in close cooperation with on of our industrial partners.
Entry requirements
A UK first or upper second class honours degree (BSc) or equivalent
In Physics or a subject with basic physics courses OR
In Computer Science OR
In Mathematics OR
In Earth Sciences OR
In Engineering OR
In any natural sciences with a strong quantitative element.
We strongly encourage students to sign up for a specialisation area for which they already have a strong background or affinity. At the moment, the course targets primarily Physics, Earth Sciences and Mathematics (finances) students. If you do not have a degree from these subjects, we strongly recommend you to contact the University beforehand to clarify whether you bring along the right background.
Please note that standard business degrees are not sufficient, as they lack the required level of mathematical education.
Additional requirements
Programming knowledge on an graduate level in both C and Python is required.
Some undergraduate-level mathematics, covering linear algebra, calculus, integration, ordinary and partial differential equations, and probability theory.
There is a minimum SPEAKING requirement of IELTS 6.5/ TOEFL iBT 25/ Cambridge Scale 176/ Pearson Academic 62 for this course.
The tuition fees for 2025/26 academic year have not yet been finalised, they will be displayed here once approved.
The tuition fees shown are for one complete academic year of full time study, are set according to the academic year of entry, and remain the same throughout the duration of the programme for that cohort (unless otherwise stated).
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