Scientific Computing and Data Analysis (Financial Technology)

Entry requirements

All streams require a 2:1 BSc (Honours) degree or international 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 encourage applicants to select a specialization area that aligns with their background. Please note that standard business degrees do not provide the necessary mathematical foundation.

Additional requirements

• Applicants must demonstrate strong programming skills in at least one compiled language, preferably C or C++, although Rust, Java, C#, Fortran, or Pascal are also acceptable. Proficiency in Python may suffice if the applicant has a strong background in their chosen specialization. Those lacking experience in C or C++ are advised to enrol in our pre-sessional course.
• Additionally we require knowledge of undergraduate-level mathematics, covering linear algebra, calculus, integration, ordinary and partial differential equations, and probability theory.
• Please see the University guidance for information on required English language levels.

Months of entry

September

Course content

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, Engineering, AI Platforms, Computer Vision and Robotics, or Environmental and Geographic Information Systems)

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.

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.

Year 1 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.

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.

Advanced Financial Technologies

develops your understanding of financial technologies, with a particular emphasis on the practical implementation of innovations such as distributed ledgers, smart contracts, and decentralized finance. In this module, you will also gain hands-on experience in designing and deploying financial infrastructures

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

Information for international students

If you are an international student who does not meet the requirements for direct entry to this degree, you may be eligible to take a pre-Masters pathway programme at the Durham University International Study Centre.

Fees and funding

For further information see the course listing.