MSc Scientific Computing And Data Analysis (Financial Technology)

Gain an understanding of the mathematical principles behind modern financial markets, and elements of programming and communication in the context of the financial industry.

Advances in fields such as Physics, Engineering, Earth Sciences or Finance are increasingly driven by experts in computational techniques. Notably, people skilled to write code for the most powerful computers in the world and skilled to process the biggest data sets in the world can truly make a difference.

The MSc in Scientific Computing and Data Analysis 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
• Implementation and application of fundamental techniques in a domain specialisation (presently astrophysics, particle physics, financial mathematics, or earth and environmental sciences).

Through MISCADA’s FinMath specialisation, students will be introduced to the mathematical principles behind modern financial markets, and elements of programming and communication in the context of the financial industry. Financial mathematics draws on tools from probability theory, statistics, partial differential equations, and scientific computing, 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.

The FinMath specialisation has a very strong mathematical flavour and educates students who will later on work within the quantitative divisions of fintech companies. To succeed, you need a very strong mathematical background (BSc in Maths is recommended).

Why study this course?

The degree targets an audience with excellent technical skills (in particular mathematics and programming) and makes the students understand how modern scientific computing and data analysis tools work. The course is designed along five core educational aims:

1. Train the next generation of research-aligned data and computational scientists and engineers for the UK high tech sector; for this, they have to be equipped with a very solid understanding of the underlying computing technologies and methodologies
2. Equip students with the skills and knowledge to apply successfully for further higher education programmes (PhD) with a strong computing and data flavour in Durham or outstanding international institutions
3. Provide students with the opportunity to obtain a deep insight into the state-of-the-art in the application domain (specialisation) with respect to computational and data challenges
4. Enable students to bridge the widening gap between their specialisation’s application domains, big data challenges, and high-performance computing once they have mastered the course
5. Make students aware of the societal, economical and ethical responsibilities, opportunities and implications tied to massive data processing and compute power; this includes training on entrepreneurship.

Watch our course overview video (various languages) here!

Course Structure

The course is structured into five elements spanning three terms. In this course:

• you will obtain a strong baseline in methodological skills
• you will study selected topics from your chosen specialisation area with a strong emphasis on computational and data challenges.
• you can choose to put emphasis on data analysis or scientific computing
• you will do a challenging project either within the methodological academic departments (Mathematical Sciences or Computer Science), or within the specialisation area, or in close cooperation with our industrial partners
• you will acquire important professional skills spanning collaboration and project management, presentation and outreach as well as entrepreneurial thinking