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.
Months of entry
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.
Our suite of Masters 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 and the simulation and analysis of mathematical models
- 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. You can find more details about the course here.
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:
- 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
- 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
- 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
- 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
- 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!
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
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.
Qualification, course duration and attendance options
- full time12 months
- Campus-based learningis available for this qualification
Course contact details