Taught course

Scientific Computing and Data Analysis (Astrophysics)

Durham University · Department of Computer Science

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 a graduate level in both C and Python is required.

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


Course content

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)
  • Replace with: 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).

MISCADA’s Astrophysics specialisation aims to equip students with the background needed to address some of the biggest research questions in fundamental science, such as how we can use large surveys and supercomputer simulations to probe the nature of dark matter and dark energy. The courses include stellar structure and evolution, galaxy formation, large-scale structure and simulations of structure formation.

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:

  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

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

UK students
£12,750 per year
International students
£28,750 per year

For further information see the course listing.

Qualification, course duration and attendance options

  • MSc
    full time
    12 months
    • Campus-based learningis available for this qualification

Course contact details