Scientific Computing and Data Analysis (Astrophysics)
Address some of the biggest research questions in fundamental science.
12 months full-time
12 months full-time
Please note: Courses may be affected by Covid-19 and are therefore subject to change due to the ongoing impact of Covid-19. Applicants will be informed of any changes which we are required to make to course entries as a result of Covid-19.
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:
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 the dark matter and dark energy. The courses include stellar structure and evolution, galaxy formation, large-scale structure and simulations of structure formation.
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:
Watch our course overview video (various languages) here!
The course is structured into five elements spanning three terms. In this course:
The course is taught using a wide range of learning research-led and teaching methods:
A detailed list of learning and teaching methods is found per module in the module descriptions.
Besides the formal characteristics clarified in these descriptions, students from the course will be given the opportunity to work with a wide variety of top-notch computer kit and software:
A UK first or upper second class honours degree (BSc) or equivalent
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.
Programming knowledge on an graduate level in both C and Python is required.
|Home students||£12,100 per year|
|EU students||£27,500 per year|
|Island students||£12,100 per year|
|International students||£27,500 per year|
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).
Please also check costs for colleges and accommodation.
We are committed to supporting the best students irrespective of financial circumstances and are delighted to offer a range of funding opportunities.Find out more about Scholarships and Bursaries
For further information on career options and employability, student and employer testimonials and details of work experience and study abroad opportunities, please visit our employability web pages.
Our courses cover topics including data analysis, machine learning, big data, advanced algorithms, scientific computing and business analytics. You will have access to extensive and diverse research facilities, for example a Tier-3 supercomputer, several smaller compute clusters with various architectures, a visualisation suite, GPGPU machines, and large-scale data acquisition kit (telescopes, for example).
We have now moved into a purpose-built £40M new building at Upper Mountjoy in Durham, which is indicative of the major investment the University is making in Computer Science. The building will also include colleagues from Mathematical Sciences and will mean we can further develop joint teaching and research strategies.
The building has academic offices, offices for research staff and students, open-plan space for undergraduate students to work, breakout spaces to collaborate, labs, computer rooms and, of course, a café.
Learn more about our facilities and equipment.