G5K609 Scientific Computing and Data Analysis MSc Postgraduate Taught 2021
Please note: 2020-21 courses may be affected by Covid-19 and are therefore subject to change due to the ongoing impact of Covid-19. Summaries of course-specific changes resulting from the impact of Covid-19 will be provided to applicants during August 2020.
For the latest information on our plans for teaching in academic year 2020/21 in light of Covid-19, please see www.durham.ac.uk/coronavirus
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, or financial mathematics).
Why study this course
This course will open doors for you, both in the industry as well as in further study, and aims to:
- Train the next generation of expert research-aware data and computational scientists and engineers for the global high tech sector, equipped with genuine understanding of the underlying computing technologies and methodologies
- Give you a deep insight into the state-of-the-art computational and data challenges in your chosen specialisation
- Enable you to bridge the widening gap between application domains, big data challenges and high-performance computing
- Prepare you to apply successfully for further higher education programmes (PhD) with a strong computing and data flavour in Durham or other world-leading institutions
- Make you 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 1-minute course overview video here!
Chinese version here
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
Course Learning and Teaching
The course is taught using a wide range of learning research-led and teaching methods:
- Practical classes/computer labs
- Independent study, research and analysis
- Project (dissertation) and coursework
- Group and individual presentations
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:
- GPGPU/heterogeneous architectures
- HPC systems with specialist software installations (such as performance analysis tools)
- GPU-based AI kit and data acquisition tools
Subject requirements, level and grade
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 any natural sciences with a strong quantitative element.
We strongly encourage students to sign up for a specialisation area for which they already have some background or affinity. At the moment, the course targets primarily Physics students and Mathematics 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 at least one programming language and commitment to learning C and Python independently if not known before.
Interest in Computational Physics or its Data Analysis. The course tackles computational and data analysis challenges from this area.
The course page provides self-assessment tests and tutorial links to assess your programming skills. We expect applicants to confirm themselves that they are aware of the required programming skills and provide evidence (course transcripts, links to programming projects or brief description of conducted projects).
English Language requirements
Please check requirements for your subject and level of study.
How to apply
Fees and Funding
Full Time Fees
|EU Student||£26,500.00 per year|
|Home Student||£11,660.00 per year|
|Island Student||£11,660.00 per year|
|International non-EU Student||£26,500.00 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.
Scholarships and funding
Department of Computer Science
For further information on career options and employability, including the results of the Destination of Leavers survey, student and employer testimonials and details of work experience and study abroad opportunities, please visit our employability web pages.
Open days and visits
Pre-application open day
Overseas Visit Schedule
Postgraduate VisitsPGVI or
Department of Computer Science
The Department of Computer Science offers postgraduate courses that are challenging and technologically relevant, covering topics including big data, computer graphics, computer vision, image analysis, the Internet and the mathematical foundations of computing. You will have access to extensive and diverse research facilities, for example a Tier-3 supercomputer, a visualisation suite, several general-purpose computing on graphics processing units clusters and workstations, autonomous cars, and a team of intelligent robots. We have strong links with industrial partners; recent graduates have become successful entrepreneurs and software developers, have gained prestigious positions in banking and finance, and have entered the IT and engineering industries.
Ranked joint 1st in the UK for Internationally Excellent or World-Leading research impact in REF 2014.