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Degree type

MSc

Course length

1 year full-time

Location

Durham City

Program code

G5T109

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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.

Course details

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).

Through MISCADA’s Earth & Environment domain specialisation, we seek to provide the advanced knowledge of how to use sophisticated datasets and tools to tackle cutting-edge and societally-relevant problems relating to the Earth’s environment and management of its scarce resources. We introduce a variety of Earth and Environmental datasets, as well as to the specialist mathematical and software tools required for their quantitative and computational analysis.

Why study this course

The course 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-affine 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

Learning

The course is taught using a wide range of learning research-led and teaching methods:

  • Lectures
  • 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

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.

For more information including self-assessment tests and tutorial links to assess your programming skills.

English language requirements

Fees and funding

Full Time Fees

Tuition fees
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.

Scholarships and Bursaries

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

Career opportunities

Computer Science

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.

Department information

Computer Science

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).

Rankings

  • 95% of our graduates leave with excellent career prospects. The Complete University Guide Graduate Prospect Score 2020.
  • 5th in The Complete University Guide 2021.

Staff

For a current list of staff, please see the School's web pages.

Facilities

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.

Visit Us

The best way to find out what Durham is really like is to come and see for yourself!

Postgraduate Open Day
  • Date: 24/11/2021
  • Time: 09:00 - 17:00
Register for open day
Discover Durham Tours
  • Date: 25/10/2021
  • Time: 13:30 - 16:00
Register for open day