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, or financial mathematics).
Through MISCADA’s Earth & Environment domain specialisation, we seek to provide 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 the specialist mathematical and software tools required for their quantitative and computational analysis. You can find more details about the course here
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:
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
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!
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
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.
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).
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
Joint Number 1 in the UK for student employability in The Complete University Guide 2023
6th in The Complete University Guide 2023
6th inThe Guardian University Guide 2023
8th in The Times and Sunday Times University Guide 2023
97% of our research outputs are world-leading or internationally excellent (REF 2021)
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.
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