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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.
Months of entry
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, Financial Mathematics, or Earth and Environmental Sciences (see G5T109).
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.
The course is structured into five modules spanning three terms and is currently available with a specialisation in astrophysics, particle physics, or financial mathematics.
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
Fees and funding
Scholarships available for 2022 entry will be determined in September 2021. Over 60 scholarships are available, each year. Some scholarships are awarded to more than one person. For further information see the course listing.
Qualification, course duration and attendance options
- full time12 months
- Campus-based learningis available for this qualification
Course contact details