杜倫大學(xué)

Course Summary

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.