Scientific Computing and Data Analysis (Earth and Environmental Sciences)
Using the latest scientific computing and data analysis techniques, learn to predict and analyse aspects of climate change, natural hazards and natural resource management.
Developments in fields such physics, engineering, Earth sciences or finance are increasingly driven by experts in computational techniques. Those with the skills to write code for the most powerful computers in the world and to process the biggest data sets in the world have the potential to make a positive impact on issues relating to the Earth and its environment.
Our suite of Masters in Scientific Computing and Data Analysis (MISCADA) 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 an area of specialisation (as well as Earth and Environmental Sciences we offer options in Astrophysics, Computer Vision and Robotics, or Financial Technology)
The MISCADA specialist qualification in Earth and Environmental Sciences is designed to equip you with advanced knowledge and skills in the use of 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 out more here.
There’s great synergy between the modules and you will be given plenty of opportunities to put your learning into practice from the start of the course, including analysis of data across a range that includes satellites and handheld devices. Our research-led approach allows you to take some of the newest theoretical ideas and directly translate them into working codes in their respective application areas. If you have an undergraduate degree in a science subject with a strong quantitative element, including computer science and mathematics and want to work at the highest level in earth and environmental sciences, either in academia or in industry, then this could be the course you’re looking for.
Course Structure
Introduction to Machine Learning and Statistics provides knowledge and understanding of the fundamental ideas and techniques in the application of data analysis, statistics and machine learning to scientific data.
Introduction to Scientific and High Performance Computing provides knowledge and understanding of paradigms, fundamental ideas and trends in High Performance Computing (HPC) and methods of numerical simulation.
Professional Skills provides C refresher training with an outlook into large-scale code usage. You will also develop wider professional skills in areas such as entrepreneurship, intellectual property and build the skill you will need to communicate novel ideas in science, and reflect on ethical issues around data and research.
The Project is a substantive piece of research into an unfamiliar area of Earth and environmental sciences, scientific computing or data analysis, or a related area in cooperation with an industry partner. The project will develop your research, analysis and report-writing skills.
Earth and Environmental Sciences introduces a variety of Earth and environmental, and geospatial datasets and the specialist mathematical and software tools required for their quantitative and computational analysis. The module also provides advanced knowledge of how to use these datasets and tools to tackle cutting-edge and societally-relevant problems.
Plus optional modules which may include:
Advanced Statistical and Machine Learning: Foundations and Unsupervised Learning
Advanced Statistics and Machine Learning: Regression and Classification
Data Acquisition and Image Processing
Performance Modelling, Vectorisation and GPU Programming
Advanced Algorithms and Discrete Systems
Computational Linear Algebra and Continuous Systems
This degree is organised by the Department of Computer Science with specialisations offered in collaboration with the Department of Earth Sciences, the Department of Mathematical Sciences, the Business School and the Department of Physics. Teaching and learning methods are varied, they include a combination of lectures, practical classes/computer labs, field work, independent study, research and analysis, a project (dissertation) and coursework. Some modules also include group and individual presentations.
You will also be given the opportunity to work with a wide variety of high-quality computer kit and software. This includes HPC systems such as GPU clusters, systems with heterogeneous architectures and specialist software installations (such as performance analysis tools), AI tools and data acquisition tools.
Assessment
Assessment takes a combination of forms including coursework, presentations and a project which is worth one-third of your total mark. You will complete your dissertation-style project on a topic of your choice from within the methodological academic departments (Mathematical Sciences or Computer Science), or within the Earth and environmental sciences, or in close cooperation with our industrial partners.
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).
Qualifications in computer science are highly sought after by employers across the globe and an award from our Department provides the academic skills, industry insight and research-informed approach that sets postgraduates up for careers in a broad range of sectors.
Many postgraduates have gone on to work as software engineers, analysts, consultants, programmers and developers. Some have founded their own start-ups or work in leading software companies, high-technology consultancies, banking and finance, retail, engineering, the communications and IT industry.
The Department has strong research links, spanning both industry and government, including the automotive sector with Jaguar Land Rover and Renault, the defence and security sector with QinetiQ and Boeing, with government in the Civil Service and at GCHQ and in the manufacturing sector with Procter & Gamble. Other high-profile employers include BAE Systems, Google and BT.
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
The Department is at the heart of the fast-paced world of applications and algorithms. We maintain an in-depth understanding of the fundamentals of computation and are fully up to speed with the latest technologies that emerge at an ever-increasing rate.
Learning from academics who lead cutting-edge research provides valuable insight into high quality projects, and gives our postgraduate community the opportunity to play a role in shaping a future in which crucial developments in society are supported by technological innovation.
Taught courses balance fundamental knowledge and an emphasis on programming and mathematical skills with practical applications. The content and structure are such that they suit postgraduates who already have experience in the industry or other employment and want to add a formal qualification to their achievements.
Researchers in the Department offer a range of expertise across the computer science spectrum in areas such as artificial intelligence, data science, bioinformatics, high-performance computing, graphics and fundamental algorithms.
We ensure our research-led activity does not function in isolation and keep close links with local high-technology industries as well as national and international employers. Those relationships ensure we are at the leading edge of developments across the sector and can revise and adapt the Department’s curriculum to reflect the changes.
Rankings
4th in The Complete University Guide 2024
Top 10 inThe Guardian University Guide 2024
Top 10 in The Times and Sunday Times University Guide 2024
97% of our research outputs are world-leading or internationally excellent (REF 2021)
Facilities
The Department is located in a £40 million purpose-built building in the heart of Durham at Upper Mountjoy and features open-plan work areas, breakout spaces for collaboration projects, laboratories and computer rooms.
We are fortunate to have supercomputers for High-Performance Computing and for data analysis and machine learning as well as access to several visualisation and data postprocessing laboratories.
We are also able to host local computer hardware which give postgraduate researchers a safe environment to test prototype solutions, explore innovative technologies they are developing or to actually design new solutions.
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