Scientific Computing and Data Analysis (Earth and Environmental Sciences)

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

Core modules:

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. The module includes a field trip in which students can gather geospatial data and learn how to process it on the fly. The module culminates in a mini project.

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