This page is for the academic year 2021-22. The current handbook year is 2022-23
Department: Earth Sciences
Earth and Environmental Sciences
||Available in 2021/22
||G5T109 Scientific Computing and Data Analysis (Earth and Environmental Sciences)
Excluded Combination of Modules
- To provide an introduction to a variety of Earth and Environmental and geospatial datasets, including remotely-sensed satellite imagery, and to the specialist mathematical and software tools required for their quantitative and computational analysis.
- To provide advanced knowledge of how to use these datasets and tools to tackle cutting-edge and societally-relevant problems relating to the study of the Earth’s physics and chemistry.
- This module will equip students with the necessary specialist mathematical and software tools to handle, manipulate, visualise and analyse geospatial datasets.
- This will include developing understanding and experience of spatial and geospatial reference systems, geostatistics and Geographical Information Systems software.
- This module will also introduce Earth and Environmental Sciences datasets and cutting-edge problems through a series of detailed topics, each focussed on one or more key data streams or types, including but not limited to geophysical data or model outputs, remotely-sensed satellite data, and Environmental time-series.
- Each topic will feature an introduction to the Earth Sciences context, background, and theory underpinning the key data streams for that topic, an in-depth examination of data collection, handling and processing, and a discussion of unique considerations, limitations and strengths of the individual datasets. Each topic will also highlight a variety of diverse current and societally-relevant problems the data can be used to address.
- Students will have an opportunity to choose one of these topics to investigate further through an independent summative mini-project.
- Class-based teaching in this module is supplemented by a Data Camp; a short field course focussed on acquisition of data in the field from a variety of sources (e.g. individual sensors or drones), followed by processing of these datasets and integration and joint analysis with supplementary datasets across a diverse range of scales (e.g. satellite data, national, international sensor networks).
- Knowledge and understanding of Earth and Environmental and geospatial datasets, including remotely-sensed satellite data and field data.
- Knowledge and understanding of mathematical and software tools for handling, visualising, analysing and modelling these datasets.
- Knowledge and understanding of select topics of active research in Earth and Environmental Science.
- Specialised and advanced computational and mathematical skills for handling, visualisation, analysis and modelling of geospatial and remotely-sensed datasets
- Intellectual and practical skills necessary to synthesise and integrate information/data acquired from a variety of sources and at a variety of scales.
- Intellectual and practical skills necessary to use Earth and Environment data and advanced methodologies for the solution of complex, novel, specialised and unfamiliar problems.
- Intellectual and practical skills necessary to plan, conduct and report on field projects.
- Presentation skills
- Team working
- Problem solving, written presentation of an argument
- Ability to learn actively and reflectively, to develop intuition, and the ability to tackle unfamiliar and complex new material
- Develop an adaptable and flexible approach to study and work.
Modes of Teaching, Learning and Assessment and how these contribute to
the learning outcomes of the module
- This module will be delivered through a series of flexible 3 hour sessions comprising both lectures and practicals, supported by surgeries.
- The core teaching will also be supplemented by a Data Camp field course.
- The practicals form an important component of the module allowing "hands on" learning and experience.
- Summative assessment is made up of a practical test 20%, a mini-project based on a topic of choice 60% and a group project based on data camp 20%.
Teaching Methods and Learning Hours
|Self-Study and Reading
|Component: Continual Assessment
||Component Weighting: 100%
||Length / duration
|Mini-project based on topic of choice
|Group project based on data camp
■ Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University