GEOL50215: Data Science Applications in Earth Sciences

• None

• None

• None

• To provide students with an understanding of the applications of data science in the Earth and Environmental Sciences
• To provide students with experience of handling, amalgamating and analysing diverse Earth and Environmental datasets from a range of sources and across a range of spatial and temporal scales
• To provide students with experience of using datasets to address problems at the forefront of Earth and Environmental Sciences, across a range of topics
• To provide knowledge of, and the ability to apply, popular software packages currently used in industry settings.

• The content will be based around topics including but not restricted to:
• Geophysics and inverse theory application
• Active remote sensing (LIDAR and radar)
• Passive (multispectral) remote sensing
• Environmental time series (e.g. river flows and water quality)
• Data camp using field, drone and satellite observation

Subject-specific Knowledge:

• By the end of this module, students should:
• Understand the systems for collecting, handling and plotting spatial data
• Understand how to apply physical models to understand environmental systems.
• Understand the spectrum of remote sensing techniques and Earth observation products
• Understand the use of archived data
• Appreciate the main software packages for collation and analysis of environmental data.

Subject-specific Skills:

• By the end of this module, students should: Be able to download and manipulate Earth Observation products Be able to process data coming from a range of archived sources Be able to collate and use data from a range of sources and across a range of spatial scales Be able to use standard software packages to develop models and solve problems

Key Skills:

• Effective written communication
• Planning, organising and time-management
• Problem solving and analysis

• Learning outputs are met through classroom-based workshops, supported by online resources. The workshops consist of a combination of taught input, case studies, discussion and computing labs. Online resources will typically consist of directed reading and a programming environment with example code.
• The summative assessment will be based upon: an individual written report (with code where necessary) on the analysis of a given data set with options supplied from each of the topics covered, an individual report of data collected as part of a group exercise within the data camp and a group presentation.

The formative assessment consists of classroom-based exercises on specific data topics of relevant to the learning outcomes of the modules. Oral feedback will be given on a group and/or individual basis as appropriate.

If you have a question about Durham's modular degree programmes, please visit our Help page. If you have a question about modular programmes that is not covered by the Help page, or a query about the on-line Postgraduate Module Handbook, please contact us.

Prospective Students: If you have a query about a specific module or degree programme, please Ask Us.

Current Students: Please contact your department.