GEOL50315: Data Analysis in Space and Time

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• To provide students with an understanding of data methods and tools used in the Earth and Environmental Sciences, with a particular focus on those used for analysing spatial and temporal datasets
• To provide experience of physical modelling of complex real-world systems
• To provide knowledge of, and the ability to apply, popular software packages currently used in industry settings.

• Spatial information systems
• Geostatistics
• Geographical Information Systems software
• Numerical analysis
• Inverse theory
• Time series analysis
• General and generalised linear models

Subject-specific Knowledge:

• By the end of this module, students should:
• Understand the systems for recording spatial data
• Understand how to solve forward and inverse physical models
• Develop statistical models of environmental data
• Appreciate the main Python and R packages for analysis of Earth and Environmental data and understand how to use them.

Subject-specific Skills:

• By the end of this module, students should:
• Be able to convert data between coordinate systems
• Be able to analyse time series data in both the time and frequency domains
• Be able to construct predictive time series models
• Be able to solve or invert physical models
• Be able to develop general and generalised linear models of continuous and discrete data
• 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 a series of data modelling exercises to demonstrate knowledge of techniques taught.

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

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