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GEOL50315: Data Analysis in Space and Time

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Type	Tied
Level	5
Credits	15
Availability	Available in 2023/24
Module Cap	None.
Location	Durham
Department	Earth Sciences

Prerequisites

None

Corequisites

None

Excluded Combinations of Modules

None

Aims

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.

Content

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

Learning Outcomes

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

Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module

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.

Teaching Methods and Learning Hours

Activity	Number	Frequency	Duration	Total
Lectures	8	2 times per week (Term 1, weeks 6-9)	1 hour	8
Workshops	8	2 times per week (Term 1, weeks 6-9)	2 hours	16
Surgery	12	3 times per week (Term 1, weeks 6-9)	1 hour	12
Preparation and reading				114
Total				150

Summative Assessment

Component: Assignment		Component Weighting: 100%
Element	Length / Duration	Element Weighting	Resit Opportunity
Individual written assignment based on data problem	2000 words maximum	100

Formative Assessment

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.

More information

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