Postgraduate Handbook 2026-2027
Module GEOL50315: Data Analysis in Space and Time
Department: Earth Sciences
GEOL50315: Data Analysis in Space and Time
| Type | Level | Credits | Availability | Module Cap | Location |
|---|---|---|---|---|---|
| Tied | 5 | 15 | Available in 2026/2027 | None. | Durham |
Tied To
- G5P123: Data Science (Earth and Environment) (see Postgraduate Handbook 2025-2026 for details)
- G5P723: Data Science (Environmental Data Science)
Pre-requisites
- None
Corequisites
- None
Excluded Combinations
- 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
- 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
- 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 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 Hours | Attendance Monitored |
|---|---|---|---|---|---|
| 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 | |
| Drop-In Classes | 12 | 3 times per week (Term 1, weeks 6-9) | 1 hour | 12 | |
| Preparation and Reading | 114 | ||||
| Total | 150 | ||||
Summative Assessment
| Component | Component Weighting | Element | Length/Duration | Element Weighting | Resit Opportunity |
|---|---|---|---|---|---|
| Assignment | 100% | Assignment | 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.
Students who do not attend monitored activities shown under Teaching Methods
and Learning Hours, or who fail to complete the summative or formative
assessment(s) specified above, may be subject to the Academic Progress
procedures defined in the University's General Regulation V, and may be
required to leave the University.