Postgraduate Programme and Module Handbook 2021-2022 (archived)
Module GEOL50315: Data Science Tools in Earth Sciences
Department: Earth Sciences
GEOL50315:
Data Science Tools in Earth Sciences
| Type |
Tied |
Level |
5 |
Credits |
15 |
Availability |
Available in 2021/22 |
Prerequisites
Corequisites
Excluded Combination of Modules
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
- 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.
- 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
- 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 |
|
| 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% |
|
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.
■ 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
