Postgraduate Programme and Module Handbook 2021-2022 (archived)
Module GEOL50215: Data Science Applications in Earth Sciences
Department: Earth Sciences
GEOL50215:
Data Science Applications 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 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.
Content
- 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
Learning Outcomes
- 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.
- 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
- 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: an individual written report 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
Teaching Methods and Learning Hours
| Activity |
Number |
Frequency |
Duration |
Total/Hours |
|
| Lectures |
8 |
2 times per week (Term 2, weeks 16-19) |
1 hour |
8 |
|
| Workshops |
8 |
1 times per week (Term 2, weeks 16 - 19) |
2 hours |
16 |
|
| Surgery |
4 |
1 times per week (Term 1, weeks 16-19) |
1 hour |
4 |
|
| Data Camp |
1 |
3 days of 7 hours per day |
21 hours |
21 |
|
| Independent Learning |
|
|
|
101 |
|
| 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 relevant 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
