Module overview
We are now in the era of “big data”. In the environmental context, this usually means messy and complex datasets that don't follow the rules of traditional statistical techniques and yet have the potential to shed light on the challenges faced by environmental managers. Statistical learning provides a useful tool set for exploring and uncovering the stories that these datasets can tell. This module will cover the background to statistical learning and its relationship to machine learning, as well as teaching you how to handle complex environmental datasets efficiently in order to visualise, explore and model the underlying processes.
MSc students are expected to have successfully completed a statistics course at undergraduate level
Linked modules
Pre-requisite: ENVS1005
Aims and Objectives
Learning Outcomes
Subject Specific Intellectual and Research Skills
Having successfully completed this module you will be able to:
- analysing, synthesising and summarising information critically, including prior research.
- recognising and using subject-specific theories, paradigms, concepts and principles.
- applying knowledge and understanding to complex and multidimensional problems in familiar and unfamiliar contexts
- collecting and integrating several lines of evidence to formulate and test hypotheses
Subject Specific Practical Skills
Having successfully completed this module you will be able to:
- collecting, recording and analysing data using appropriate techniques in the field and laboratory.
- planning, conducting, and reporting on environmental investigations, including the use of secondary data.
- referencing work in an appropriate manner.
Knowledge and Understanding
Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:
- the need for both a multi-disciplinary and an interdisciplinary approach in advancing knowledge and understanding of Earth systems, drawing, as appropriate, from the natural and the social sciences
- the contribution of environmental science to the development of knowledge of the world we live in,
- the processes which shape the natural world at different temporal and spatial scales and their influence on and by human activities
- methods of acquiring, interpreting and analysing environmental science information with a critical understanding of the appropriate contexts for their use
- the terminology, nomenclature and classification systems used in environmental science.
Transferable and Generic Skills
Having successfully completed this module you will be able to:
- using the internet critically as a means of communication and a source of information.
- appreciating issues of sample selection, accuracy, precision and uncertainty during collection, recording and analysis of data in the field and laboratory.
- receiving and responding to a variety of information sources (eg textual, numerical, verbal, graphical).
- preparing, processing, interpreting and presenting data, using appropriate qualitative and quantitative techniques and packages including geographic information systems solving numerical problems using computer and non-computer-based techniques.
- communicating appropriately to a variety of audiences in written, verbal and graphical forms.
Syllabus
This module will include, but is not limited to:
o Approaches to data analysis
o Data wrangling
o Feature engineering
o Resampling methods
o Generalized Linear Models
o Generalized Additive Models and MARS
o Decision trees and neutral networks
The focus will be on using R and RStudio.
Learning and Teaching
Teaching and learning methods
The module consists of a series of lectures and workshops covering the theory, background and potential applications of the modelling techniques covered. These will be supplemented by practical computer sessions that enable you to put the theory into practice.
| Type | Hours |
|---|---|
| Preparation for scheduled sessions | 18 |
| Lecture | 11 |
| Practical classes and workshops | 33 |
| Wider reading or practice | 25 |
| Revision | 30 |
| Follow-up work | 33 |
| Total study time | 150 |
Assessment
Assessment strategy
Referral method - In class test, ~2 hours. This summative assessment must be passed in order to complete the module.
Summative
This is how we’ll formally assess what you have learned in this module.
| Method | Percentage contribution |
|---|---|
| Analysis and report | 50% |
| Exercise | 50% |
Referral
This is how we’ll assess you if you don’t meet the criteria to pass this module.
| Method | Percentage contribution |
|---|---|
| Assessment | 100% |
Repeat Information
Repeat type: Internal & External