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ENVS6034 Advanced Quantitative Methods

Module Overview

We are now in the era of “big data”. In the environmental context, this results in large, usually complex datasets that have the potential to shed light on the challenges faced by environmental managers. Statistical modelling provides a useful tool set for exploring and uncovering the stories that these datasets can tell. This module will cover the background to statistical modelling, 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

Aims and Objectives

Module Aims

This module aims to build upon the skills learnt in earlier statistics courses to enable you to visualise, explore and build predictive statistical models from large and complex datasets.

Learning Outcomes

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

  • the processes which shape the natural world at different temporal and spatial scales and their influence on and by human activities
  • the terminology, nomenclature and classification systems used in environmental science.
  • the contribution of environmental science to the development of knowledge of the world we live in,
  • methods of acquiring, interpreting and analysing environmental science information with a critical understanding of the appropriate contexts for their use
  • 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
Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

  • collecting and integrating several lines of evidence to formulate and test hypotheses
  • recognising and using subject-specific theories, paradigms, concepts and principles.
  • applying knowledge and understanding to complex and multidimensional problems in familiar and unfamiliar contexts
  • analysing, synthesising and summarising information critically, including prior research.
Transferable and Generic Skills

Having successfully completed this module you will be able to:

  • receiving and responding to a variety of information sources (eg textual, numerical, verbal, graphical).
  • appreciating issues of sample selection, accuracy, precision and uncertainty during collection, recording and analysis of data in the field and laboratory.
  • using the internet critically as a means of communication and a source of information.
  • 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.
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.


This module will include, but is not limited to: o Approaches to data analysis o Data preparation and visualisation techniques o Transformations o Resampling methods o Linear additive models o Non-linear models and machine learning o An introduction to Bayesian techniques The focus will be on combining the strengths of Excel, SPSS and R, plus other open-source packages such as JASP.

Special Features

For students with specials needs, an individual assessment with be made and appropriate arrangements made to ensure they are enabled to benefit from the exercise or an equivalent experience.

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.

Wider reading or practice25
Preparation for scheduled sessions18
Practical classes and workshops33
Follow-up work33
Total study time150


Assessment Strategy

Referral method - In class test, ~2 hours. This summative assessment must be passed in order to complete the module.


MethodPercentage contribution
Class Test  (120 minutes) 50%
Class Test  (120 minutes) 50%


MethodPercentage contribution
Class Test  (120 minutes) 100%

Repeat Information

Repeat type: Internal & External

Linked modules

Pre-requisite: ENVS1005

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