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The University of Southampton
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ENVS6034 Advanced Quantitative Methods

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

Aims and Objectives

Learning Outcomes

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 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.
  • methods of acquiring, interpreting and analysing environmental science information with a critical understanding of the appropriate contexts for their use
  • the contribution of environmental science to the development of knowledge of the world we live in,
Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

  • applying knowledge and understanding to complex and multidimensional problems in familiar and unfamiliar contexts
  • recognising and using subject-specific theories, paradigms, concepts and principles.
  • analysing, synthesising and summarising information critically, including prior research.
  • collecting and integrating several lines of evidence to formulate and test hypotheses
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.
  • 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.
  • appreciating issues of sample selection, accuracy, precision and uncertainty during collection, recording and analysis of data in the field and laboratory.
  • communicating appropriately to a variety of audiences in written, verbal and graphical forms.
  • receiving and responding to a variety of information sources (eg textual, numerical, verbal, graphical).
Subject Specific Practical Skills

Having successfully completed this module you will be able to:

  • referencing work in an appropriate manner.
  • 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.

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.

TypeHours
Revision30
Follow-up work33
Wider reading or practice25
Practical classes and workshops33
Lecture11
Preparation for scheduled sessions18
Total study time150

Assessment

Assessment Strategy

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

Summative

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

Referral

MethodPercentage contribution
Class Test  (120 minutes) 100%

Repeat Information

Repeat type: Internal & External

Linked modules

Pre-requisite: ENVS1005

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