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Computational Ecology MSc

Key information

Qualification type

MSc - Master of Science

Subject areas

Ecology Data Analysis

Course type


Course Summary

About this course

This course addresses the severe shortage of scientists trained to a high standard in both data modelling and ecology. Ecological and environmental scientists now generate large amounts of observational data. The data could be from:

camera traps
GPS records
eDNA samples

Due to the unique combination of ecology and big data, the course is suitable for:

ecologists involved in scientific research
graduates in mathematics or computing

As an interdisciplinary course, some modules are unique whilst others are shared with other courses. This ensures that you will gain:

detailed technical skills needed by future employers
understanding of the broader ecological context of data collection.

What you'll learn

You will gain hands-on experience of using modern computing and modelling methods. This will develop your essential technical skills, which are in demand by employers. Through taught modules and a research project, you will also enhance broader abilities such as:

a critical review of scientific literature
advance planning

Statistical methods, modelling and technical skills are developed in the taught component. Modules subject areas include:

statistical methods
ecological modelling for wildlife and conservation
meta-analysis and decision support
human impacts on natural systems
visualisation of ecological data
geographic information systems and remote sensing.

Different course options

Study mode

Full time


12 months

Start date



Modern ecological surveys can produce large datasets, e.g. from GPS tags on animals, camera traps, genetics data etc. These large datasets require environmental scientists trained in data analytics, in other words capable of managing the entire pipeline from raw data, data cleaning and management, modelling, visualisation, and communication to other scientists and non-specialists. Students will learn different methods of storing and handling large datasets, the theory and practice of reproducible research, robust software coding with version control, data visualisation, both static and interactive (web-based) graphical display and communication of results. The module will focus primarily on data from ecology and environmental science, but will also include examples of data processing skills needed to understand complex bioinformatics or similar data.

Tuition fees

UK fees
Course fees for UK / EU students

For this course (per year)


Average for all Postgrad courses (per year)


International fees
Course fees for non-UK / EU students

For this course (per year)


Average for all Postgrad courses (per year)


Entry requirements

A 2:2 BSc or BEng honours degree, or international equivalent, in a relevant subject such as: microbiology; agriculture; environmental engineering; biology; biological sciences; ecology; environmental science; zoology; marine biology; oceanography; geography. We will also consider applicants with non-standard qualifications and relevant experience.