Statistical Ecology (PGDip/MSc) University of St Andrews

In this course, you will learn the modern statistical methods currently used by professionals in ecology. You will learn to formulate problems, conduct appropriate analyses and effectively communicate results to a variety of audiences. To balance theory and application, placement opportunities will be available with partner organisations within the UK and abroad.

Course information

The MSc in Statistical Ecology is a one-year taught programme run by the School of Mathematics and Statistics.

This course aims to give you a sound understanding of the statistical foundations of modern methods in statistical ecology, to give you the skills to use these methods effectively, and to give you experience of applying them to real-world problems, under the supervision of experts, some of whom are leading researchers in this field.

Highlights

• Introduces key concepts and methods in statistical ecology and provides an overview of the field.
• Taught by staff at the Centre for Research into Ecological and Environmental Modelling (CREEM), who have more than two decades’ experience developing, using and teaching methods in statistical ecology.
• Core modules in Semester 1 provide a solid statistical foundation for specialist modules later in the course.
• Optional placements with collaborators in the UK and abroad as part of a supervised summer research dissertation; connects theoretical training with real field studies and professionals.
• Flexible dissertation format, which can include producing a podcast, web page, poster, field report, training materials, or a short film.

Careers

Statistical skills are highly valued in ecology and conservation, with modern ecological methods becoming increasingly quantitative. The course is therefore excellent preparation for a career as a scientist in:

• government environment agencies
• industry
• consultancies
• conservation organisations.

Graduates may also work as wildlife managers, using their analytical skills to better inform management decisions.