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.
The PGDip/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 the experience of applying them to real-world problems, under the supervision of experts, some of whom are leading researchers in this field.
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 has 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.
The course consists of two semesters of taught courses followed by a dissertation undertaken over the summer months.
Modules and course material are taught through:
Small group discussion tutorials
You may be assessed on your knowledge and understanding of the course through:
Further particulars regarding curriculum development.
The modules in this programme have varying methods of delivery and assessment. For more details of each module, including weekly contact hours, teaching methods and assessment, please see the latest module catalogue which is for the 2020-2021 academic year; some elements may be subject to change for 2021 entry.
Students typically take the following modules. However, students with adequate statistical training or experience may be exempt from one or both of the first two modules listed below and may take other optional modules instead.
Introductory Data Analysis: covers essential statistical concepts and analysis methods relevant for commercial analysis.
Applied Statistical Modelling using GLMs: covers the main aspects of linear models and generalised linear models, including model specification, various options for model selection, model assessment and tools for diagnosing model faults.
Estimating Animal Abundance and Biodiversity: introduces the main types of survey methods for wildlife populations.
Computing in Statistics: teaches computer programming skills, including principles of good programming practice, with an emphasis on statistical computing.
Modelling Wildlife Population Dynamics: introduces students to methods for constructing mathematical models of wildlife population dynamics and of fitting these models to diverse data from wildlife surveys.
Statistical Problem Solving: focuses on problem formulation and scientific reporting to a variety of audiences; it consists of a set of case studies covering a range of application areas.
As part of their optional choices, all students must take:
Any statistics-focused module at level 5000 in the School (those with module codes beginning MT57 in the module catalogue, or ID5059).
One additional module at level 3000, 4000, or 5000 in the School (those with module codes beginning with MT3, MT4 or MT5 in the module catalogue).
Students who have been exempted from taking one or both of 'Introductory Data Analysis’ or 'Applied Statistical Modelling Using GLMs' may instead choose other relevant modules in statistics.
All students are recommended to include one of the following two modules in their choices:
Optional modules are subject to change each year and require a minimum number of participants to be offered; some may only allow limited numbers of students.
During the final three months of the course, MSc students complete a dissertation or a portfolio dissertation to be submitted by the end of August. Dissertations are supervised by members of teaching staff who will advise on the choice of subject and provide guidance throughout the progress of the dissertation.
A number of options for placements with organisations within the UK are available to work on a range of real-world problems specified by the organisations. Placements may range from a few visits to the organisation to being hosted by the organisation for a large part of the dissertation. Students on placements will be co-supervised by scientists at the organisation and St Andrews staff. International placements will also be available, with similar supervision arrangements. International placements involve an additional cost.
If students choose not to complete the dissertation requirement for the MSc, there is an exit award available that allows suitably qualified candidates to receive a Postgraduate Diploma. By choosing an exit award, you will finish your degree at the end of the second semester of study and receive a PGDip instead of an MSc.
The modules listed here are indicative, and there is no guarantee they will run for 2021 entry.
Conferences and events
There are a number of different seminars held each week in the School of Mathematics and Statistics. These include:
Centre for Research into Ecological and Environmental Modelling seminars
Pure Mathematics colloquia
Algebra and Combinatorial seminars
Analysis Group seminars
Applied Mathematics seminars
Solar and Magnetospheric Theory Group seminars
There are many potential scholarships or support schemes available to postgraduates.
Recent Graduate Discount
The University of St Andrews offers a 10% discount in postgraduate tuition fees to students who are eligible to graduate or who have graduated from St Andrews within the last three academic years and are starting a postgraduate programme with the University of St Andrews.
You should have the following qualifications:
A good 2.1 undergraduate Honours degree in a relevant discipline (e.g. biological sciences, ecology, mathematics, statistics, environmental science or computer science) or a 2.2 in a relevant discipline and equivalent work experience (for example, at least 12 months working in a relevant field).
You should also have undergraduate training in mathematics and statistics at SCQF Level 8 or equivalent experience.
If you studied your first degree outside the UK, check the international entry requirements.
You must be able to demonstrate English language proficiency.
The qualifications listed are indicative of minimum requirements for entry. Some academic Schools will ask applicants to achieve significantly higher marks than the minimum. Obtaining the listed entry requirements will not guarantee you a place, as the University considers all aspects of every application including, where applicable, the writing sample, personal statement, and supporting documents.
Personal statement (optional)
Two original signed academic references
Academic transcripts and degree certificates
Evidence of English language proficiency (required if English is not your first language).
After the MSc
The MSc in Statistical Ecology prepares students for further postgraduate studies in quantitative ecology, conservation, or statistics applied to ecological problems.
The MSc is taught by members of the world-leading Centre for Research into Ecological and Environmental Modelling (CREEM), and graduates may continue their education by enrolling for a PhD within CREEM or within statistics, biology, wildlife, ecology or conservation departments worldwide.
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
Graduates may also work as wildlife managers, using their analytical skills to better inform management decisions.
The Careers Centre offers one-to-one advice to all students on a taught postgraduate course and offers a programme of events to assist students in building their employability skills.