Last modified: 22 Jul 2026 14:16
This course is uniquely tailored for environmental scientists and ecologists and will provide students with the required background theory and practical skills relevant to modern science. Our example-led lectures and real-world based practical sessions will provide you with a foundation to become confident and proficient in analysing real data. Throughout this course, we will introduce you to using the programming language R to implement modern statistical modelling techniques. You will use the flexible linear and generalised linear modelling frameworks to analyse environmental and ecological data with an emphasis on robust and reproducible statistical methods.
| Study Type | Postgraduate | Level | 5 |
|---|---|---|---|
| Term | First Term | Credit Points | 15 credits (7.5 ECTS credits) |
| Campus | Aberdeen | Sustained Study | No |
| Co-ordinators |
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Delivered over 11 weeks, this course aims to develop the statistical understanding and practical computing skills you need to analyse ecological and environmental data using R. Example-led lectures introduce the underlying statistical concepts, while separate computer practicals provide structured opportunities to apply these concepts to realistic datasets and develop confidence in reproducible data analysis.
The course begins with statistical inference, uncertainty and the foundations of working with R and RStudio. You will then develop skills in importing, manipulating, exploring and visualising data before progressing to the linear modelling framework. This includes fitting, checking, interpreting and communicating models with continuous and categorical variables, extending models to include multiple variables and comparing plausible models using appropriate model-selection methods. Throughout the course, emphasis is placed on understanding the assumptions and limitations of statistical methods, selecting analyses that are appropriate to the research question and data and presenting results clearly and reproducibly.
Practical exercises, consolidation activities and formative support are integrated throughout the course to reinforce learning and support a manageable progression from foundational skills to independent analysis. The course includes three assessments. The first assesses your core R skills, while the second assesses your understanding and interpretation of a simple linear model. In the final assessment, you will analyse an ecological or environmental dataset and produce a written report that presents and interprets your findings. Dedicated support and question-and-answer sessions are provided before you complete and submit the final assessment independently.
Information on contact teaching time is available from the course guide.
| Assessment Type | Summative | Weighting | 60 | |
|---|---|---|---|---|
| Assessment Weeks | 14 | Feedback Weeks | 16 | |
| Feedback |
Written Report - Analyse a provided data set and complete a structured written report.
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| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Conceptual | Apply | Understand the theory of linear modelling and how to apply this theory to fit models to biological and ecological data using R. |
| Conceptual | Evaluate | Be able to critically evaluate linear models through model validation and also interpret model output in a biological context. |
| Conceptual | Understand | Have an appreciation and working knowledge of how to conduct your data analysis in a robust and reproducible way. |
| Procedural | Apply | Be able to visualise and explore biological and ecological data using appropriate graphs and summary tables using R. |
| Procedural | Understand | Have a good understanding and working knowledge of using R. |
| Assessment Type | Summative | Weighting | 20 | |
|---|---|---|---|---|
| Assessment Weeks | 11 | Feedback Weeks | 11 | |
| Feedback |
90-minute MyAberdeen based test. Written feedback will be provided for each question. |
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| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Conceptual | Understand | Have an appreciation and working knowledge of how to conduct your data analysis in a robust and reproducible way. |
| Conceptual | Understand | Understand how we can ask questions in science and specifically how we can apply statistical inference to estimate population parameters. |
| Procedural | Apply | Be able to visualise and explore biological and ecological data using appropriate graphs and summary tables using R. |
| Assessment Type | Summative | Weighting | 20 | |
|---|---|---|---|---|
| Assessment Weeks | 12 | Feedback Weeks | 13 | |
| Feedback |
120-minute Myaberdeen based test. Written and verbal feedback will be provided individually. |
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| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Conceptual | Apply | Understand the theory of linear modelling and how to apply this theory to fit models to biological and ecological data using R. |
| Conceptual | Evaluate | Be able to critically evaluate linear models through model validation and also interpret model output in a biological context. |
| Conceptual | Understand | Have an appreciation and working knowledge of how to conduct your data analysis in a robust and reproducible way. |
| Procedural | Understand | Have a good understanding and working knowledge of using R. |
There are no assessments for this course.
| Assessment Type | Summative | Weighting | ||
|---|---|---|---|---|
| Assessment Weeks | Feedback Weeks | |||
| Feedback |
Any components that were previously passed will be carried forward. |
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| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
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| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Conceptual | Understand | Have an appreciation and working knowledge of how to conduct your data analysis in a robust and reproducible way. |
| Procedural | Apply | Be able to visualise and explore biological and ecological data using appropriate graphs and summary tables using R. |
| Conceptual | Apply | Understand the theory of linear modelling and how to apply this theory to fit models to biological and ecological data using R. |
| Procedural | Understand | Have a good understanding and working knowledge of using R. |
| Conceptual | Evaluate | Be able to critically evaluate linear models through model validation and also interpret model output in a biological context. |
| Conceptual | Understand | Understand how we can ask questions in science and specifically how we can apply statistical inference to estimate population parameters. |
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