Skip to Content


Last modified: 28 Jun 2018 10:27

Course Overview

This course is uniquely tailored for biologists and will provide students with the required background and skills relevant to modern ecology and biology. The unique format of example-led lectures and real-world based practicals will provide you with a foundation to become confident and proficient in dealing with real data. Throughout this course, we will introduce you to using the programming language R (an industry standard) to implement modern statistical modelling techniques. You will use the flexible linear and generalised modelling framework to analyse biological data.

Course Details

Study Type Postgraduate Level 5
Session First Sub Session Credit Points 15 credits (7.5 ECTS credits)
Campus None. Sustained Study No
  • Professor David Lusseau
  • Dr Alex Douglas
  • Professor Rene Van Der Wal

Qualification Prerequisites


What courses & programmes must have been taken before this course?

  • Either Any Postgraduate Programme (Studied) or BI4015 Grant Proposal (Passed)
  • One of MRes Ecology & Environmental Sustainability or MRes Applied Marine and Fisheries Ecology or MSc Ecology & Environmental Sustainability or MSc Applied Marine and Fisheries Ecology or Master Of Science In Ecology & Conservation or MSc Forestry (Taught) or Master Of Science In Environmental And Forest Management or MSci Biological Sciences

What other courses must be taken with this course?


What courses cannot be taken with this course?

  • (Studied)

Are there a limited number of places available?


Course Description

The module will be divided in themed weeks during which students will gain skills in sampling design (through practicals) and analytical technique (through lecture and computer labs).

Week 1: Students are introduced to simple sampling designs, concepts of inference, causality and probability, and the language R.

Week 2: Students continue their progression in statistical analyses and are introduced to data exploration and visualisation in R as well as real-world sampling designs.

Week 3: Students learn about general linear models and their interpretation (model fitting, model selection, and model validation) and are exposed to more advanced models.  Students will undertake a one hour in-class assessemnt.

Week 4: Students extend the linear modelling framework to apply it to a range of data types using generalised linear models. Students will undertake a one hour in-class assessment.

Week 5: Students continue exploring generalised linear models and are also given the option to cover generalised additive models. Students get the opportunity to go over material covered in previous weeks.

Week 6: Student-lead teaching. Students are given the opportunity to go over previous material to reinforce learning and are given time to prepare for their final in-class assessment (3 hours). Students are also offered the opportunity to optionally cover additional material such as multivariate statistics.

Associated Costs


Contact Teaching Time

Information on contact teaching time is available from the course guide.

Teaching Breakdown

More Information about Week Numbers

Details, including assessments, may be subject to change until 31 August 2023 for 1st half-session courses and 22 December 2023 for 2nd half-session courses.

Summative Assessments

The module will be assessed based on 3 in-class graded practicals (20%, 20%, 60%).

Formative Assessment

There are no assessments for this course.



Course Learning Outcomes


Compatibility Mode

We have detected that you are have compatibility mode enabled or are using an old version of Internet Explorer. You either need to switch off compatibility mode for this site or upgrade your browser.