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Last modified: 26 Feb 2018 19:47

Course Overview

Review of basics of probability theory, data exploration and analysis using the linear modelling framework; depending on the option taken - fundamentals of using geographic information systems (GIS); bioinformatics and their application; statistical computing with R; capturing data; experimental design.


Course Details

Study Type Undergraduate Level 3
Session First Sub Session Credit Points 15 credits (7.5 ECTS credits)
Campus None. Sustained Study No
  • Professor Michelle Pinard
  • Dr Thomas Cornulier

Qualification Prerequisites


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

  • Any Undergraduate Programme (Studied)
  • One of BSc Biology (Studied) or BSc Conservation Biology (Studied) or BSc Zoology (Studied) or BSc Animal Ecology (Studied) or BSc Marine Biology (Studied) or BSc Animal Behaviour (Studied) or BSc Behavioural Biology (Studied) or MSci Biological Sciences (Studied) or BSc Biological Sciences (Honours) (Studied) or BSc Plant and Soil Sciences (Studied) or Bachelor Of Science In Environmental And Forest Management (Studied) or BSc Ecology (Studied) or BSc Forestry (Studied) or BSc Forest Sciences (Studied) or BSc Environmental Science (Studied)
  • Either Programme Level 3 or Programme Level 4

What other courses must be taken with this course?


What courses cannot be taken with this course?

  • BI3007 Experimental Design, Analysis and Presentation (Studied)

Are there a limited number of places available?


Course Description

Using real biological cases studies, the first 3 weeks of the course will refresh and reinforce basic notions of statistics and statistical modelling. You will learn how to approach the analysis of biological data, introducing a workflow covering hypothesis formulation, graphical data exploration, statistical analysis, validation of assumptions and interpretation of results. This will take you to a level which will allow you to design, at least the first stages of your level 4 honours project

For the following 2 weeks of the course will allow you to choose from 4-6 options in advanced data handling techniques also pertinent to level 4 honours projects.

The course is intensive but alternates lectures followed by practical sessions so you can practice the taught principles with real data and benefit from considerable assistance from 3-4 staff and 5-6 demonstrators.

Associated Costs


Further Information & Notes

Students will attend 3 x 1hr lectures per week and 3 x 1hr practical session per week during the first 3 weeks. The following 2 weeks have a variable format depending on the choice of option. It is expected that students would spend a further 14-15 hours per week in self-study.

This course runs in weeks 7-11, and is scheduled in Thread 2, so may have contact hours in any or all of these times:  Mondays, 14-18; Tuesday, all day; Friday, 14-18.   


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

1st Attempt: First 3 weeks session: Continuous assessment by two tests (25% 5 each) and one final exam (25%).  Last 2 weeks session: Assessment based on test or practical report, depending on option selected. 


Resit: As first attempt with opportunity to resit the failed component (s) and pass marks carried forward.

Formative Assessment

Tutorial/workshop sessions will provide opportunity for student-student and student-tutor interaction. Exercises completed during the practical sessions are supported by material that can be used for self-assessment and staff will provide informal verbal feedback during practical sessions.


Students will receive automated feedback on MCQ with opportunities to ask questions in online forum and Q&A sessions and written feedback on their practical reports.

Course Learning Outcomes


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