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BI3010: STATISTICAL ANALYSIS OF BIOLOGICAL DATA (2016-2017)

Last modified: 28 Jun 2018 10:27


Course Overview

In a series of cases studies, you will learn how to analyse and interpret biological data to a level which will allow you to design, at least, the first stages of your level 4 honours project.

You will also choose from4-6 topics in advanced data handling techniques also pertinent to level 4 honours projects.

The course is intensive but allows you to work largely at your own pace with considerable assistance from 3-4 staff and 5-6 demonstrators.

 

Course Details

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

Qualification Prerequisites

None.

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

  • One of BSc Biology (Studied) or BSc Conservation Biology (Studied) or BSc Biology-Environmental Humanities (Studied) or BSc Plant Biology (Studied) or BSc Zoology (Studied) or BSc Animal Ecology (Studied) or BSc Marine Biology (Studied) or BSc Parasitology (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 BSc Biology - Education (Primary) (Studied) or Bachelor Of Science In Environmental And Forest Management (Studied) or BSc Ecology (Studied) or BSc Wildlife Management (Studied) or BSc Forestry (Studied) or BSc Forest Sciences (Studied) or BSc Environmental Science (Studied) or BSc Environmental Science (Physical Sci) (Studied) or BSc Biology - Education (Secondary) (Studied)
  • Any Undergraduate Programme (Studied)

What other courses must be taken with this course?

None.

What courses cannot be taken with this course?

  • BI3007 Experimental Design, Analysis and Presentation (Studied)

Are there a limited number of places available?

No

Course Description

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; multivariate statistics.

Associated Costs

None

Further Information & Notes

Students will attend 3 x 1hr lectures per week and 1 x 1hr practical session per week during the first 3 weeks. The second 2 weeks has 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.  If this is an optional course, there may also be contact hours on Wednesdays, 9-11.


In light of Covid-19 this information is indicative and may be subject to change.

Contact Teaching Time

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

Teaching Breakdown

None.

More Information about Week Numbers


Summative Assessments

1st Attempt: One 2-hour MCQ exam (25%) and continuous assessment (75%). The examination will be “open book” with MCQ answers and continuous assessment will be based on reports produced during practical sessions.

Resit: One 2-hour MCQ exam (25%) and a new assignment for the failed continuous assessment allowing the candidate to demonstrate achievement of learning outcomes (75%).

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.

Feedback

Students will receive written feedback on their practical reports.

Course Learning Outcomes

None.

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