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Computational Methods in Biodiversity Research (BIOS0002)

Key information

Faculty
Faculty of Life Sciences
Teaching department
Division of Biosciences
Credit value
15
Restrictions
Priority will be given to students on programmes where the module is compulsory.
Timetable

Alternative credit options

There are no alternative credit options available for this module.

Description

The aim of this module is to acquire skills needed to collect, manipulate and perform statistical analysis of the kinds of data that typically underpin scientific research in biodiversity, evolution and conservation.

After taking the module you will be able to:

  • Understand basic field techniques and experimental design.
  • Retrieve, view, manage and manipulate scientific data.
  • Describe data using statistics, and to construct, implement and understand basic hypothesis testing.
  • Understand different types of statistical models.

For students based on the Bloomsbury campus, the course starts with a 2-3 day field trip to the ¹û¶³Ó°Ôº Blakeney Point field station, for which travel, accommodation, and standard meals are financially covered. Students must wear walking boots, warm clothes, and bring a sleeping bag, pillow, and any snacks (main meals only are provided as part of the course).

For ¹û¶³Ó°Ôº East students, fieldwork will take place on campus in and around our Living Lab – the Queen Elizabeth Olympic Park. Students are encouraged to wear walking boots and warm clothes.

During the field courses, you will design and conduct a basic field experiment.ÌýFieldwork will be followed by lectures and practical sessions delivered by academics from within ¹û¶³Ó°Ôº and the Zoological Society of London, in which you will learn how to manipulate, visualise and analyse ecological data, using the widely-used, freely available computer programming language R.

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Module deliveries for 2024/25 academic year

Intended teaching term: Term 1 ÌýÌýÌý Undergraduate (FHEQ Level 7)

Teaching and assessment

Mode of study
In person
Methods of assessment
100% Coursework
Mark scheme
Numeric Marks

Other information

Number of students on module in previous year
2
Module leader
Dr Tim Newbold
Who to contact for more information
t.newbold@ucl.ac.uk

Intended teaching term: Term 1 ÌýÌýÌý Postgraduate (FHEQ Level 7)

Teaching and assessment

Mode of study
In person
Methods of assessment
100% Coursework
Mark scheme
Numeric Marks

Other information

Number of students on module in previous year
38
Module leader
Dr Tim Newbold
Who to contact for more information
t.newbold@ucl.ac.uk

Intended teaching term: Term 1 ÌýÌýÌý Undergraduate (FHEQ Level 7)

Teaching and assessment

Mode of study
In person
Intended teaching location
¹û¶³Ó°Ôº East
Methods of assessment
100% Coursework
Mark scheme
Numeric Marks

Other information

Number of students on module in previous year
0
Module leader
Dr Tim Newbold
Who to contact for more information
t.newbold@ucl.ac.uk

Intended teaching term: Term 1 ÌýÌýÌý Postgraduate (FHEQ Level 7)

Teaching and assessment

Mode of study
In person
Intended teaching location
¹û¶³Ó°Ôº East
Methods of assessment
100% Coursework
Mark scheme
Numeric Marks

Other information

Number of students on module in previous year
43
Module leader
Dr Tim Newbold
Who to contact for more information
t.newbold@ucl.ac.uk

Last updated

This module description was last updated on 8th April 2024.

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