Environmental Data Analysis: Part 1
| Code | School | Level | Credits | Semesters |
| GEOG2068 | Environmental & Geographical Sciences | 2 | 10 | Autumn Malaysia |
- Code
- GEOG2068
- School
- Environmental & Geographical Sciences
- Level
- 2
- Credits
- 10
- Semesters
- Autumn Malaysia
Summary
This module considers, within the context of Environmental Science, (i) designing field sampling campaigns, (ii) types of data and exploratory data analysis, (iii) testing for relationships, (iv) testing for differences, and (v) time-series analysis. More complex analyses are covered in the related module, Environmental Data Analysis Part 2, which runs in the spring semester.
Target Students
A core module for BSc Environmental Science students; also available to students in the School of Biosciences.Available to exchange students
Classes
- One 45-minute lecture each week for 10 weeks
- One 1-hour laboratory each week for 10 weeks
Assessment
- 25% Practical exercise 1
- 25% Practical exercise 2
- 50% Practical exercise 3
Assessed by end of autumn semester
Educational Aims
The aim of this module is to provide BSc Environmental Science students with an introduction to a range of quantitative and qualitative approaches to data collection and analysis. It will cover key aspects of survey design (i.e. to ensure resulting data are appropriate to addressing research questions, and suitable for application of relevant statistical tests) and then covers several of the broad classes/types of techniques used to understand patterns in data. The module is connected to, and a pre-requisite for, the Environmental Data Analysis Part 2 module which runs in Spring semester.The two Environmental Data Analysis modules have been designed to bring together all the data analysis material covered in the Environmental Science degree into a bespoke ‘statistics’ learning experience that is ordered and taught in a consistent way and uses appropriate and relevant data and examples. The two modules are based around practical work using the open source R software package that Environmental Science students can then become very familiar with and utilize in their final year research projects.Learning Outcomes
On successful completion of this module, students will be able to:
Knowledge and Understanding
A1) Design statistically robust field surveys
A2) Differentiate between different types of data (nominal, ordinal etc)
A3) Test for differences using both parametric and non-parametric approaches
A4) Test for relationships between data sets
A5) Test for temporal trends in data
Intellectual Skills
B1) Develop (statistical) hypotheses
B2) Understand statistical concepts (inference, probability) and relate them to specific questions
Professional/Practical Skills
C1) Apply a range of statistical tests to help understand differences and relationships in environmental data
C2) Use the R operating environment to apply statistical tests
Transferable/Key Skills
D1) Draw well founded conclusions from data
D2) Communicate these conclusions in written form, using statistical conventions (P value etc)
Conveners
- Prof Christopher Neil Gibbins