Environmental Data Analysis: Part 2
| Code | School | Level | Credits | Semesters |
| GEOG2069 | Environmental & Geographical Sciences | 2 | 10 | Spring Malaysia |
- Code
- GEOG2069
- School
- Environmental & Geographical Sciences
- Level
- 2
- Credits
- 10
- Semesters
- Spring Malaysia
Summary
This module considers, within the context of Environmental Science, (i) classification and ordination (hierarchical clustering, PCA, MDS, CCA), (ii) Spatial statistics, (iii) designing questionnaires and interviews (iv) analysing social survey data.
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
- 20% Coursework 1: Practical exercise on ordination
- 20% Coursework 2: Analysing social survey data
- 60% Coursework 3
Assessed by end of spring semester
Educational Aims
The aim of this module is to build upon the content of Environmental Data Analysis Part 1, and to explore more complex statistical approaches for dealing with multivariate data, as well as to expose students to some key approaches to collecting and analysing social survey data.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 social surveys
A2) Identify ways of correctly analysing qualitative data
A3) Appreciate the role of multivariate approaches to data analysis
Intellectual Skills
B1) Understand statistical concepts (inference, probability) and relate them to specific questions
B2) Identify appropriate data analytical tools to address particular research hypotheses or questions
Professional/Practical Skills
C1) Apply a range of statistical tests to help understand patterns in environmental data
C2) Independently use the R operating environment to address particular research questions
Transferable/Key Skills
D1) Draw well founded conclusions from data
D2) Choose appropriate ways to best communicate these conclusions
Conveners
- Prof Christopher Neil Gibbins