Data Handling and Statistics module (GE32021)

Develop your data visualisation, analysis, and statistical skills. You will develop this while learning the programming language 'R'. You will also work with real-world geographical data

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This module emphasises the importance of interpreting and presenting data. Employers highly value these skills. They are also essential for drawing meaningful conclusions from data.

Throughout the module, you'll learn statistical analysis techniques. You will use these in various kinds of coursework, such as:

  • laboratory and fieldwork reports
  • paper interpretation
  • your senior honours research project

You will learn the modern approach to data interpretation. It has made the process much simpler and makes data easier to analyse and present.

By the end of the module, you will be confident and competent in the fundamental techniques of data analysis and statistics.

What you will learn

In this module, you will:

  • gain an introduction to the basic concepts and techniques of modern statistical analysis. You will also learn the language 'R' through which these techniques are implemented
  • the necessary personal qualities to meet and overcome challenges in what is sometimes seen as a difficult discipline
  • produce your senior honours research project

By the end of this module, you will be able to:

  • use the graphical user interface 'RStudio' to organise projects. This will also be used to present and visualise your analysis effectively
  • interpret the findings of academic papers
  • produce fieldwork reports using statistical analysis techniques

Assignments / assessment

  • class test 1 (40%)
  • class test 2 (60%)

This module does not have a final exam.

Teaching methods / timetable

Statistical analysis is a subject best learned by doing.

The majority of the time you spend on this module will be in RStudio applying the statistical techniques that you are learning.

This module adopts a flipped classroom approach.

You will be introduced to the learning material before class. Classroom time will then be used to deepen understanding through discussion with peers. There will also be problem-solving activities facilitated by your tutors.

Week Activity
0 Introductory Materials
1 Data Types; Populations & Sample; Dataset Distributions; Exploratory Analysis
2 Hypotheses; p Value; Correlation; Linear Regression
3 Multiple Linear Regression; Model simplification; Model checking
4 Categorical data; Contingency Tables; and Rank correlation coefficients
5 Class Test 1
6 Reading Week - Additional resources covering coding survey responses; non-linear analysis techniques.
7 Analysis of frequency - χ2; Analysis of variance - t-test
8 ANOVA – Analysis of variance; Model checking; Data transform
9 Multi-way ANOVA; Identification of the "Minimum Sufficient Model"; Analysis procedure
10 Recap key concepts and techniques
11 Class Test 2


This module is available on following courses: