College of Social Sciences and International Studies
Data Visualisation
Module SOCM029 for 2021/2
Module SOCM029 for 2021/2
- Overview
- Aims and Learning Outcomes
- Module Content
- Indicative Reading List
- Assessment
Postgraduate Module Descriptor
SOCM029: Data Visualisation
This module descriptor refers to the 2021/2 academic year.
Module Aims
The main aims of the module are:
- To understand and apply principles of data visualization
- To develop skills in capturing and managing data for visualisation
- To analyze subject relevant data sets using data visualisation techniques
- To learn to quantitatively and qualitatively evaluate existing visualizations
- To further develop skills in using the ggplot2 package for R and related packages for data visualisation.
On successfully completing the programme you will be able to: | |
---|---|
Module-Specific Skills | 1. Demonstrate proficiency in the use of R for data visualisation 2. understand code in R and implement appropriate commands to perform relevant analyses 3. Understand principles of and appropriate use of data visualization to communicate data analysis. |
Discipline-Specific Skills | 4. Develop coding skills in a way that results in high level of synergies with quantitative research skills. 5. manipulate data in R and use the appropriate analytic tools. 6. interpret output from each program and draw appropriate inference regarding the hypotheses being tested. |
Personal and Key Skills | 7. Demonstrate understanding of computing skills 8. Demonstrate understanding of data management skills |
Module Content
Syllabus Plan
Whilst the module’s precise content and order of syllabus coverage may vary, it is envisaged that it will include the following topics:
- Course Introduction, Terminology
- Introduction to R. Types of data
- Main principles of data visualization
- Types of statistical graphics
- Cleaning and preparing data for visualisations. The package dplyr.
- Basic Charts and Plots, Multivariate Data Visualization
- The package ggplot2: structure of plots and commands
- Principles of Perception, Color, Design, and Evaluation
- Text Data Visualization
- Temporal Data Visualization
- Geospatial Data Visualization
- Network Data Visualization
Learning and Teaching
This table provides an overview of how your hours of study for this module are allocated:
Scheduled Learning and Teaching Activities | Guided independent study | Placement / study abroad |
---|---|---|
20 | 130 | 0 |
...and this table provides a more detailed breakdown of the hours allocated to various study activities:
Category | Hours of study time | Description |
---|---|---|
Scheduled Learning and Teaching Activity | 20 | 10 x 2 hour lectures and labs .These lectures cover the main concepts of the course. These practical sessions cover the application of techniques |
Guided Independent Study | 60 | Reading and preparing for lectures and labs (around 4-6 hours per week); |
Guided Independent Study | 70 | researching and writing assessments and assignments (researching, planning and writing the course work). |
Online Resources
This module has online resources available via ELE (the Exeter Learning Environment).
http://r4ds.had.co.nz/data-visualisation.html