Postgraduate Module Descriptor

SOCM029: Data Visualisation

This module descriptor refers to the 2019/0 academic year.

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.

Intended Learning Outcomes (ILOs)
This module's assessment will evaluate your achievement of the ILOs listed here - you will see reference to these ILO numbers in the details of the assessment for this module.
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

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

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
Lectures with lab	20 hours in total	10 x 2 hour lectures and labs .These lectures cover the main concepts of the course. These practical sessions cover the application of techniques
Independent study	60 hours	Reading and preparing for lectures and labs (around 4-6 hours per week);
Independent study	70 hours	researching and writing assessments and assignments (researching, planning and writing the course work).

This module has online resources available via ELE (the Exeter Learning Environment).

http://r4ds.had.co.nz/data-visualisation.html