Data Visualisation

Module title	Data Visualisation
Module code	SSIM918
Academic year	2023/4
Credits	15
Module staff	Dr Chris Playford (Lecturer)

Duration: Term	1	2	3
Duration: Weeks		11

Number students taking module (anticipated)	25

Module description

This course will introduce you to the field of data visualization. You will learn basic principles of data and model visualization. The focus will be on the principles of turning data into graphical representations for describing and exploring data, analysing hypotheses and relationships and presenting evidence. Particular attention will be paid to visualizing data for policy audiences. You will learn techniques for visualizing different types and formats of data utilizing industry standard, open source software.

Module aims - intentions of the module

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 analyse 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)

ILO: Module-specific skills

On successfully completing the module you will be able to...

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.

ILO: Discipline-specific skills

On successfully completing the module you will be able to...

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.

ILO: Personal and key skills

On successfully completing the module you will be able to...

7. demonstrate understanding of computing skills;
8. demonstrate understanding of data management skills.

Syllabus plan

Whilst the module’s precise content may vary from year to year, it is envisaged that the syllabus will cover some or all of 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 activities and teaching methods (given in hours of study time)

Scheduled Learning and Teaching Activities	Guided independent study	Placement / study abroad
20	130

Details of learning activities and teaching methods

Category	Hours of study time	Description
Scheduled Learning & Teaching activities	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).

Formative assessment

Form of assessment	Size of the assessment (eg length / duration)	ILOs assessed	Feedback method
In-class exercises	15 minutes each x 4	1-8	Peer and oral feedback

Summative assessment (% of credit)

Coursework	Written exams	Practical exams
100	0	0

Details of summative assessment

Form of assessment	% of credit	Size of the assessment (eg length / duration)	ILOs assessed	Feedback method
Practical Exercise 1 using the skills/techniques developed to investigate a research problem relevant to the students chosen discipline.	50	1,500 words & up to 5 data figures	1-8	Written feedback
Practical Exercise 2 using the skills/techniques developed to investigate a research problem relevant to the students chosen discipline.	50	1,500 words & up to 5 data figures	1-8	Written feedback
	0
	0
	0
	0

Details of re-assessment (where required by referral or deferral)

Original form of assessment	Form of re-assessment	ILOs re-assessed	Timescale for re-assessment
Practical Exercise 1	Practical Exercise 1 (1500 words & up to 5 data figures)	1-8	August/September re-assessment period
Practical Exercise 2	Practical Exercise 2 (1500 words & up to 5 data figures)	1-8	August/September re-assessment period

Indicative learning resources - Basic reading

Wickham, H. et al. (2021). ggplot2: Elegant Graphics for Data Analysis (3rd ed.). New York: Springer. https://ggplot2-book.org/index.html

Healy, K. (2018). Data Visualization. Princeton, NJ: Princeton University Press.

https://socviz.co/

Wickham, H. and Grolemund, G. (2017) R for data science : import, tidy, transform, visualize, and model data. https://r4ds.had.co.nz/index.html

Munzner, T. (2014). Visualization Analysis and Design. Florida, United States: CRC Press LLC.

Cleveland, W. S. (1993). Visualizing Data. Summit, NJ: Hobart Press.

Few, S. (2004). Show me the Numbers: Designing Tables and Graphs to Enlighten. Oakland, CA: Analytics Press.

Few, S. (2009). Now You See It: Simple Visualization Techniques for Quantitative Analysis. Oakland, CA: Analytics Press.

Tufte, E. R. (2001). The visual display of quantitative information (2^nd ed.). Cheshire: Graphics Press.

Indicative learning resources - Web based and electronic resources

https://ggplot2-book.org/index.html

https://socviz.co/

https://r4ds.had.co.nz/index.html

Key words search

Quantitative methods; data visualisation; social science; data science; statistics;

Credit value	15
Module ECTS	7.5
Module pre-requisites	none
Module co-requisites	none
NQF level (module)	7
Available as distance learning?	No
Origin date	17/02/2022