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Visualisation of Information – FREE COURSE

Martin Mölder

University of Tartu

Martin Mölder (PhD in comparative politics) is a researcher Johan Skytte Institute of Political Studies at the University of Tartu, Estonia.

His main research focus is political parties, their ideological and political positions, and the functioning of party systems. He also teaches, among other things, quantitative methods.

Martin has extensive background in the use of R for data management and statistical analysis in the social sciences.

He has taught the following courses at the ECPR Summer School in Methods & Techniques:

  • R Basics 2016 & 2017
  • Intermediate R: Capacities for Analysis and Visualisation 2017, 2018 & 2019
  • Advanced Topics in Applied Regression 2019


Course Dates and Times

Monday 17 – Friday 21 February, 08:00–08:50
This is a FREE supplementary course for anyone who has registered and paid for a one-week or two-week course.
To book, please check the box when registering.

Prerequisite Knowledge

This course is for students of all disciplines, quantitative and qualitative. 

It does not require any prior knowledge.

Short Outline

Being able to convey data, in whatever form, and the results of your analyses visually ensures that your message gets to your audience as effectively as possible. And having the knowledge to read visualisations quickly and be able to detect either intended or unintended distortions is a part of general scientific literacy. 

This short course seeks to give you a basis for both – an understanding of what can be visualised and how, and a critical eye on what is out there.

The course will not focus on technical skills, although short materials will be provided (because as practice has shown again and again, technical skills do not prevent one from making an ugly or dishonest graph), but rather on the more general principles that are behind good information visualisation.

Long Course Outline

Our eyes can help us understand, but also to misunderstand the world. They do not reflect the world as it is, but rather create a mental representation of it with gaps and biases. This course thus begins with a session where we focus on how some of our cognitive mechanisms can lead us astray and how that informs us about some of the basic principles that should be followed in data visualisation. 

Each day aims to give you an overview of one piece of the puzzle, with examples of good and bad practices, as well as some technical implementation along the way. 

Day 1
We look at some of our built-in cognitive mechanisms and how they influence our perceptions. Some colours, forms and shapes are easier to perceive and to correctly evaluate. We should do a favour to our audience and use them. 

Day 2
We go to the opposite extreme by focussing on what not to do – visualisations that are bad in style and taste as well as ones which out of ignorance or malice deceive the intended audience. If you know how to lie with statistics, then you also know how to avoid lying by accident. 

Day 3
We get a bit more specific and try to think of what types of data and what types of variables are out there and what kinds of plots we can make either to most effectively show a single variable or several variables together. 

Day 4
We focus on statistical models and their visualisation. A regression table is something that even (or especially) some seasoned social scientists cannot fully interpret, but a well designed and presented visualisation of model results is something that almost everybody can understand. And if you are not going to make them yourself, then you at least will know what you see when you come across them in a text. 

Day 5
We spend this day on the border between the academic and the non-academic, where the visualisation of information meets art. We will go over examples of modern and classic complex data visualisation and discuss, and focus on some aspects of what make them so great.

Day Topic Details
1 Cognition and good visualisation

How do we perceive shapes and colours?
What kinds of visualisations does it make sense to make? 

2 Learning from others' mistakes

What kinds of visualisations does it make sense not to make?

3 Plotting simple data

What kind of data goes together with what kind of a plot?

4 Plotting models

How to plot regression models and how to understand such plots

5 Envisaging complex data

How to tell a good story and how to please the eye with complex visualisations

Day Readings

Cleveland, W.S. and McGill, R., 1984
Graphical perception: Theory, experimentation, and application to the development of graphical methods
Journal of the American Statistical Association, 79(387), pp.531–554


Healy, K., 2018
Data Visualization: A Practical Introduction, Chapter 1
Princeton University Press


Wilke, C.O., 2019
Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures, Chapters 6, 7, 12
O'Reilly Media


Healy, K., 2018
Data Visualization: A Practical Introduction, Chapter 6
Princeton University Press


Segel, E. and Heer, J., 2010
Narrative visualization: Telling stories with data
IEEE Transactions on Visualization and Computer Graphics, 16(6), pp.1139–1148


Cairo, A., 2012
The Functional Art: An introduction to information graphics and visualization
New Riders

Cleveland, W.S. and Cleveland, W.S., 1985
The elements of graphing data
Monterey, CA: Wadsworth advanced books and software

Healy, K., 2018
Data visualization: a practical introduction
Princeton University Press

Steele, J. and Iliinsky, N., 2010
Beautiful visualization: Looking at data through the eyes of experts
O'Reilly Media, Inc

Tufte, Edward R. 1992
The Visual Display of Quantitative Information
Graphics Press

Tufte, Edward R. 2003
Envisioning Information
Graphics Press

Tufte, Edward R. 2006
Beautiful Evidence
Graphics Press

Ware, C., 2010
Visual Thinking: For Design

Wilke, C.O., 2019
Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures
O'Reilly Media

Yau, N., 2013
Data Points: Visualization that Means Something
John Wiley & Sons

Additional Information


This course description may be subject to subsequent adaptations (e.g. taking into account new developments in the field, participant demands, group size, etc). Registered participants will be informed in due time.

Note from the Academic Conveners

By registering for this course, you confirm that you possess the knowledge required to follow it. The instructor will not teach these prerequisite items. If in doubt, contact the instructor before registering.