Data Visualisation 2019/20

This is the only website where to find learning material about the course. The website will be updated regularly thoughout the course with the latest slides and tutorial information. Learn will be used for assignments only! Bookmark this page.

Course content and learning outcomes

The course aims to provide a general understanding of how to use data visualizations in your work and how to be critical about it. The course has three specific learning outcomes which you assignments will be marked against.

1. Analysis: The ability to identify and describe a visualization challenge in terms of context, stakeholders, data, and tasks.
2. Design: Design and implement a visualization through one of various media (interactive, infographic, data comic) and through a self-chosen set of tools. Visualization designs are meant to match an earlier identified challenge.
3. Evaluation: Critically reflect on a visualization design and suggest constructive solutions to identified challenges. ’

The course teaches:

• perceptual foundations,
• visualization design basics and design steps,
• visualization guidelines,
• a range of visualization techniques (visual representations) and when to use them,
• tools for visualization design and visual data exploration,
• methods to evaluate visualization designs (for evaluation and improvement).

Course assignments will require you to find a data set you would like to work with during the course. Students are free to chose the data set they would like to work with. If you are not sure about the data you chose, talk to me after the lecture. Student work around data on climate change can enter a competition for a possible public exhibition/conference in November, associated to the COP26 in Glasgow. Data can come from any source as long as privacy and copy rights are respected: the internet, an external collaborator, the student’s own research, another course the student is taking, personally collected data.

Course Organization

This course has 11 lectures, 5 tutorials and 2 assignments. Any lecture is 2h, including 90min lecturing, a 10min break, and 20min for question and answering. Each course week comes with a small home work which should not take more than XX h.

There are 2 assignments, both need to be handed in. There is no written exam. Students will work individually for assignment Assignment 1 and in groups of 3 for Assignment 2.

A detailed description and slides for each lecture is found here

Date	Session	Lecture	Tutorial	Assignments
Jan, 13	1	Foundations I: Introduction to Data Vis	—	—
Jan, 20	2	Foundations II: Visualization design	T1: Critique+Redesign	—
Jan, 27	3	Foundations III: Tools for data visualizations	—	—
Feb, 03	4	Techniques: Visualizing Statistical and Multivariate Data	T2: Challenge+Design	—
Feb, 10	5	Techniques: Multivariate data and Trees	—	Assignment 1
Feb, 17	-	Week of creative learning	—	—
Feb, 24	6	Techniques: Networks and Geography	T3: Data and Tools	—
Mar, 02	7	Techniques: Temporal and Interaction		—
Mar, 09	8	Advanced: Storytelling	T4: Storytelling	—
Mar, 16	9	guest lecture	—	—
Mar, 23	10	Advanced: Evaluating vis	T5: Evaluation + Open Atelier	—
Mar, 30	11	Topic Lecture	—	Assignment 2

Recommended Literature

• Alberto Cairo: The Truthful Art
• Segel, Heer, Narrative Visualization, 2010, https://egerber.mech.northwestern.edu/wp-content/uploads/2015/02/Narrative_Visualization.pdf
• Bertin, Semiology of Graphics, 1987
• Nussbaumer: Storytelling with Data, 2017: http://www.storytellingwithdata.com
• Tufte: Visual Evidence: Images and Quantities, Evidence and Narrative, 1997
• Colin Ware: Information Visualization, 1999
• Tamara Munzner: Visualization Analysis & Design, 2014
• Scott Murray: Interactive Data Visualization for the Web An Introduction to designing with D3, 2013
• Manual Lima: Visual Complexity Mapping Patterns of Information, 2011
• Ben Shneiderman: The eyes have it: A task by data type taxonomy for information visualizations, 1996
• Panday et al: How deceptive are deceptive visualizations: An empirical analysis of common distortion techniques, 2015
• Von Landesberger et al: Visual analysis of large graphs: state of the art and future research challenges, 2011
• Tamara Munzner: A nested model for visualization design and validation, 2009

Additional Course material (optional)

• Books and sources here
• Visualization collections and sources here
• Visualization tools and sources here