Creative Data Visualisation (Summer University) (Spring 2023)

Abstract

Learn to make engaging, custom data visualisations by designing with data, illuminating patterns, and ultimately create meaning from tables and numbers.

Description

Visualisation is an increasingly important part of making sense of our highly networked, data-rich world. Using a combination of interpretive methods and representational techniques, we will explore creative ways visualisation can make invisible relationships, structures and stories visible. This course takes a more individual approach to designing with data, going beyond standard charts.

We will learn to draw with data, with code as our enabler, using analog and digital techniques to practice creating data visualisations. We will learn how to collect, classify, encode, and display data and use interactivity to construct relationships and meaning. Along the way, we will also learn about principles and processes of data visualisation, how to develop small projects and scope expression.

The course focuses on topics of cycles, seasonality, repetition, and time in relation to the environment, changing climate, and ecosystem. We will use data about the past, present and future of our environment.

We will begin with a warm-up to the topics of design, data, programming, getting started with our topic. We will play and experiment with data visualisation in order to become familiar with d3.js. You will then create a small project, with continued code demos, design guidance and project mentorship.

Formal prerequisites

A basic knowledge of HTML and Javascript is highly recommended. This can be achieved by taking the following short tutorials:

• Introduction to HTML/CSS
• Introduction to Javascript

Intended learning outcomes

After the course, the student should be able to:

• Explain how to collect, classify, and encode data
• Apply analog and digital techniques for making visualisations
• Describe and apply the basics of drawing with D3.js / SVG
• Reflect using metaphor, visual relationships, and interaction to make meaning out of data
• Design a data visualisation experience

Learning activities

The course will consist of lectures, group work, and project work. Students will apply techniques of data visualisation programming and sketching. Student work will be a combination of analog and digital activities, readings, research and ongoing project presentations.

Course literature

• Lupi, Giorgia: The Data We Don't See, 2017
• http://giorgialupi.com/bruises-the-data-we-dont-see
• Yau, Nathan: 7 Basic Rules for Making Charts and Graphs, Flowing Data, 2010
• https://flowingdata.com/2010/07/22/7-basic-rules-for-making-charts-and-graphs/
• Stefaner, Moritz: Little Boxes, Well Formed Data, 2016
• https://medium.com/@moritz_stefaner/little-boxes-f19ea00435a8
• Kantor, Ilya: Modern Javascript Tutorial (Introduction & JavaScript Fundamentals), Javascript Info, 2022
  
• https://javascript.info/
• Hildén, Jonathan & Koponen, Juuso: The data visualization handbook, 2019
  

• Murray, Scott: Interactive Data Visualization for the Web: An Introduction to Designing with D3, O'Reilly Media, 2017

  
  

Student Activity Budget

Estimated distribution of learning activities for the typical student

• Preparation for lectures and exercises: 20%
• Lectures: 20%
• Exercises: 20%
• Assignments: 25%
• Exam with preparation: 15%

Ordinary exam

Exam type:
C: Submission of written work, External (7-point scale)
Exam variation:
C1G: Submission of written work for groups

reexam

Exam type:
C: Submission of written work, External (7-point scale)
Exam variation:
C1G: Submission of written work for groups

Time and date