VISIBLE DATA LAB
Instructional goals
In the age of big data, charts, visualizations and infographics have become the preferred way to access vast amounts of information. However, we often barely look at them, thinking of them as direct or automatic transcriptions of the data they provide.
Instead, data and information visualizations are communicative artifacts of great complexity, capable of conveying not only data but also the worldview of those who collected, interpreted and translated it into visual form.
The aim of the lab course is to provide students with the skills to consciously engage with the visual representation of information, both from an analytical and a design point of view.
Prerequisites
There are no pre-requisites for attendance.
Course Contents
The lab course is made up of 8 lessons, spread over 4 days of 6 hours each, equally divided between moments of reflection on themes, theoretical concepts, operational and design tools, and moments of teamwork, in which students can test themselves on the various phases that make up a data communication project, starting from a shared dataset.
Reference Books
A. Cairo, The functional art. An introduction to information graphics and visualization, New Riders, 2013.
A. Cairo, How Charts Lie: Getting Smarter about Visual Information, W W Norton, 2019.
M. Friendly, H. Wainer, A History of Data Visualization and Graphic Communication, Harvard University Press, 2021.
V. Manchia, “Beyond immediacy and transparency. A semiotic approach to discursive and rhetorical strategies in media visualization and data visualization”, «Punctum. International Journal of Semiotics», 2022, vol. 8, n. 1, pp. 85-113, <https://punctum.gr/volume-08-issue-01-2022-semiotic-approaches-to-big-data-visualization/>.
L. Manovich, Cultural Analytics, MIT Press, 2020.
E.R. Tufte, The visual display of quantitative information, Graphics Press, 1983.
E.R. Tufte, Envisioning Information, Graphics Press, 1990.
E.R. Tufte, Visual Explanations. Images and Quantities, Evidence and Narrative, Graphics Press, 1997.
Teaching Methods
Lectures, analysis and discussion of case studies, teamwork.
Assessment Method
Final teamwork project to be presented at the end of the course.
Week 1
Day 1, Lesson 1. Learning to read data and information visualizations as complex visual and communicative artefacts: introductory remarks.
The language of infoviz. Focus on some themes and concepts of information design.
Day 1, Lesson 2. Beyond immediacy and transparency. Data display as visual translation.
Introduction and commentary of the dataset for work on the final team projects.
Day 2, lesson 3. Analysis of communication strategies in some examples of data and information visualization (Part 1).
Preparatory activity for work on final team projects.
Day 2, lesson 4. Analysis of communication strategies in some examples of data and information visualization (Part 2).
Classroom work on final team projects and revision.
Week 2
Day 3, lesson 5. Development of a shared workflow for data visualization.
Classroom work on final team projects and revision.
Day 3, lesson 6. Designing data. Designing a communication strategy.
Classroom work on final team projects and revision.
Day 4, lesson 7. Final revision on final team projects.
Day 4, lesson 8. Final team projects presentations and discussion.