Course Introduction to Data Visualization

The course N/T 624/01 Introduction to Data Visualization is being offered by the Graduate School of Natural Sciences/GSNS and Graduate School of Technical Sciences/GSTS, Aarhus University, spring 2025.

Course parameters: No. of contact hours/hours in total incl. preparation, assignment(s) or the like: 40hrs (3x7hrs course days + 3x1 hour lunch + 3x2hrs preparations + 10hrs visualization project + 3hrs online chart clinique)

Objectives of the course: Data visualization is the science and practice of encoding often large and complex datasets into visual representations to support their analysis and communication. Such a visualization can be worth more than a thousand words, as the saying goes -- but only if done right. Thus, the aim of this course is threefold in:

• conveying the theoretical aspects and guidelines of a data visualization that make it a truthful and useful representation of the underlying data;
• practicing to create tailored data visualizations that follow these guidelines through a structured ideation process and a deliberate design approach; and
• training to critically read data visualizations and to reason about their validity and effectiveness on the basis of the introduced guidelines, and to suggest improvements where necessary.

Learning outcomes and competences:

• Subject Knowledge: Participants will be familiar with key concepts and issues within the subject area of data visualization (see course contents).
• Practical Skills:
  • o Participants will be able to generate tailored solutions for a data visualization scenario at hand (dataset, reading task) and justify their visualization design.
  • o Participants will have proficiency in assessing given data visualizations for their strengths and limitations (representational errors, perceptual deficiencies).
• Academic Proficiency: This course places particular emphasis on analytical and conceptual aspects, so that participants can successfully identify and navigate the trade-offs often to be made when visualizing data.

Compulsory programme:
Preparation for Day 1:

• Assignment: Prepare own visualization for discussion

Day 1:

• Introduction to Data Visualization (Lecture)
• Introduction to Visual Literacy (Interactive Lecture)
• Visualization Critique I (Discussion)
• Vega-Lite Tutorial (Demo)

Preparation for Day 2:

• Assignment: Prepare own visualization problem for design exercise

Day 2:

• Multivariate Visualization Techniques (Lecture)
• Introduction to Color Scales (Lecture)
• Visualization Critique II (Discussion)
• Visualization Design (Exercise)

Preparation for Day 3:

• Assignment: Prepare Black Hat Visualization

Day 3:

• Visualization of Time-oriented Data (Lecture)
• Solutions from Black Hat Visualization Exercise (Discussion)
• Visualization Critique III (Discussion)
• Course Recap (Quiz)

Homework (individual, ca.4 weeks time to complete)

• Design and Preparation of a Data Visualization for data from own research
  (e.g., figure for an upcoming paper submission or thesis)
• Online “Chart Clinique” to present and briefly discuss the created figures

Course contents:

• Aspects of data visualization: Marks, Channels, Perceptual precedence
• Criteria of “good” visualization: Expressiveness, Effectiveness
• Common mistakes in visualization: Lie factor, Perceptual equidistance, Histogram vs. Bar chart, Data normalization, Binning, etc.
• Advanced charting techniques: Scatterplot matrices, Parallel coordinates, Trellis plots, Mosaic charts, Glyphs, Horizon charts, Time maps, Triangular model
• Advanced color mapping: single-hue vs. multi-hue color coding, divergent color scales, two-tone pseudo coloring, bivariate color scales, histogram equalization, task-driven color coding
• Structured visualization ideation: Five design sheets method

Prerequisites: None, no programming knowledge required.

Name of lecturer: Assoc.Prof. Hans-Jörg Schulz

Type of course/teaching methods:

Data Visualization is a topic in which there are usually no clear-cut right or wrong solutions, but where different data representations have different advantages and drawbacks that need to be weighed against each other to find the right chart for the visualization scenario at hand. To convey this discursive nature of Data Visualization and to enable course participants to practice this skill themselves, the course is designed around active participation and lively discussions in groups and in the plenum – in particular across disciplines and their respective representational traditions and opposing opinions. To foster this active participation and to ensure the best possible learning experience for all participants, Data Visualization is exclusively run as an in-person, on-campus course.

Teaching in this course follows the system-activity approach, which is a combination of both traditional ways of illustrative presentation of material, and methods that involve an independent research and learning process. The goal of this approach is a holistic view on the course topic in which knowledge and practice complement each other to enable critical thinking and analysis as a basis for more truthful and useful data visualizations.

Literature: None required. Lecture slides with bibliographic information for further reading will be made available.

Course homepage: Brightspace

Course assessment: Attendance threshold (80%)

No show fee:

Course participants on our transferable skills courses, who do not show up at the course or cancel their course participation after the course cancellation deadline (without providing a doctor’s note), may have to pay a no-show fee, unless someone from the waiting list is able to take part in the course instead.

The no-show fee is DKK 1,200 (the price of one ECTS). The no-show fee has been introduced due to many late cancellations, thus preventing people from the waiting lists to have a seat at the courses.

Registration:

• Participation in the course is without cost for PhD students from Aarhus University

Due to an Agreement between Danish Universities that came into force as of 1 January 2011, participants from other universities than Aarhus University will have to pay DKK 1,200 per ECTS. In principle this also applies to external parties, but exemption can be granted under specific circumstances.

Please be aware that your registration for the course not necessarily equals your admission for the course. You will receive an e-mail after the registration deadline regarding whether you are admitted for the course or if you are registered on the waiting list. Please note that seats are allocated on a first-come-first-served basis.

Course dates:

• 12 March 2025 08:00 - 16:00
• 13 March 2025 08:00 - 16:00
• 14 March 2025 08:00 - 16:00