Course Introduction to Data Visualization

The course 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 2027.

ECTS credits: 1.5 ECTS

Course parameters:

• Language: English
• Level of course: PhD course
• Time of year: Spring 2027

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

Capacity limits: 45

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:
• Participants will be able to generate tailored solutions for a data visualization scenario at hand (dataset, reading task) and justify their visualization design.
• 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 (Discussion)

Preparation for Day 2:

•  Assignment: Prepare Black Hat Visualization

Day 2:

• Multivariate Visualization Techniques (Lecture)
• Advanced Color Scales (Lecture)
• Discussion of Solutions from Black Hat Visualization Exercise (Discussion)
• Speed Visualization Critiquing (Exercise)

Preparation for Day 3:

•  Prepare own visualization problem for design exercise

Day 3:

• Visualization of Spatial & Temporal Data (Lecture)
• Visualization Design using the 5 Design Sheets Method (Exercise)
• 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)
• Peer Feedback on the Data Visualization of 2 other participants
• Revision of the Data Visualization according to the feedback
• 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, Cartograms
• 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, semantic color-coding
• Structured visualization ideation: Five design sheets method

Prerequisites: none

Name of lecturer: 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%) and completion of all three assignments                   

Time:
9.00 am-3.30 pm with a 30min lunch break at 12pm-12.30pm

No-show fee:
To pass a course, you must be physically present at least 80 per cent of the time. Homework, assignments etc. do not count as participation. (i.e. up to 20 per cent of absence is accepted).
As a rule, a no-show fee of DKK 1,200 (the price of one ECTS) will be charged if

• you are absent more than 20 percent of the time, or
• you cancel after the cancellation deadline

The only exception to this rule is registered illness, death of nearest relatives etc.

In case of late cancellation due to illness, we may require a doctor’s note from you.

You must notify the course secretary in case of absence due to illness no later than 9:00 on the day of the course.

(*For online courses, the no-show fee is DKK 500).

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 note that you will be required to provide payment details in the course registration module. Your registration will not be considered valid if this information is missing or incorrect.

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:

• 31 March 2027 09:00 - 15:30
• 01 April 2027 09:00 - 15:30
• 02 April 2027 09:00 - 15:30