ECTS file

As data becomes easier and cheaper to generate, we are moving from a hypothesis-driven to data-driven paradigm in scientific research. As a result, we don’t only need to find ways to answer any questions we have, but also to identify interesting questions/hypotheses in that data in the first place. In other words: we need to be able to dig through these large and complex datasets in search for unexpected patterns that - once discovered - can be investigated further using regular statistics and machine learning. Interactive data visualization provides a methodology for just that: to allow the user (be they domain expert or lay user) to find those questions, and to give them deep insight in their data.


  • Background and context of data visualization and visual data analysis
  • Design as a process: framing the problem, ideation, sketching, design critique, …
  • Programming visualizations: static and dynamic
  • Project: visualization of expert dataset


Teaching assistants:

  • Danai Kafetzaki:
  • Georgia Panagiotidou:



  Date Topic
1 18/2 - ex cathedra: introduction to data visualisation: history, marks/channels/…
- technical: vega-lite
2 25/2 - part 1: ideation \& brainstorming strategies
- part 2: visual design
3 3/3 vega-lite
4 10/3 project pitches
5 24/3 vega
6 7/4 vega and python
7 28/4 vega and R
8 19/5 final feedback on projects


Grading will be based both on continuous evaluation (10%) and a written report (90%).

Student blogs

Make sure to add your blog URL to this form: