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 13/2 introduction to data visualisation: history, marks/channels/…
2 20/2 vega-lite
3 27/2 visual design
4 5/3 vega
5 12/3 pitch project ideas
6 19/3 feedback on visual design with TAs
7 26/3 vega and python
8 2/4 vega and R
9 23/4 No session
10 30/4 final feedback on projects
11 7/5 feedback visual design for communication
12 14/5 poster session with TAs


These assignments are to be prepared for the next session.

  Deadline Assignment
1 20/2 add your blog URL in this form:
2 27/2 - critique
- vega-lite exercises
- submit 1-page proposal for 2 projects
3 5/3 visual design exercise
4 12/3 vega exercises
5 19/3 visual designs for project
6 26/3  
7 2/4 vega/python exercises
8 23/4 vega/R exercises
9 30/4 pre-final implementation of project
10 7/5 visual designs for communication project
11 14/5 poster for communication project


Grading will be based both on continuous evaluation (15%) and a written report (85%).

Student blogs