Data Visualisation Course| 27/04/2019

I recently attended a Data Visualisation (DataVis) one-day course at UAL by Laura Knight.  Here are some of my (raw, personal and non-exhaustive) notes, some useful references and links. 




International Picture Language – Otto Neurath (GBooks)


  • On the difference between DataVis and Infographics
    • Opinions vary on this, but generally:
      DataVis: offers lots of data for viewer to explore
      Infographics: simpler; presents a certain narrative/view
    • Easy to mix them up, especially because even raw data isn’t neutral (since it involves choices etc.) – so anything here probably applies to both/either;
  • Choices when creating a DataVis
    • A brief helps to bring focus: 
      What is it (a graph, bar chart, heatmap etc.)
      What is it about (the topic)
      What is the audience (avoid “general audience”)
      What can they use it for (specific goal)
    • Complexity: how many elements, relationships between them, amount of data to present/emphasize etc.
    • Content > Design:
      • Find the main/essential topic/information, and put it front and center – anything else should be clearly secondary (or, ideally, removed), including labels;
      • Avoid over-designing or trying to be too clever – communicating the data is the most important thing;
      • Think outside the box: usually there will be an obvious solution for how to present a certain data set, however this might not be the best one  (location data can be displayed in a graph, for example); 
    • Presentation
      • Having said the above, presentation/design is essential for making a DataVis readable, useful and beautiful;
      • The title or subtitle (when present) can be an important element, quickly communicating the topic;
      • Doesn’t need to be 2D nor static – can be digital, animated, interactive or even physical (see this example shown by Laura);
        (I suppose it doesn’t even need to be visual – you can “visualize” any data as sound too, for example)
  • Theory
    • The goal is to turn Data into Knowledge
      (this relates to the DIKW pyramid theory – wikipedia)
    • There are different ways to organise information.
      There is a theory called LATCH that says there are only 5 ways (Location, Alphabet, Time, Category, Hierarchy)
      (I found that one to be a bit too arbitrary and general; having said that, it’s a good starting point)
    • The audience must be able to:
      1. easily find a specific information (Ex: what was the food production in 2018?)
      2. understand the relationships (Ex: food production drops during winter)
      3. what does this mean (Ex: reason of correlation)

PS: I apologize for mixing British and American English ;)
PPS: I also apologise for the lack of images in this post – I still didn’t figure out what my new website design will be :/

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