Data visualization: all the resources you'll ever need!

You want to start creating clear and attractive data visuals, but don’t know where to start? No worries, here’s a complete overview of tools, resources and inspiration you can use as a starting point for your designs.

Finding useful datasets

Inspiration for data visualization

Digital tools to create infographics and data visuals

Illustrations and icons

Colour schemes

Typography

Creating graphs

Creating maps

Creating tables

Ethics in data visualization

  • Dealing with outliers: https://flowingdata.com/2018/03/07/visualizing-outliers/
  • Visualizing projections and missing data: https://flowingdata.com/2018/01/30/visualizing-incomplete-and-missing-data/
  • Spurious correlations: https://www.tylervigen.com/spurious-correlations
  • About dynamite plots: http://biostat.mc.vanderbilt.edu/wiki/Main/DynamitePlots
  • Alternatives to box plots: https://nightingaledvs.com/ive-stopped-using-box-plots-should-you/
  • Visualizing the uncertainty in data (Flowing Data): https://flowingdata.com/2018/01/08/visualizing-the-uncertainty-in-data/
  • Visualizing uncertainty (Claus Wilke): https://clauswilke.com/dataviz/visualizing-uncertainty.html

Books about infographics and data visualization

  • Data visualisation, Andy Kirk
  • Dear Data, Giorgia Lupi & Stefanie Posavec
  • Information graphics, Taschen
  • Infographic designers’ sketchbooks, Steven Heller & Rick Landers
  • Storytelling with data, Cole Nussbaumer Knaflic
  • The visual display of quantitative information, Edward Tufte
  • Trees, maps and theorems, Jean-Luc Doumont
  • Visual journalism, Gestalten
  • Visual thinking, Willemien Brand

People on Twitter talking about data

Videos about data visualization

Dataviz blogs and online magazines

Dataviz podcasts

Books on a bookshelf - infographics resources

Read more:

thumbnail for video 07 - 7 different goals for your chart

7 different goals for your chart

A crucial step in building a powerful chart is choosing the right type of chart. A lot of charts don’t work because they simply use the wrong type of chart. To avoid this trap, we must ask ourselves a basic question: what’s the ultimate goal of our data visual? What do we want to show with our data?

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thumbnail for video 06 - making a data visual noise-free

Making a data visual noise-free

Removing noise from a data visual is not only about taking things away such as gridlines, axes or legends. That’s just one part of it, which we could call removing physical noise. Improving the signal-to-noise ratio is often also about adding little things that help our audience better understand the visual. We are helping them by removing mental noise, or mental barriers.

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Three tips to create powerful charts in Excel

Creating charts in Excel can be a very powerful tool for making sense of complex data sets, and for visualizing them. But the default options are not always the most pretty or effective ones. Here are our top three tips to create better Excel charts.

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thumbnail for video 05 - a powerful chart has a high signal-to-noise ratio

A powerful chart has a high signal-to-noise ratio

‘Less is more’. It’s a crucial principle in most of our communication, and in data visualization in particular. Because of my background as a physicist, I prefer to talk about the ‘signal-to-noise ratio’. The message - our signal - should be amplified as much as possible, giving it all of the attention. Everything that can distract from our message - the noise - should be removed.

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thumbnail for video 04 - a powerful chart tells a story

A powerful chart tells a story

A powerful chart has a clear message. It should be short and meaningful, and obvious in the blink of an eye. If there’s only one thing our audience remembers at the end of the day, this should be it.

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Navigating the landscape of powerful charts

Once we’ve decided to create a data visual or infographic, there are a lot of questions we should ask ourselves to determine the most suitable format. These considerations could include size, readability, possible interactive functionality, and the level of detail we need.

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