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.
Presentation
Doctoral Schools – Turning research data into powerful visuals:
- Download the slides for session 1 (pdf, 25.5 MB)
- Download the slides for session 2
- Download the slides for session 3
Finding useful datasets
- Google Dataset Search: datasetsearch.research.google.com
- Kaggle: kaggle.com
- data.world: data.world datasets
- Or read our blogpost: Small datasets to practice your data visualization skills
Inspiration for data visualization
- Information is Beautiful: informationisbeautiful.net
- Information is Beautiful awards: informationisbeautifulawards.com
- Visual.ly: visual.ly/view
- Pinterest: pinterest.com
- Dear data: dear-data.com
Digital tools to create infographics and data visuals
- Canva: canva.com
- Infogram: infogram.com
- Piktochart: piktochart.com
- Visme: visme.co
- Venngage Infographic maker and Graph maker
- Whimsical: whimsical.com
- Adobe Illustrator: adobe.com
- Affinity Designer: affinity.serif.com
- Flowchart tool: draw.io
- BioRender (like Canva, but specifically for life sciences): biorender.com
- List of dataviz tools: creativebloq.com/design-tools/data-visualization
- ggplot2 cheat sheet: https://www.maths.usyd.edu.au/u/UG/SM/STAT3022/r/current/Misc/data-visualization-2.1.pdf
Illustrations and icons
- Freepik: freepik.com
- The noun project: thenounproject.com
Colour schemes
- Adobe Color CC: color.adobe.com
- Coolors.co: coolors.co
- Pictaculous: digitalinspiration.com/color-palette-generator
- Website color schemes: canva.com/learn/website-color-schemes
- 100 brilliant color combinations: canva.com/learn/100-color-combinations
- Article: Your friendly guide to colors in data visualisation
Typography
- Dafont: dafont.com
- Google Fonts: fonts.google.com
- FontSquirrel font identifier: fontsquirrel.com/matcherator
- Font Pairing: fontpair.co
Creating graphs
- Dataviz catalogue: datavizcatalogue.com
- Choose and design the perfect chart: chart.guide
- Choosing the right chart: infogram.com/page/choose-the-right-chart-data-visualization
- The Chartmaker Directory: chartmaker.visualisingdata.com
- The Data Viz Project: datavizproject.com
- Datawrapper: datawrapper.de
- Flourish: flourish.studio
- LocalFocus: localfocus.nl
- D3.js: d3js.org
Creating maps
- Snazzy maps (Google maps styles): snazzymaps.com
- Mapbox: mapbox.com
- Mapme: mapme.com/stories
- Datawrapper: datawrapper.de
- Leaflet JavaScript Library: leafletjs.com
- 3D maps with QGIS and Aerialod: statsmapsnpix.com
- Read our blogpost: Behind the maps
Creating tables
- The Ultimate Guide to Designing Data Tables
- Table inspiration: fivethirtyeight.com
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
- Twitter list: Dataviz world leaders
Videos about data visualization
- The beauty of data visualization (David McCandless)
- Journalism in the age of data (Geoff McGhee)
- Datafest Tbilisi (dozens of free talks)
- Outlier 2021 (62 free talks)
Dataviz blogs and online magazines
- Chartable (by the Datawrapper team)
- Cool Infographics (Randy Krum)
- Flowing Data (Nathan Yau)
- Infographics for the People (John Grimwade)
- The Information is Beautiful blog (David McCandless)
- Junk Charts (Kaiser Fung)
- Nightingale (by the Data Visualization Society)
- The Storytelling with Data blog (Cole Nussbaumer Knaflic)
- The Visme blog on infographics
- Visualising Data (Andy Kirk)
Dataviz podcasts
Read more:
Can you use Excel to create a powerful chart?
Spreadsheet tools such as Microsoft Excel or Numbers might not be the first thing on your mind when considering data visualization tools, but they can be pretty solid choices to build data visuals. Don’t let anyone convince you that using Excel to create data visuals is unprofessional.
Choosing the right font for your data visual
Fonts evoke emotions: there are very sophisticated fonts, playful fonts, attention-grabbing fonts, and elegant handwritten fonts. Using the wrong type of font can have a lot of impact. In data visualization the implications of typography are mainly focused on readability. Labels and annotations can easily become so small they get hard to read. Above all else, we should choose a font which is readable at small sizes.
Three roles of colour in a data visual
Colour is one of the most crucial tools we have to turn a normal chart into a powerful chart with a clear message, a chart which tells a story rather than simply presenting the information.
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?
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.
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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