Data viz 101
Data visualisation encompasses a broad range of fields, techniques, and tools for creating visual representation of data for human consumption. The geographic and tabular data fields have rich toolsets for visualising their particular types of data, so keep on scrolling if you’re after some specific tools.
For now, read on for some of the theory behind data visualisation, some material to inspire, and lists of visualisation tools.
The theory of it all
For advice on the use of colour check out Paul Tol’s advice on good colour schemes.
The School of Data has a set of data visualization guidelines by Gregor Aisch that are worth a read.
Lastly, Juice Analytics has a good roundup at Data Storytelling: The Ultimate Collection of Resources.
Resources for inspiring
And finally Avinash Kaushik’s post on Data Visualization Inspiration: Analysis To Insights To Action, Faster! uses six short stories of data visualisation done well to inspire.
Resources for building
If you’re not sure exactly what tool you’re after and like staring at lists of tools waiting for something to leap out at you then check these out!
- Visualising Data’s Essential Collection of Visualisation Resources
- Drawing By Number’s Visualisation Tools and Resources
- datavisualisation.ch’s selection of tools for visualisation
Web visualisation tools
We couldn’t mention data vis without giving a nod to D3.js (Data Driven Documents) for creating interactive and amazingly detailed visualisations – find out more about Why D3.js is So Great for Data Visualization. Be warned though, the learning is quite steep as you’re starting out, but the web is full of thousand of D3.js examples that you should have no problems hacking into the shape you want (such as word clouds, real-time filtering of barcharts, and bubble trees for comparing sizes, and many, many more). Check out these couple of great tutorials Towards Reusable Charts and Data-Driven Documents, Defined.
Visualisation as a Service
If you’re playing with data vis on desktop you’ll find a lot of the tools are commercial in nature, but Tableau is worth a look (as well as the School of Data tutorial Analysing Datasets with Tableau Public).