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 and the Fink Lab's collection of colour schemes.

The School of Data has a set of data visualization guidelines by Gregor Aisch that are worth a read.

So too are the slidedecks for Stanford CS448B: Visualisation, who have now moved to be the University of Washigton's Interactive Data Lab.

Lastly, Juice Analytics has good roundup at Data Storytelling: The Ultimate Collection of Resources.

Resources for inspiring

If you're stuck for inspiration check out the Sunlight Foundation's Tumblr or Design Your Way's post of 23 inforgraphics to inspire.

Also worth a look are Information is Beautiful, infosthetics, and Visual Complexity.

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!

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. Bewarned 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.

Beyond D3.js have a look at Highcharts, Google Charts, jit, three.js, polychart.js, PhiloGL, and Flotr2 for a range of great web vis libraries.

Visualisation as a Service

For some quick out-of-the-box charting, Datawrapper, and both offer great charting as a service tools for easy prototyping without having to write any code.

Desktop tools

If you're playing with data vis on the 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).

Bonus: Android native charting libraries

If you're in need of toolts for building charts and graphs on android have a look at Androidplot, ChartDroid, or achartengine.