Interesting, yet I don't know why Tableau, Perl, Java and Python libraries, and SAS are not included...Anyway, here's the article:
At the entry level, we'll be looking at unexpected uses for familiar tools. You might not think of Excel as a visualisation package, for example – but it's capable of surprisingly complex results. If you are just getting started, these tools are musts to understand. If you deal with visualisations every day, you'll quickly find yourself advancing beyond them, but not everyone will, so you'll always be dealing with data coming in from sources you'd rather not deal with.
It isn't graphically flexible, but Excel is a good way to explore data: for example, by creating 'heat maps' like this one
You can actually do some pretty complex things with Excel, from 'heat maps' of cells to scatter plots. As an entry-level tool, it can be a good way of quickly exploring data, or creating visualisations for internal use, but the limited default set of colours, lines and styles make it difficult to create graphics that would be usable in a professional publication or website. Nevertheless, as a means of rapidly communicating ideas, Excel should be part of your toolbox.
Excel comes as part of the commercial Microsoft Office suite, so if you don't have access to it, Google's spreadsheets – part of Google Docs and Google Drive – can do many of the same things. Google 'eats its own dog food', so the spreadsheet can generate the same charts as the Google Chart API. This will get your familiar with what is possible before stepping off and using the API directly for your own projects.
3. Google Chart API
Read article at http://www.netmagazine.com/features/top-20-data-visualisation-tools