Statistical graphs are one of the most important tools of a data scientist. Typically every single data analysis begins with explorative data analysis (EDA) which is basically visualizing the data. This step tries to identify clear relationships in the data, abnormalities - e.g. outlier - and helps to generate hypothesis. 

Of course this is not the only application of statistical graphs. Perhaps even more importantly graphs are used as a tool to present and explain results. A scientific graph often says more than a thousand words.

Static Graphs

The most common approach in graphing data is of course the static graph. Single features of a data set are presented as a combination of dots, bars or similar graphical elements. This helps the observer to notice the important structures of the data on a glance.

Nowadays statistical software is not limited to two-dimensional graphs but provides many three-dimensional graphs and special features like for example geographical maps.

Interactive Graphs

The newest trend in data visualization is the use of interactive graphs. Making use of multiple user interface elements the observer is able to change and edit the graph which leads to a very different and deeper understanding of the data. 

Many statistical programs allow to interactively change colors, zoom into relevant subsets of the graph, add additional elements or just to shift the perspective of the graph. This is not only possible inside of a specific software but these interactive graphs can be exported to HTML- or FLASH-files that can easily be published via the internet.

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