Regardless of where you work or what you do, everything around you is defined by metrics. What is the price of a house in Boston? What’s the mileage of your car? How many cups of coffee are sold at Starbucks every year? More importantly, how much impact should you make to take the next big step in your career?

Being human beings, we like all our questions to be answered in a concise and comprehensive manner. Over the years, data visualization techniques have become more sophisticated. May it be expressing wildfire data using state of the art GIS systems, providing market forecasts to your prospective investors using a combination of Tableau,D3.js and Bokeh, expressing higher dimensional data using parallel coordinates or even the old fashioned number crunching using MS Excel. The greatest innovations of data visualization is taking place in our very own Big Data realm, where we need to find sophisticated way of looking at millions of data points in clever metrics of visualization. Just Google and explore!

With all these sophisticated data visualization techniques out there, I am sure that a lot of you want to touch on something simple to start out with. While MS Excel is a quick yet simple option, there is a lack of flexibility in terms of how to process your data, the level at which you can manipulate your plot handles and even sharing them across a wide range of media (i.e. web interfaces, GUIs and mobile applications). A fantastic starter, in my humble opinion, is Matplotlib.

Matplotlib is an open source Python library with a wide community of developers. Almost any complex visualization problem can easily be solved with Matplotlib’s rich array of functions. The syntax is fairly similar to Matlab (click here for details) and Octave, for those of you wanting a quick transition. On top of that, the plot handle can be seamlessly interfaced with a wide array of Python based applications.

With that, here is my basic tutorial on Matplotlib to get all of you on board the great voyage of data visualization: