Mastering Python Data Visualization Generate effective results in a variety of visually appealing charts using the plotting packages in Python

The existence of the Internet and social media in modern times has led to an abundance of data, and data sizes are growing beyond imagination. How and when did this begin?

A decade ago, a new way of doing business evolved: of corporations collecting, combining, and crunching large amount of data from sources throughout the enterprise. Their goal was to use a high volume of data to improve the decision-making process. Around that same time, corporations like Amazon, Yahoo, and Google, which handled large amounts of data, made significant headway. Those milestones led to the creation of several technologies supporting big data. We will not get into details about big data, but will try exploring why many organizations have changed their ways to use similar ideas for better decision-making.

How exactly are these large amount of data used for making better decisions? We will get to that eventually, but first let us try to understand the difference between data, information, and knowledge, and how they are all related to data visualization. One may wonder, why are we talking about data, information, and knowledge. There is a storyline that connects how we start, what we start with, how all these things benefit the business, and the role of visualization. We will determine the required conceptual framework for data visualization by briefly reviewing the steps involved.