Introducing Data Visualization and D3
Every second, netizens generate a tremendous amount of data, such as shopping habits, location, and browsing history. The data collected is analyzed and insights are drawn, which drives new businesses.
While the data analysis is fueled by machine learning and artificial intelligence, a unique requirement has quietly emerged in recent times—the ability to tell the story behind an analytical exercise. This has led to the emergence of a variety of graphing tools. Data is only as good as its presentation. Visualizations can, therefore, greatly simplify complex data and explain insights.
With the inevitable use of data analytics in web and mobile applications, there’s an increasing demand to effectively communicate results through crisp visuals. The ability to create data visualizations makes a developer stand out from the rest in the job market.
As with most technologies, the reckless use of commonly available charting tools can lead to very confusing graphical representations of data. This book will tackle what one should keep in mind when preparing visualizations for an audience.
In the next chapter, we’ll look at possible data sources before we begin the process of data visualization. We’ll start with static data in the form of flat files, and move on to more complex sources like databases on the cloud and other real-time data.