Data visualization is a fast and simple way to convey messages and concepts in an efficient manner. Besides improving the information relay, this approach has also changed the way business is done. Organizations that deal with large amounts of information on a daily basis can now process data and have the intended audience decipher it at lightning-fast speeds, thus enhancing the process of information management. However, data visualization mistakes are quite widespread, and let's take a look what they are, why they are harmful, and how to avoid them. Data Visualization (often called shortly as DataViz) is one of the oldest concepts of the science of perception and many people are wrong to believe that the use of pictures, graphs, and charts in information communication technology is a fairly new idea.
Data visualization is a key step in the data science process. Choosing the right graphic to explore data or to convey insight efficiently is an everyday task for a data scientist. From Data to Viz is a classification of chart types based on input data format. It comes in the form of a decision tree leading to a set of potentially appropriate visualizations to represent the dataset. The project is built on two underlying philosophies.
Data visualization is a great way to represent huge amounts of data in a simple and intuitive fashion. All data visualizations have the same goal: help viewers easily grasp information to make quick inferences or decisions. However, it is important that visualizations are not overdone and hit the sweet spot where they are catchy, informative, and easy to navigate. This requires a bit of learning. Putting up a good data visualization is not just a matter of throwing together some data in colorful charts.
Determining whether or not you need a visualization is step one. While it seems silly, this is probably something everyone (including myself) should be doing more often. A lot of times, it seems like a great way to showcase the amount of work you have been doing, but winds up being completely ineffective and could potentially harm what you're doing. Once you determine that you actually need to visualize your data, you should have a rough idea of the options to look at. This post will explain and demonstrate some of the common types of charts and plots.