The goal of data visualization is to expose something new about the underlying patterns and relationships contained within the data. The visualization not only needs to be beautiful but also meaningful in order to help organizations make better decisions. Visualization is an easy way to jump into a complex dataset (small or big) to describe and explore the data efficiently. Many kinds of data visualization are available, such as bar charts, histograms, line charts, pie charts, heat maps, frequency Wordles (as is shown in the following image), and so on, for one variable, two variables, many variables in one, and even two or three dimensions:
Data visualization is an important part of our data analysis process because it is a fast and easy way to perform exploratory data analysis through summarizing their main characteristics with a visual graph.
The goals of exploratory data analysis are as follows:
Detection of data errors
Checking of assumptions
Finding hidden patters (like tendency)
Preliminary selection of appropriate models
Determining relationships between the variables
We will go into more detail about data visualization and exploratory data analysis in Chapter 3, Getting to Grips with Visualization.