Data visualization is an art of building pictures around data. It is a creative way of presenting the information hidden in data via attractive visuals. However, besides being appealing to the eyes, these graphical representations should be easy to interpret so that meaningful conclusions about the data can be inferred by looking at them. As mentioned in the previous blog of the series, data visualization is more than just making certain figures from the data.
A data visual should exhibit the following characteristics:
Clarity: It should be easy to understand
Accuracy: It should be able to represent the data and the relationships and trends within, accurately
Concise: It should be able give the message precisely without taking much time
Guiding: It should be able to guide the reader with the applications and implications of the information or relationships identified within the data
Different visualizations of data 
With the evolution of data science several types of visualization techniques have been put forward. In other words, the same data can be put in the form of different visuals. However, for an effective storytelling, it is essential that the data and visuals are in agreement. Thus, it is very important to have knowledge about the types of visualizations that can be used with the given data and also to understand the allusion of the visual used.
In this blog of the series and several other blogs that follow, the types of visualization will be discussed along with their applications and limitations, if any.
The data visualization techniques can be divided into the following five types :