Visualizing quantitative data
The main goal of data visualization is to convert numerical tables into visual components such as plots or charts that could be more easily investigated and interpreted to find hidden relation in the data at hand. In order to create an informative and compelling visualization it is important to know your data type and the relation that you are looking for to visualize in order to choose the best plot or chart type for your data visualization story telling platform.
There are few patterns or interesting insights that would help to create an informative visualization such as:
Trends: to show change of a variable versus another parameter such as your company sales versus time.
Correlation: to show relationship and compare two different groups of data points such as showing the correlation of your company sales versus number of employees.
Outliers: to identify the unusual behavior or patterns in your dataset marked as outliers such as showing your company sales versus unusual regions that customer resided to purchase your product.
In order to have a successful data visualization, it is important to know your data type. There are several common data types:
Quantitative: this data type is just numerical and includes all the information that could counted, measured, or calculated.
Categorial: this data type is mostly shows the category of data points or what group they belong to such as type of products that your company produce.
Discrete: this data type takes certain values instead of including all the possible values in a given range.
Continuous: this data type can takes all the possible numerical values in a given range such as temperature of a reactor in your company's workshop.
In the next section, we will learn about various data relationships as well as different plot or chart types to create a compelling data visualization.