# Data Visualization Terminology

Data visualization is closely connected with statistics. That is why similar terms and terminology are involved and used. Data visualization derives from data science. It takes large and complex data and shows it in a visual way. Statistics and statistical data are the most common types of data presented by data visualization softwares.

We can divide that data into two groups: categorical and quantitative.

*Categorical data* - Presents and stands for a group of objects that show a certain feature or characteristic. This is the type of data that has categorical variables. They can be ordinal or nominal. Ordinal variables come with an order. For example, grouping people depending on their age and demographics. Nominal, on the other hand, have no order between them. We can take gender for example.

*Quantitative data* – This is the type of data that represents measurements. For example, this is the data that comes from measuring height or external temperature. These variables can be divided also. They can be discrete or continuous. The continuous data (variables) put the idea that calculations and measurements can always be improved to be even more precise. The discrete variables have a more restricted and limited number of possibilities and outcomes.

It is important to distinguish between these two categories of variables. That is because each one of them requires different methods and techniques of visualization.

We can further divide the visualization methods into two types:

• A table

• A graph

A table is the type of chart that has quantitative data. The data is organized in columns and rows. Each has categorical labels. A table is used mainly to find specific values. For example, it might have a categorical column label. It represents a name and the age. Each row of the table represents one person.

A graph, unlike a table, is used to show the connection and relationship between data. It also portrays values. These values can be encoded as visual objects. If there is numerical data, the values are showed in one specific area. That area is delineated by one or more axes. These axes provide scales (quantitative and categorical) used to label and assign values to the visual objects. Many graphs are also referred to as charts.

*To this day data has evolved so many times. Many educational centers and schools of thought have developed various perspectives when it comes to when and how to use data visualization. And, as technology progresses, the whole market on data visualization progresses simultaneously. We can only make close approximations about the scale the market is at and will be at. It is a gigantic industry that has only began evolving. We can’t imagine what the future of data visualization will look like, but it will be the best to prepare ourselves for a whole new digital era coming at us.*