The method of placing information in a visual framework, such as a map or graph, to make it easier for the human brain to understand data and develop new insights is known as data visualization. Data visualization is primarily used to make it easier to detect patterns, trends, and outliers in large data sets. In summary, we can say that it is the presentation of data in a pictorial or graphical form.
Visuals make analysis easier and faster, while giving you the ability to see important topics at a glance. This is because our brain is very fast at processing images. As a matter of fact, it processes images 60,000 times faster than text.
In other words, visualization is a good option to discover interesting data points and grasp them in less time.
Common general types of data visualization:
- Charts
- Tables
- Graphs
- Maps
- Infographics
- Dashboards
Data Visualization Process
The goal of the visualization should be our first consideration. Every stage of visualization requires us to think about our goal. Our goals are frequently shaped to satisfy the individual requirements of the reader. These requirements can be satisfied for informational or persuasive objectives, as I described before.
You should be able to encapsulate your conceptual framework in a single line when deciding on your concept. This phrase may be a hypothetical or a question sentence.
One of the second most important points is to determine the type of visualization. It is very important that the structure of your visualization is suitable for your data.
There are four basic graphics that can be used if you just have one variable, and that variable is a numeric (quantitative) variable. However, for most numeric datasets, a histogram will do.
Histogram: Histogram graphs make it possible to assess how data are distributed. It is one of the graphs that is most frequently used, particularly for continuous datasets.
Normal Quantile Plot: To ascertain whether the data set has a normal distribution, use the normal quantile plot.
Stem and Leaf Plot: The stem and leaf plot is a technique for displaying how frequently data occurs at specific intervals.
Whisker and Box Plot: It’s used to swiftly track a dataset’s minimum, maximum, mean values, and variability/outlier states.
We only have one variable, and if that variable is a categorical variable, the following three chart are available:
Bar Chart: Bar charts show frequency numbers of values for different levels of a categorical or nominal variable.
Pie Chart: A pie chart (or circle chart) is a circular statistical chart divided into slices to show numerical ratio.
Pareto Chart: It is a type of chart that shows the frequencies in the data set from largest to smallest and shows their cumulative values with a curve.
If you have more than one variable, the most common way to visualize two numerical variables is with a scatter plot. They help to measure the correlation between variables, as well as to see the strength and direction of this correlation. If you have a dataset that you want to track with time, line charts would be the right choice. You can choose a stacked bar chart for your variables in the same category. These examples can be increased. As a result, it is very important to choose the appropriate chart for the data type in order to make the visualization correct.
After deciding on the sort of visualization as we saw in step two, we can proceed to the coding stage with the proper visual channels. Whether or not our choice of visual channels is appropriate for the various data types is one of the key issues. Finding techniques that accurately encode or map variables is our task. These fitness levels can be categorized as relational, ordinal, quantitative, or categorical.
The natural perception that takes place in our thoughts assigns that feature an automatic ordering, which determines if any visual channel has a natural order. In other words, this perception is unrelated to the factors that have been taught. The fact that it has values that the reader can detect, differentiate, and recall is another thing to take into account when picking the visual channel.
The advantages of good data visualization
Almost all industries use the technique of making data more understandable through visualization. Finance, marketing, service industries, education, sports, etc. There are important applications of data visualization in many areas that you can think of. The more you make this visualization suitable for its purpose, the more useful it will be. It is increasingly important for professionals to use data to make decisions and tell stories about when, what, where and how this data informs, using images.
