Visualization has become an integral aspect of data analysis in today's data-driven environment. Businesses may obtain insights and make data-driven choices by using a variety of successful data visualization approaches. This article will examine the many types of data visualization.

What is Data Visualization? 

Before learning about types of data visualization, let’s learn about what is data visualization first.

The art of presenting your data and information as graphs, charts, or maps is known as data visualization. Data visualization's purpose is to emphasize observations that would not otherwise jump out when looking at a linear list of values and numbers to enable people to quickly and easily grasp their data.

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How to Select the Appropriate Graph or Chart for Your Data?

To successfully express your message and insights, selecting the appropriate chart or graph for your data is essential. The following factors need to be considered while choosing the optimal data visualization:

  • Purpose

What are you trying to visualize? Are you attempting to demonstrate contrasts, patterns, or connections in your data?

  • Type of Data

What kind of data do you have? Is it a numerical or category list? Both continuous and discrete? This will aid in choosing the best types of data visualization charts.

  • Context

What context does your data come from? Is it recent or historical? Local or worldwide? This will enable you to choose the proper scale and coverage for your visualization.

Most Common Types of Data Visualization

There are many types of data visualization. The most common types are:

1. Column Chart 

They are a straightforward, time-tested method of comparing several collections of data. A column chart may be used to track data sets across time.

2. Line Graph

A line graph is used to show trends, development, or changes through time. As a result, it functions best when your data collection is continuous as opposed to having many beginnings and ends.

3. Pie Chart

In a pie chart, a single, constant number is represented by the several categories that make up its parts. You will portray numerical quantities in percentages when you employ one. All of the various components should sum up to a hundred percent when totaled.

4. Bar Chart

To compare data along two axes, use bar charts. A visual representation of the categories or subjects being measured is shown on one of the axes, which is numerical.

5. Heat Maps

A data visualization method that uses colors to denote values; great for seeing trends in huge datasets.

6. Scatter Plot

The correlation between variables is examined using a scatter plot. At the point where the data's two values overlap, the data are represented on the graph as dots.

7. Bubble Chart

A variant of the scatter plot where the size and color of the bubbles, which represent the data points, provide extra information, are used to depict the data points as dots.

8. Funnel Chart

To illustrate a sequential process from top to bottom, a funnel chart's principal purpose is to represent it graphically. As the process flows down, the amount generally decreases, making the data set at the top of the process greater than the bottom.

9. Radar Chart

Radar charts are a sort of data visualization that aids in the analysis of objects or categories in light of a variety of attributes. The radar chart consists of a circle with concentric rings, and the data are shown as dots on the chart. The shape is then formed by connecting the dots. Each thing or group has a shape.

10. Tree Chart

An alternative to a table for precise numerical data is a tree chart, often known as a tree diagram. The basic goal of a tree chart is to represent data as pieces of a larger whole within a category.

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11. Flow Chart

One extremely adaptable method of data display is the flowchart. Use mind maps for brainstorming, flowcharts to depict a process graphically and hierarchical data of objects or people.

12. Gauge

A gauge is a percentage visualization. There are a few uses for the half-doughnut-like form. To display a percentage figure with an arrow pointing to it is the simplest use. If you have a small quantity of data to work with, this is a fantastic option.

13. Gantt Chart

Horizontal bar graphs are the basis for the Gantt chart; however, they differ significantly from them. A rectangle that extends from left to right stands for each item on the chart. Depending on how long each activity takes to accomplish, each one varies in size.

14. Venn Diagram

A Venn diagram is a data visualization that compares two or more objects by emphasizing their similarities. The most typical Venn diagram design consists of two overlapping circles.

15. Histogram

While a histogram and a bar graph are similar, they use distinct charting systems. The ideal sort of data visualization for frequency-based analysis of data ranges is a histogram.

16. Waterfall Chart

A style of bar graph that demonstrates how a sequence of positive and negative numbers affects an initial value.

17. Marimekko Chart

A graphic depiction called a Marimekko chart shows category data using stacked bar graphs of various widths. Mekko charts and mosaic plots are other names for the same type of diagram.

18. Choropleth Map

The technique of color mapping symbology is used to create choropleth maps, which are themed maps used to display statistical data. It shows geographically segmented sections or regions that are colored, shaded, or patterned according to a data variable, known as enumeration units.

19. PERT Chart

PERT is a technique for calculating the least amount of time needed to finish a project by analyzing the amount of time needed to complete each job and the dependencies related to it.

20. Dichotomous Key

An identification chart called a dichotomous key allows users to choose between questions and assertions offered in the chart to arrive at a conclusion that will assist them in identifying objects or anything else.

21. Mind Map

Using a radial layout to represent thoughts and ideas, mind maps are data visualization that helps organize and spark ideas while dealing with complicated material.

22. Timeline

They are visual depictions of a historical period with significant events labeled in chronological order. They may be more detailed visuals or rather straightforward linear representations.

23. Concentric Circles

A style of data visualization known as concentric circles makes use of circles inside circles to represent hierarchical connections or proportions, with the size of the circles signifying the amount of data being displayed.

24. Radial Wheel

With each spoke or segment denoting a separate category or value, radial wheels are a style of data visualization that uses a circular structure to highlight connections between data elements.

25. Percentage Bar

A type of data visualization known as percentage bars use a horizontal bar with proportional segments to show numbers as percentages of the total and the relative size of each category.

26. Donut Chart

Donut charts, often called doughnut charts, are variants on pie charts that include a hole in the center, giving them the appearance of doughnuts. This open space may be used to display further information.

27. Half-Donut Chart

The half-doughnut chart is precisely what its name suggests—it's a half-doughnut chart. When displaying modest amounts of data, this type of data visualization is a useful option. A half-donut chart should, ideally, not include more than three wedges.

28. Polar Graph

If the data values are substantially dissimilar from one another, choose a polar graph as the types of data visualization in data science. If not, it could be difficult to read at a glance.

29. Icon Array

Icon arrays are a type of data visualization that works well for displaying proportions and patterns because they employ icons or symbols to represent individual data points, such as circles or squares.

30. Cone Chart

Hierarchy is depicted with a cone chart. The greatest value data is located on the broadest section of the cone. The other values are distributed in descending order from top to bottom of the cone.

How To Choose The Right Type Of Chart: Questions To Ask?

To ensure that you select the optimal visualization approach for your data, it is crucial to ask the right questions when choosing the style of chart or graph to employ. Consider the following questions:

  • What sort of narrative am I attempting?
  • How much data do I have?
  • What audience am I presenting for, and how much complexity and depth do they need to comprehend my data?
  • What kind of data do I have?
  • How can I create a compelling and clear visualization?

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Best Data Visualization Tools

Among the top types of data visualization tools are:

  • Google Charts
  • Tableau
  • Grafana
  • Chartist
  • Datawrapper
  • Visual.ly
  • RAW

Conclusion

In conclusion, choosing the appropriate form of data visualization can be crucial for effectively expressing the patterns and insights buried in complex data. Businesses and individuals may harness the potential of data visualization to improve decision-making and outcomes by knowing the advantages and disadvantages of various data visualization techniques. If you wish to master these techniques, you must enroll in our Post Graduate Program In Business Analysis today!

FAQs 

1. What main kinds of data visualization are there?

Bar charts, line charts, scatter plots, pie charts, and heat maps are a few of the prevalent types of data visualization.

2. What are the data visualization field's key objectives?

Data visualization's primary objectives are to convey insights, trends, patterns, and correlations in data in a simple and obvious manner.

3. What advantages can data visualization offer?

Data visualization can potentially improve the communication of detailed information and speed up data processing.

4. Why is data visualization effective?

Data analysis and comprehension are aided by data visualization because it uses the visual system to spot patterns and correlations swiftly.

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