10 Types of Data Visualization You Can Use for Your Reports
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Data can be visualized in a variety of ways for a variety of reasons.
We will look at 10 of the most common and most useful methods of data visualization and how they can be used to represent your data.
What Is Data Visualization?
Data visualization is a way of representing data in a graphical or pictorial format.
This makes it easier for decision-makers to identify patterns and trends among data, helping them to make more informed decisions. It also makes data more presentable, allowing it to be shared in a more eye-catching and digestible way.
10 Types of Data Visualization You Can Use for Your Reports
Here are 10 of the most common types of data visualization.
1. Bar Chart
One of the most common types of data visualization is the bar chart. Sometimes referred to as a column chart, this graph compares data along two axes; the vertical axis, or ‘y’ axis, and the horizontal axis, or x-axis.
In a horizontal bar chart, the numerical values will be on the x-axis, while the measured categories will be on the y-axis, resulting in horizontal bars. In a vertical bar chart, the contents of the axis are reversed, resulting in vertical bars.
Bar charts lend themselves well to using color coding systems accompanied by a legend. This allows for more complex data sets to be displayed in an easily digestible manner.
2. Pie Chart
Pie charts are another common form of data visualization. Unlike a bar chart, which represents numerous categories of data, a pie chart depicts a single variable broken down into various proportions.
The pie chart depicts an entire data set, while each segment of the chart represents a specific category within the data. As such, the sum of all the individual portions should be 100%.
Pie charts highlight proportional data, such as demographics, market shares, etc. They work best with data with five or six categories, allowing each section to be easily visible. Making each portion a different color can assist in this.
A line chart is a data visualization method that depicts how data changes over time. Like a bar chart, it contains an ‘x’ and a ‘y’ axis; however, in a line chart, both axes will represent numerical values.
The quantitative measurement will be plotted along the vertical axis, while the horizontal axis will represent the relevant time frame. The data point for each time interval will be plotted with dots, which are then connected with a line.
The line will highlight positive or negative trends over time. Line charts can feature only one line, or can display multiple categories by plotting several lines.
4. Scatter Plot
A scatter plot is a graph used to analyze the correlation between two variables. The horizontal axis features one variable, and the vertical axis another, with points plotted on the graph at the intersection of the two values.
For example, the below graph depicts the relationship between the height in cm and weight in kg of 1,477 schoolchildren. Scatter plots are best used to represent larger datasets that do not feature time as a variable.
5. Venn Diagram
Venn diagrams show the relationship between different variables, highlighting their similarities. They do this by overlapping the various shapes representing the variables at points, indicating common factors.
If two shapes don’t overlap, this indicates the opposite; those two factors do not share that feature. Venn diagrams are useful as a descriptive medium to highlight relationships, but do not lend themselves well to depicting the extent of these relationships.
6. Choropleth Map
Maps can be a great way to display data visually, helping to link data sets to geographic concepts. For example, choropleth maps represent regional statistical values like population densities.
The data is divided into various value sets, each of which is then assigned a color. These different colors will often be shades of the same base color, with the color becoming darker as the value increases, to represent the relationships between the data better visually.
7. Radar Chart
Radar charts are a data visualization method used to represent data incorporating more than one variable. Radar charts consist of
concentric circles representing values, plotting data points as dots.
Dots common to a category are connected with lines, producing a shape when all the points have been connected. These shapes will often be shaded in different colors to display numerous comparable categories on one chart.
Tables are a data visualization method well suited to displaying large data sets. They organize data into rows and columns, meaning several pieces of information about many entries can be displayed together. Various cells can be highlighted to draw attention to specific pieces of data.
Tables also play a key role in many aspects of data management, such as facilitating the construction of reliable data pipelines during ETL (extract, transform, load) processes.
A boxplot is a type of data visualization useful for displaying a dataset’s key statistics. They can highlight the minimum and maximum values and median and average values of a set of data.
Boxplots can display variation in a data sample without necessarily making assumptions about its distribution. They can help those analyzing data to get to grips with its key points before drawing conclusions about it.
Flowcharts are an incredibly versatile type of data visualization, which can be used to depict processes such as model risk management, summarize a strategy proposal, layout training procedures, or in several other ways.
They typically consist of a header shape, which branches out into a series of other shapes connected by lines or arrows. Arrows will help to show the stages of a process in order.
Getting the Most From Your Data Through Visualization
Data visualization is extremely important for a range of industries, whether you’re a marketer determining the ROI of your campaigns or a data scientist analyzing various types of information in big data.
Determine which types of data visualization are most appropriate for what you’re trying to achieve, and then utilize them to draw clearer conclusions and better present your data to a variety of audiences.
About the Author
Pohan Lin is the Senior Web Marketing and Localizations Manager at Databricks, a global Data and AI provider connecting the features of data warehouses and data lakes to create lakehouse architecture, with over 18 years of experience in web marketing, online SaaS business, and e-commerce growth. Pohan is passionate about innovation and is dedicated to communicating data’s significant impact in marketing. Pohan Lin also published articles for domains such as Spiceworks and IT Chronicles.
Disclaimer: The author is completely responsible for the content of this article. The opinions expressed are their own and do not represent IEEE’s position nor that of the Computer Society nor its Leadership.