5 Types of Data Visualization

To make effective, data-driven decisions, business executives want to hire professionals with experience in business analytics. Knowing how to use data visualizations effectively is one of the foundational skills in data expertise. Learn more about different types of data visualization, including the benefits of AI in data visualization and the types of careers that use data visualization.

What is data visualization?

Data visualization is a term used to describe the process of using graphics to identify patterns or trends within a set of data. These trends allow data scientists and other professionals to draw conclusions and interpret data to make more effective decisions and strategic planning.

Why is data visualization helpful?

Data visualization helps businesses make more effective decisions. This is because it tends to be easier to read and understand data when it’s presented in a graphic format. These graphics are shareable among a team or clients, allowing for effective communication and planning. The ability to quickly respond to trends and patterns, as revealed in the data itself, means businesses can pivot strategies to save time and resources.

Data visualization helps reveal important relationships and correlations in data that allow the viewers to draw conclusions, discover new information that’s unclear when looking at raw data, and understand large amounts of data that would otherwise be difficult to parse.

How is AI used in data visualization?

Data visualization uses artificial intelligence (AI) to process large amounts of data and convert it into visual graphs and charts. Unlike people, who need to manually add data to a graph and calculate the results themselves, AI has the ability to process data in seconds and create accurate, customized visual reports.

AI is especially helpful when handling large amounts of data. It’s difficult for people to comprehend vast amounts of data independently. Still, AI’s ability to process these volumes quickly means that professionals can use their skills to extrapolate and make decisions instead of inputting numbers.

Who uses data visualization?

Data visualization is typically used by three different audiences: academics, individuals, and businesses. Academics use data visualization for their research to track trends and identify patterns over a period of time. Individuals use them for personal information tracking, such as budgeting. Businesses use data visualization to analyze their performance and make strategic decisions.

5 types of data visualization

Data visualization charts come in a wide variety of options that cater to specific data sets and goals. Five types of data visualization charts include the following:

Heat map

Heat maps show differences in data using variations in shades of color. For example, if someone wanted to see how many people live in different areas of the United States, a heat map that shows population concentration using a color gradient is a helpful choice. Heat maps use a legend to provide the user with information about what the heat map represents so that anyone looking at it can understand it.

Bar chart

A bar chart uses an axis to show a series of categories being compared while the other shows their respective values. It is helpful for data that’s being compared based on size. 

Scatter plot

Scatter plots use points that plot against a horizontal and vertical axis. These points show the connection between variables. Scatter plots are effective when used for large amounts of data and are popular in regression data analytics. 

Word cloud

Word clouds represent text data by frequency. The more a word appears within a text data set, the larger and bolder it is within the word cloud. This type of data visualization can help discover keyword trends within websites and sentiment trends within qualitative research. 

Pie chart

Pie charts allow for comparisons of different parts of a single data set. Pie charts are effective ways to compare the proportions of a data set against itself. Each slice of the pie’s size depends on that data set’s value compared to the rest of the data.

Pros and cons of using data visualization

Data visualization offers many benefits to its users, mainly by providing an effective way to view and understand large amounts of data. Visual graphs and charts allow the viewer to see trends, patterns, and correlations that the raw data might otherwise hide. These insights are helpful for decision-making since they provide context rooted in solid data. 

The cons of data visualization tend to come from choosing the wrong type of data visualization chart. It’s possible to misrepresent your data sets if entered into the wrong chart, so it’s important to know what you’re trying to learn through the use of a certain type of chart. Another con of visualization is drawing incorrect or inexact conclusions.

How to get started in data visualization

If you’re interested in working within a field that uses data visualization, the first step is to become familiar with the foundational skills needed for data analysis. Some skills to consider include design, statistics, and analytics. Online courses offer an excellent way to learn the concepts and hone relevant skills. After developing your skills, gaining proficiency, and building a portfolio representing your expertise, you’ll be ready to apply for entry-level jobs.

One such role is a data visualization specialist. These professionals work across various sectors, including governments, health care, and technology. Data visualization specialists usually need a background in computer science, and their average annual salary is $80,329, according to Glassdoor’s December 2023 data [1].

Getting started with Coursera

Sharpen your data visualization skills with courses and degrees on Coursera. With options like IBM’s Data Visualizations and Dashboard with Excel and Cognos, you’ll have the chance to learn the foundational skills required to pursue a career that uses data visualizations. You could also take Share Data Through the Art of Visualization, a course offered in the Google Data Analytics Professional Certificate Program on Coursera.