Using data visualization techniques to make cool plots out of massive amounts of data is getting more popular every year. Learning about different data visualization methods will help you make effective data visualizations. According to Statista, the global data visualization market has grown from $4.5 billion in 2017 and is expected to reach $7.7 billion by 2023.
From climate change visualization to business projections, data visualization ideas can help us digest huge amounts of data and take away relevant knowledge. Effective data visualization is important in the telling of news stories and in the field of statistics. If you want to know how to learn data visualization, then read this article for some basic data visualization techniques.
Best Data Visualization Technique Examples
Data visualization is the process of creating graphical representations of information, making it easier to understand and draw conclusions from. Common data visualization techniques include pie charts, bar charts, and scatter plots. We’ll cover examples of the most effective data visualization charts below.
Pie Charts
Pie charts are an easy way to represent percentages for data that add up to 100 percent. They are simple and easy to read. Pie charts don’t work well for complex data, but they are a great way to share relevant knowledge about a budget. In fact, representing budgets as pie charts is a fundamental business skill.
Bar Charts
Bar charts, also known as a bar plot or a bar graph, work best for categorical data, where you can organize your data into distinct categories. The horizontal axis of the bar plot shows the category and the vertical axis shows the measured value. For example, you might show the average measured speed of different dog breeds. You can add a confidence interval to each bar when you are representing an averaged value.
Histogram Plot
A histogram plot is a type of bar plot used to represent continuous data rather than categories of data. For instance, you could plot the number of people driving in a car vs the time of day. The time of day would appear on the horizontal axis, and the number of people driving at that time would be represented by the height of the bar in the bar plot.
Box and Whisker Plots
The box and whisker plot, also known as a box plot, is used to show the average value and first and third quartiles of categorical data. A box plot shows if the data is distributed evenly, or if it is skewed. For instance, you can show the average test scores as well as the first and third quartile of students who take the SAT in different cities.
Violin Plots
A violin plot is a more complex version of a box and whisker plot for large data sets. In a violin plot, the width of the plot changes to show the frequency of observations. Using this visualization tool, you could see the average test scores of students who take the SAT in different cities like in the example above, but the violin plot would also show the frequency of each score.
Scatter Plots
A scatter plot is another effective data visualization technique that shows the relationship between two variables. It’s a quick way to look for trends or correlations in a data set. Scatter plots are ideal for larger data sets.
Highlight Tables
A highlight table uses different colors and shades to help readers spot trends in a large table of values. For example, you may highlight a field of statistics in green showing sales that were higher than projected, and highlight sales below the target in red. A person can look at that highlight table and see a pattern in a much shorter period of time than in a regular table.
Bullet Graphs
Bullet graphs are an effective visualization tool for businesses to view financial performance compared to their targets. The target is represented by a vertical line on a scale of values. A horizontal line runs from the left side of the scale to the actual value. If the horizontal line crosses the vertical line, then the performance exceeded the target. If the horizontal line doesn’t cross the vertical line, then the goal wasn’t met.
How to Learn Data Visualization
- Use simple data visualization software. Data visualization tools like Microsoft Excel, Tableau, Infogram, and ChartBlocks are easy to learn. You don’t need to know how to code to use these tools to make a cool plot.
- Take online data visualization courses. There are many online data visualization courses for you to learn effective data visualization techniques. These courses are short and affordable. In fact, many of them are free. Coursera is a great place to look for online data visualization courses.
- Join a data visualization bootcamp. If you want to learn a lot fast, the best bootcamps for data visualization can help you learn essential data visualization techniques. The best bootcamps also offer valuable career services to help you get a job after you finish the program.
- Watch YouTube tutorials. YouTube has tutorials available for just about any data visualization technique. Professors and scientists with different fields of expertise share YouTube tutorials so you can learn about data visualization in different industries.
- Practice. Apply your new data visualization methods to open source data sets. Make a climate change visualization using a violin plot, bar plot, or histogram plot. Learn what kinds of data work best with each kind of plot.
Data Visualization Ideas: Top 5 Tips to Master Data Visualization
Data visualization graphics should be easy for a reader to interpret. There are many ways to create data visualization graphics, and knowing which method to use can be a challenge. These tips will help you create your data visualizations.
Know Your Audience
Consider who will be using your data visualization graphic. Focus your data analytics on the kinds of questions your audience wants to answer. What relevant knowledge do you want your readers to walk away with?
Choose the Right Visual Representation
Since there are many ways to create data visualization graphics, you should choose the right one for your audience and the data you plan to present. A table can show a lot of data but can be overwhelming for the reader. A line chart will show changes and trends over a period of time. Scatter plots reveal correlations between two variables. Understand which plots are commonly used in the industry you are working in.
Provide Context
Always put labels on your graphics so readers can understand your chart without your being there to explain it to them. Remember you can use color, arrows, text, and other cues to help the reader follow the flow of information. Cite the source of your data.
Keep Data Visualization Simple
Keeping data visualizations simple and digestible is the only way to get your point across without losing your reader. If you create a graphic that is too complicated or busy, the reader won’t be able to figure out what you are trying to communicate.
Design Engaging Data Visualizations
Make a graphic that conveys all the information you want and is pleasant to look at. You can’t make an effective data visualization if your reader can’t stand to look at your graphic. Don’t use clashing colors or textures that are difficult to look at.
Are Data Visualization Techniques Worth Learning?
Yes, data visualization techniques are worth learning about if you are interested in a career in the field of statistics, like a data visualization engineer or data analyst. Data analytics are important in business too.
Data Visualization Techniques FAQ
Yes, it is hard to learn data visualization if you don’t have much computer literacy or you don’t know much about statistics. Luckily, there are bootcamps, courses, and tutorials that can help you improve at any stage.
Yes, data visualization is used in virtually every facet of business. It’s an important business skill used to provide insights on user experience, sales, manufacturing, production, financials, efficiencies, and more.
It is very difficult to become a data visualization engineer without a degree in computer science, graphic design, or data science. You can attend a bootcamp instead of a traditional school to meet this education requirement. It is worth the investment because computer and information research scientists make $126,830 a year and have a job growth outlook of 22 percent.
The average cost of attending a bootcamp was $11,727 in 2020, according to Career Karma’s State of the Bootcamp Market report. This tuition cost may seem high, but it is less than the cost of a Bachelor’s Degree in Data Analytics. To learn data visualization, you should choose an education plan that aligns with your budget and career goals.
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