10 Free Data Visualization Tools You Need to Try

Data visualization does not have to be complicated or costly. Check out these 10 free tools that enable you to start creating beautiful visualizations right away.

Data visualization is an increasingly popular method of effectively gaining insight from data. Using tables, charts, and graphs to create enticing visualizations isn't just an interesting method of representing complex insights, it also appeals to our innate pattern-spotting abilities, assisting us in comprehending data in ways we wouldn't necessarily be able to do through the dull medium of Excel spreadsheets.

Creating visual representations of data is no longer unusual. But today, it's simpler than ever to make insightful visuals. You can create stunning and useful representations without spending a fortune thanks to the abundance of free data visualization tools, such as open source frameworks for Python and R and private platforms that require almost no technical expertise.

Without further ado, let's get started and learn how to enhance the aesthetic value of your data for free!

1. Tableau Public

It's no secret that Tableau is a serious competitor to other major analytics software. Tableau Public is a well-liked data visualization tool that can be used to make numerous types of charts, graphs, maps, and other visuals. You can easily share your visualizations with others by embedding them in websites and sharing them with friends and colleagues. The public gallery on Tableau features a wide variety of user-made visualizations. Tableau has a great and incredibly supportive user community, known as #Datafam on Twitter, that looks to help others in the community improve on their data visualization skills.

Anyone interested in creating and discovering the world of data visualization can use Tableau Public, which was developed with the help of scientists and academics. Tableau Public is a great option for anyone interested in data sharing, public collaboration, and learning data visualization through exposure to the work of others.

Pro:

Tableau Public's primary strength is the high-quality, fully functional, and interactive visualizations it provides for data analysis.

Con:

The fact that you can't save your workbook locally and have to put all of it on your Tableau Public profile makes it less useful for projects that involve private information.

2. Microsoft Power BI

Excel, Outlook, and Word aren't the only Microsoft staples. They also specialise in data visualization. While the premium edition of Microsoft Power BI includes all of its features, a free version is also available, and it provides access to some excellent data visualizations.

Power BI is another "no-code" product that makes it easy to create standard charts and graphs with a drag-and-drop interface. With more than two dozen data-viz types available, ranging from heat maps to scatter plots and tables, the free edition of Power BI allows you to connect to 70+ data sources, publish to the web, and export to PowerPoint, Excel, and CSV. There are many companies that will pay for the complete business version, so if you want to show that you're comfortable with the software, generating some simple data dashboards for your portfolio is a good idea. Make sure you take a look!

Pro:

It has a large footprint and is heavily used by a variety of businesses from all industries, making it an excellent tool to use if you want to create visualizations to help your career.

Con:

Power BI can be difficult to master. If all you want to do is import data and generate reports, Power BI is the simplest BI application to use. However, Power BI is not just one tool; it's a suite of applications designed to work together.

3. Google Data Studio

Google Data Studio is a robust data analytics and visualization tool that can be used directly in a web browser. With this tool, you can easily create reports and dashboards that are both useful and visually appealing, making them ideal for internal and external sharing and dissemination. If you already have a Google account, Google Data Studio can be easily accessed from there.

Google Data Studio is a useful tool for analysts and designers who are already very familiar with other Google services.

Pro:

It's great that data from Google Analytics, Google Ads, and Search Console can all be viewed in one place.

Con:

The customisation options are restricted compared to the other tools on this list.

4. Leaflet

Since it's essentially a JavaScript library that you'll need to integrate into your data visualization framework on your own, this is not a solution for total beginners. However, it has gained popularity due to its small footprint and excellence in creating not only static maps, but also dynamic, interactive maps optimised for mobile devices. Even some of the commercial BI solutions we've looked at can struggle to meet such a demanding standard.

Pro:

Incredibly lightweight (33 KB) with incredible map-building abilities.

Con:

Not suitable for complete beginners. If you aren't afraid of the command line or performing API calls, you should give it a shot.

5. Chartbuilder

Quartz, a popular financial news website, released this chart-making tool to the public in 2013. Quartz's in-house tool allowed its journalists to rapidly and easily visualise numerical data, giving their stories a distinct edge. Chartbuilder isn't the simplest tool for newcomers to master, and it also requires knowledge of downloading and running a Python script to begin with.

Pro:

Although Chartbuilder only generates static charts, these are quite well executed. Users can quickly move from raw data to polished published material.

Con:

The only real drawback of the tool (apart from the learning curve) is that it does not provide interactive visualizations like the majority of the others on this list.

6. Infogram

Try out Infogram if you're looking for a simple tool that strips away all technological hurdles. Infogram is all about quickly publishing data online and is aimed mostly at non-technical users and non-designers like marketers, journalists, educators, and business people. s mostly a premium tool, but you can test out its capabilities with the free version. With the free edition, you can make up to ten projects and publish them as online apps, and it contains more than 35 different types of interactive charts and amazing animations.

Unlike many of the other tools on our list, Infogram's primary focus is not on creating dashboards but rather infographics. The same limitations as Tableau Public apply, but this is a good approach to experimentation if you're more interested in playing around with various methods of making gorgeous visuals than getting bogged down in the nitty-gritty of statistical research. It shows that you don't need to be a professional data analyst to come up with results that look good.

Pro:

Infogram simplifies the creation of not only interactive visualizations but also reports, slideshows, dashboards, email subject lines, and social media posts.

Con:

The Infogram branding is quite visible in the free edition when you post an interactive chart onto your website. To get rid of it, you'll need to purchase a premium subscription.

7. Seaborn

Seaborn is a high-level interface that builds on the matplotlib Python package and allows you to make static graphics that are both beautiful and insightful. Seaborn is open-source, so you can modify the code to your heart's content and produce unparalleled visualizations.

Seaborn allows you to display the results of numerous data analytics approaches, including linear regression, time-series analysis, and others. If you're serious about creating stunning visuals that also function as data analytics tools, Seaborn is a perfect option.

Pro:

An excellent method to improve your Python skills.

Con:

The visualizations are less interactive and more static, and to address Seaborn issues, you may need to use Matplotlib in parallel.

8. Plotly

Plotly is one of the most popular data visualization applications for Python. Using Plotly, a figure that was previously static becomes fully dynamic; you can choose between seeing a single data point and numerous data points simultaneously. This interactivity takes only a single line of code, which is really potent. You can design the same kinds of functions as with Matplotlib and Seaborn, but with Plotly, you have the luxury of being able to dig into details and create histograms much more simply.

Pro:

Plotly is more powerful than Matplotlib or Seaborn since it enables the creation of rich, interactive diagrams based on the use case.

Con:

Plotly has a steep learning curve, and users will need to know Python and learn how to use Plotly's own syntax.

9. Shiny from RStudio

The majority of data analysts prefer Python because it is easier to learn; however, R is commonly used in specific fields, particularly where statistical programming is prevalent. R is more difficult to learn than Python, but it may be used to make excellent visualizations. Shiny from RStudio (commonly known as RShiny) is a R package that allows you to accomplish precisely this. Use it to create interactive web applications. These can be hosted independently on websites or embedded into larger dashboards with no web development knowledge required. Additionally, you can enhance RShiny-built applications with CSS and HTML widgets.

Pro:

An incredibly flexible tool and a large user base. RStudio also provides a step-by-step tutorial that gradually presents all the features and activities you need to know in order to maximise the effectiveness of the software.

Con:

RShiny is intended for users with no prior experience with R; however, we recommend at least a basic understanding.

10. D3

The final item on our list is yet another programming-intensive tool. Because it is designed to provide data visualization to online browsers, it also requires some web development knowledge. Once you have learned the subtleties of D3, you'll be faced with an incredibly flexible tool that enables users to let their imaginations run wild. Using scalable vectors, cascading style sheets, and numerous other modifiable elements, the visuals can be altered in any way imaginable.

Pro:

Once perfected, the outputs are visually stunning and highly engaging.

Con:

D3 is notoriously difficult to code, but there are few tutorials and instructions available on how to make the most of it.