Pros: Rich visualizations, highly-customizable, large support community.Availability: Free, open-source software.But thanks to Python’s popularity, online tutorials are on the increase, and those who have questions can usually find the answers on Stack Overflow. As is often true for open-source tools, Plotly has limited support documentation. While this still requires some coding skills, you’re unlikely to be using the tool unless you have some pre-existing programming knowledge. Through APIs, Plotly also lets you create web apps, bypassing the need for in-depth understanding of languages like JavaScript, CSS, or HTML. An array of advanced scientific and 3D charts has secured the tool’s standing in the fields of science and engineering. So, if you know how to code, Plotly streamlines the creation of graphics, charts, and dashboards. Because Plotly’s libraries are Python-based, they’re easy to integrate with other Python libraries and apps. Their various products-from Dash to Chart Studio-are open-source and highly customizable. Pros: Highly customizable visuals, with many different tools available under the Plotly banner.Ĭomputing start-up Plotly has produced several data visualization libraries, mostly built using Python.Commonly used by: Data analysts and data scientists.Availability: Open-source software with enterprise versions available.This includes those often used for data analytics, as well as some that are better suited to non-technical users. Many of these are designed to streamline-and even automate-the data visualization process.įrom free, open-source software to commercial solutions, in this post, we’ll explore our top seven data visualization tools. And while creating visualizations can be time-consuming, there’s a booming demand for data viz tools. This is also useful for creating dashboards and reports that non-data-savvy decision-makers can understand. To make sense of all this information, we often represent it visually to aid our natural pattern-spotting abilities. Working with big data means grappling with thousands, if not millions, of discrete data points. But what visualization tools can we use to help? Let’s take a look. Interpreting datasets often involves representing them visually.
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