Turn messy, jumbled, and complex data into beautiful visualizations with data tools.
Data visualization software is an integral part of working in data whether you’re a data scientist or data analyst. By effectively visualizing data, you can draw meaningful insights, patterns, anomalies, outliers, and much more.
Additionally, datasets are often far too big to be able to draw insights from without aggregating and sorting data. Imagine trying to work with thousands, if not millions of data points. The brain cannot interpret such large amounts of information.
It helps that our eyes are drawn to colors and patterns.
Below are a few common ways to visualize data:
- Charts (Gantt, area, bar)
- Graphs (bar, line, wedge stack)
- Maps (dot distribution, heat, tree)
Below you'll find a selection of data visualization tools are used across the industry, and have varying levels of applicability depending on the data you work with, as well as the audience you want to educate.
Top Data Visualization Tools
Tableau is an interactive data visualization tool that’s known for its ease of use and ability to drag and drop features into a dashboard.
Of particular note is the fact that Tableau can be used for mapping data from a multitude of sources (not just Excel) as it allows one plot latitude and longitude coordinates. Tableau is an excellent tool for anyone working with data, and can be particularly useful when trying to visualize and present data in a digestible way, with real-time changes.
Tableau is covered in our Intro to Data Analytics part-time course, and involves connecting a Microsoft Excel worksheet to visualize data. Overall, Tableau offers a wide range of functionality in terms of working with data sets from various sources and performing statistical analysis on them.
With Tableau, toggling between dashboards is also easy and intuitive. You can see a number of public datasets and dashboards in Tableau’s gallery here to give you a sense of what’s possible.
Seaborn is a Python data visualization library based on Matplotlib.
It provides a high-level interface for drawing attractive and informative statistical graphics. A sample of functionalities include:
A dataset-oriented API for examining relationships between multiple variables Specialized support for using categorical variables to show observations or aggregate statistics Options for visualizing univariate or bivariate distributions and for comparing them between subsets of data Automatic estimation and plotting of linear regression models for different kinds dependent variables
ECharts is a free, powerful charting and visualization library offering an easy way of adding intuitive, interactive, and highly customizable charts to your commercial products.
Power BI is well-suited for working with limited amounts of business data, and is effective in predictive modeling, reporting, and optimization.
As Power BI is a Microsoft tool, it’s the go-to tool for users of Microsoft’s suite of tools including Excel. Similarly, Power BI’s advantage is Microsoft’s business analytics that includes platforms such as Azure Machine learning, SQL Server based Analysis Services, data streaming in real time, and other Azure databases offers.
Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python.
Highcharts is a simple and flexible charting API used, according to Highcharts, by 80 out of the world’s 100 largest companies, making it among the most widely-used charting tools. Of particular note, Highcharts It makes it easy to add interactive charts to web and mobile projects.
If you’re looking to play around with Highcharts, it’s free to use for personal usage.
D3.js is powerful but it has a big learning curve
Vega is a visualization grammar, a declarative language for creating, saving, and sharing interactive visualization designs. With Vega, you can describe the visual appearance and interactive behavior of a visualization in a JSON format, and generate web-based views using Canvas or SVG.
Learn more about the data visualization tools we teach in our bootcamp and part-time introductory program, and how it fits in within the data profession by downloading our curriculums below.