You need to create a data visualization for your team. Which data science tools should you use? (2024)

Before you select a data science tool for data visualization, you need to understand your data and what you want to achieve with it. Different types of data may require different kinds of visualizations, such as charts, graphs, maps, or dashboards. For example, if you want to show the relationship between two variables, you might use a scatter plot or a line chart. If you want to compare the distribution of a variable across categories, you might use a bar chart or a box plot. If you want to show the geographic location of your data, you might use a map or a choropleth. Knowing your data and your objectives will help you narrow down your options and choose the most appropriate data science tool for your project.

Another factor to consider when choosing a data science tool for data visualization is the platform or environment that you will use to create and share your visualizations. Some tools are web-based, meaning that you can access them from any browser and share them online with your team or clients. Some tools are desktop-based, meaning that you need to install them on your computer and export your visualizations as files or images. Some tools are integrated with other data science platforms, such as Python, R, or MATLAB, meaning that you can use them within your existing workflow and code. Depending on your preferences, skills, and resources, you may opt for a web-based, desktop-based, or integrated tool for data visualization.

When you have a clear idea of your data, goals, and platform, you can start looking into some of the most popular and powerful data science tools for data visualization. Tableau, Plotly, Matplotlib, and D3.js are just a few examples that can be used for various purposes and scenarios. Tableau is web-based and desktop-based and offers interactive and dynamic dashboards and reports from different sources of data. Plotly is web-based and integrated, enabling you to create interactive and customizable charts and graphs from data in Python, R, MATLAB, or JavaScript. Matplotlib is integrated with Python to create static and animated plots and graphs. D3.js is web-based to help create interactive and creative data visualizations using HTML, CSS, and JavaScript. Additionally, you can explore other tools such as Power BI, Qlik, ggplot2, Bokeh, etc., to find the right one for your project. Comparing their features and functionalities is the best way to determine which tool meets your needs and expectations.

Once you have chosen a data science tool for data visualization, it is important to test and refine your visualizations to guarantee accuracy, clarity, and effectiveness. To improve your visualizations, you should first check the quality and validity of your data. Make sure that it is reliable, complete, and consistent. Additionally, choose the right type and style of visualization that best suits your data type, message, and audience. Enhance its readability and aesthetics with colors, fonts, labels, legends, and scales. Incorporate elements such as filters, sliders, tooltips, annotations, or links to make your visualization more engaging and informative. Finally, ask for feedback from your team, clients, or users to evaluate your visualization. Use metrics such as views, clicks, or conversions to measure the impact and performance of your visualization.

The final step in creating a data visualization for your team is to share it with them and other stakeholders or audiences. Depending on the data science tool used, you may have different options and formats for sharing visualizations, such as publishing online, exporting as a file or an image, and presenting live. Uploading to a web server or cloud service can reach a wider and more diverse audience, whereas saving as a PDF, PNG, JPG, SVG, or other formats can preserve the quality and appearance of your visualization. Additionally, displaying on a screen or projector can be used as a visual aid or storytelling tool to explain your data. Through using these steps and tips, you can create an effective and efficient data visualization for your team that will help you communicate your data. Data visualization is an important skill for any data scientist, which can be enhanced through the right data science tools to showcase work in the best way.

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

Research

+ Follow

Thanks for your feedback

Your feedback is private. Like or react to bring the conversation to your network.