Data Visualization Tools Every Data Scientist Should Know

5 Data Visualization Tools Every Data Scientist Should Know

Data visualization is a must-have skill for all data professionals for extracting insights and communicating findings effectively. Effective data visualization not only helps understand data but also facilitates decision-making and storytelling.

If you are a data analyst or a data scientist, you’ve probably used Python data visualization libraries like matplotlib and seaborn within the Jupyter notebook environment. But there are data visualization tools—beyond the Python libraries—you should know about. These are very helpful to create and share findings and insights with team members and stakeholders alike.

This article explores five such data visualization tools you can add to your data science toolbox. We’ll go over the key features and use cases for each of the tools. Further, we’ll look at learning resources that can help you take the next best step with these tools. Let’s get started.

1. Tableau

Tableau is a popular data visualization tool you can use to create visualizations, shareable dashboards, and BI reports.

Tableau has a friendly UI that supports drag-and-drop functionality. So users can create visual representations of data even if they’re not super comfortable with coding.

Key Features

Let’s review some of the key features of Tableau:

  • With Tableau’s drag-and-drop interface, you can create visualizations without requiring coding skills.
  • Tableau connects to a range of data sources including databases, spreadsheets, and cloud-based data platforms.
  • It offers a variety of visualization options such as bar charts, line charts, scatter plots, maps, and more.
  • Users can interact with visualizations by filtering data and drilling down into details as needed.

Use Cases

Tableau is used for BI and reporting:

  • Creating interactive dashboards for monitoring key performance indicators (KPIs) and trends.
  • Analyzing large datasets—performing exploratory data analysis—to uncover insights and patterns.
  • Generating visually appealing reports and presentations for stakeholders.

Learning Resources

If you’d like to learn Tableau, check out the following resources:

2. Power BI

Power BI is a popular BI and data visualization tool from Microsoft. Like Tableau, Power BI allows users to connect to multiple data sources, create visual reports, and share insights.

Key Features

Now let’s go over Power BI’s features:

  • Power BI integrates with SQL Server, Excel, Azure, and other data sources.
  • Power BI supports data modeling. So you can build data models and relationships for complex analyses.
  • With Power BI, you can create charts, graphs, maps, and custom visuals.
  • It also allows sharing of dashboards and reports with stakeholders for collaborative decision-making.

Use Cases

Here are a few uses of Power BI:

  • Common data exploration application to explore and visualize data to identify trends and patterns as well as monitoring business metrics.
  • Enterprise reporting – creating comprehensive reports and dashboards for presenting results of analyses.

Learning Resources

Check out the following resources to get started with Power BI:

3. Looker Studio

Looker Studio, formerly Google Data Studio, is a free tool from Google that enables users to create customizable dashboards and reports by connecting to data sources such as Google Analytics, Google Sheets, BigQuery, and more.

Key Features

The features of Looker Studio are as follows:

  • Integrates well with Google services and other third-party data sources
  • Provides extensive customization options for designing reports and dashboards
  • Supports real-time collaboration, allowing multiple users to edit and view reports simultaneously
  • Facilitates easy sharing of reports and dashboards with stakeholders

Use Cases

Looker Studio has the following applications in analytics, visualization, and reporting:

  • Marketing analytics – visualizing and analyzing digital marketing performance metrics from Google Analytics.
  • Tracking key metrics and KPIs across different departments or projects.
  • Creating client-facing reports that are interactive and easy to understand.

Learning Resources

The following resources should help you get started with Looker Studio:

4. Plotly

Plotly is an open-source Python library for creating interactive and web-based visualizations. Yeah, I did say you need to go beyond Python libraries; but Plotly is great for interactive data visualization and creating web apps for your Python applications.

It supports a wide range of chart types and can be used for both simple exploratory data analysis and production-garde applications. Plotly Dash extends Plotly’s capabilities to create interactive web applications for data visualization.

Key Features

Let’s list some of Plotly’s capabilities:

  • Allows users to create highly interactive visualizations – interactive charts and graphs with zoom, hover, and click events.
  • Supports a variety of chart types including scatter plots, bar charts, line charts, heatmaps, and more.
  • With Plotly Dash, you can create interactive dashboards and web applications for data exploration.
  • Works well with Jupyter Notebooks, Flask applications, and other Python frameworks for deployment.

Use Cases

The use cases of Plotly are fairly straightforward, but let’s list them. With Plotly, you can:

  • Create dynamic and interactive visualizations for your datasets.
  • Visualize complex data sets and simulations with interactive features.
  • Build custom dashboards and web applications for data-driven decision-making.

Learning Resources

To get up and running with Plotly, check out:

5. D3.js

D3.js, Data-Driven Documents, is a JavaScript library for creating interactive visualizations—charts and graphs—in web browsers. It manipulates the Document Object Model (DOM) based on data, offering all the flexibility and customization you’d ever need.

You’ll find D3.js particularly helpful if you’re someone transitioning from software development to data science (as you’d already be comfortable with JavaScript then).

Key Features

Some features of D3.js include:

  • Allows manipulation of HTML and SVG elements.
  • Allows for the creation of custom and complex interactive visualizations

Use Cases

D3.js is commonly used for:

  • Creating visualizations—charts and maps on the web—tailored to specific data analysis needs.
  • Building dashboards for real-time data monitoring and analytics.

Learning Resources

If you’d like to learn D3.js, you’ll find the following helpful:

Summary

Let’s summarize the tools, their key features, and common use cases:

Tool  Key Features Use Cases 
Tableau Drag-and-drop interface, easy integration with data sources, various visualization options, interactivity Business intelligence, data exploration, reporting
Power BI Seamless data integration, diverse visualization options, data modeling, collaboration Real-time analytics, data exploration, enterprise reporting
Looker Studio Integration with Google services, extensive customization, real-time collaboration, easy sharing Marketing analytics, performance monitoring, client reporting
Plotly Interactive visualizations, wide range of chart types, dashboards, integration with Python Interactive data visualization, dashboard applications
D3.js Direct DOM manipulation, custom visualization, animation, interactivity Custom interactive visualizations on the web

Whether you’re an experienced data professional or a beginner in data science, becoming proficient in one or more of these tools will help you distill data into helpful visualizations and communicate your data insights better. So happy data visualization!

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