Data Science, a well-established field in the IT industry, has been gaining prominence for nearly a decade. It proves beneficial across various industries, employing top-notch methodologies for market analysis and extracting valuable insights from data.
The data obtained is processed, with data analysts examining patterns and predicting user behavior based on this information. This is where data visualization tools play a crucial role.
In this article, we will discuss some of the best data visualization tools that data scientists should explore to streamline the process and achieve valuable results.
What is Data Visualization?
Data Visualization involves presenting analyzed data in visual forms such as graphs and images. These visual representations simplify the comprehension of analyzed trends for humans.
When dealing with extensive datasets, Data Visualization becomes crucial. It aids data scientists in grasping insights collected during the analysis, making it more accessible through graphs and charts.
Best Data Visualization Tools Data Scientists Need To Use
In the current business landscape, the choice of tools and techniques used by developers, whether Android or iOS, plays a significant role. Businesses worldwide leverage these tools to gain insights and maintain a competitive edge. Leading iOS and Android mobile app development companies utilize these tools for analyzing data sets from mobile apps, contributing to business growth and customer base retention.
Here are some of the best Data Visualization tools for every Data Scientist:
1. Tableau
Tableau is a powerful and widely-used interactive data visualization software that allows users to analyze and represent data effectively. It employs a user-friendly drag-and-drop interface, eliminating the need for users to write custom code. Compatible with various data sources, Tableau is the preferred choice for top companies like Amazon. Its real-time collaboration and data mixing features make it a versatile tool for data scientists.
Key features of Tableau:
Simple business intelligence tool for data visualization.
No custom code is required.
Real-time collaboration and data mixing capabilities.
Pros:
User-friendly drag-and-drop interface.
Compatibility with various data sources.
Real-time collaboration and data mixing features.
No custom code required for data scientists.
Cons:
Relatively expensive, which may be a drawback for smaller businesses.
Steeper learning curve for advanced features.
2. D3
D3.js is a JavaScript library designed for creating dynamic and interactive data visualizations in web browsers. Released in 2011, D3 supports HTML, CSS, and SVG, enabling developers to present data in creative and flexible ways. With powerful SVG operation capabilities, D3 integrates multiple methods for data processing, allowing data scientists to effortlessly map their data to SVG attributes.
Key features of D3:
Powerful SVG operation capability.
Integration of multiple methods and tools for data processing.
Effortless mapping of data to SVG attributes.
Pros:
Powerful and flexible for creating dynamic visualizations.
Supports HTML, CSS, and SVG.
Integration of multiple methods for data processing.
Open-source and actively maintained by the community.
Cons:
Requires coding skills, potentially limiting accessibility for non-programmers.
Steeper learning curve compared to some other tools.
3. Qlikview
QlikView is a business intelligence platform that transforms data into valuable information, similar to Tableau. While it requires payment for commercial use, it is preferred by established data scientists for its ability to analyze large-scale data effectively. QlikView integrates with a wide range of data sources and boasts fast data analysis capabilities, making it deployable and configurable across various scenarios.
Key features of QlikView:
Integration with a wide range of data sources.
Fast data analysis capabilities.
Easily deployable and configurable.
Pros:
Fast data analysis capabilities.
Integration with a wide range of data sources.
Deployable and configurable for various scenarios.
Strong community support.
Cons:
Requires payment for commercial use.
User interface may not be as intuitive as some other tools.
4. Microsoft Power BI
Microsoft Power BI is a comprehensive set of business analytics tools designed to simplify data preparation and analysis. Its seamless integration with Microsoft tools, availability for both mobile and desktop versions, and the fact that it is free to use make it a preferred choice. Power BI allows even non-data scientists to generate interactive visualizations and create machine learning models.
Key features of Microsoft Power BI:
Generate interactive data visualizations across multiple data centers.
Offers enterprise data analytics and self-service on a single platform.
Allows non-data scientists to create machine learning models.
Pros:
Seamless integration with Microsoft tools.
Available for both mobile and desktop versions.
Free to use and download.
User-friendly interface for non-data scientists.
Cons:
Limited capabilities compared to more advanced tools.
Steeper learning curve for some advanced features.
5. Datawrapper
Datawrapper is a user-friendly data visualization tool designed for non-technical users. Its standout feature is its simplicity, eliminating the need for technical skills such as coding. Widely used in media organizations, Datawrapper provides an easy interface for creating visualizations without requiring advanced knowledge of coding.
Key features of Datawrapper:
Provides users with an embed code and export options for charts.
Option to select multiple map types and charts.
Requires no advanced knowledge of coding for installation.
Pros:
Extremely user-friendly, no coding skills required.
Embed code and export options for easy sharing.
Ideal for beginners in data visualization.
Cons:
Limited customization compared to more advanced tools.
May lack some advanced features required by experienced data scientists.
6. E Charts
E Charts, an enterprise-level chart data visualization tool developed by Baidu, is a pure JavaScript chart library. Known for its compatibility across various platforms and browsers, E Charts offers multidimensional data analysis capabilities and provides a framework for the rapid construction of web-based visualizations.
Key features of E Charts:
Multidimensional data analysis capabilities.
Charts available for all-sized devices.
Provides a framework for rapid construction of web-based visualizations.
Pros:
Multidimensional data analysis capabilities.
Compatible with various platforms and browsers.
Framework for rapid construction of web-based visualizations.
Cons:
Requires coding skills for customization.
Documentation may not be as extensive as some other tools.
7. Plotly
Plotly is a data visualization tool that excels in creating intricate visualizations. Built on top of the open-source d3.js library, Plotly integrates with programming languages like Python, Matlab, and R. It offers user-friendliness, quick deployment, and access for rapid exploration and prototyping.
Key features of Plotly:
Built-in permissions and integrations with SAML.
Quick deployment of the data visualization tool.
Access for rapid exploration and prototyping.
Pros:
Integrates with Python, Matlab, and R.
Built on top of d3.js for intricate visualizations.
Quick deployment and access for rapid exploration.
Cons:
Commercial packages may have associated costs.
May not be as beginner-friendly as some other tools.
8. Sisense
Sisense provides a complete analytics solution, offering a user-friendly drag-and-drop interface for creating complex graphics, charts, and interactive visualizations. It allows the accumulation of records in easily accessible repositories and enables dashboards to be shared across groups, ensuring non-technical personnel can find solutions to their problems.
Key features of Sisense:
Various tools for understanding data in a visual environment.
Direct connection to multiple data sources.
Ties together various maps and charts.
Pros:
Complete analytics solution with a drag-and-drop interface.
Dashboards can be shared across groups.
Direct connection to multiple data sources.
Cons:
May be more suitable for business users than advanced data scientists.
License costs may be a barrier for smaller organizations.
9. FusionCharts
FusionCharts, based on JavaScript charting, is a market leader known for producing 90 unique chart types. It integrates with various systems and frameworks, providing flexibility in visualization creation. FusionCharts stands out with its ability to create visualizations from scratch and offers informative tooltips to assist users in understanding different functionalities.
Key features of FusionCharts:
Informative tooltips for users.
Ensures understanding of different functionalities.
Enables comparison of values between different data points.
Pros:
Produces 90 unique chart types.
Informative tooltips for user assistance.
Flexibility to create visualizations from scratch.
Cons:
May have a steeper learning curve for beginners.
Commercial use may require a license.
10. HighCharts
HighCharts is a widely-used data visualization tool that requires a license for business use. It is known for its compatibility and ease of use, making it convenient to add interactive charts to advanced applications. HighCharts is frequently chosen for its quick and flexible solution, even without the need for specialist statistical visualization training.
Key features of HighCharts:
Good compatibility for data visualization.
Widely used for data analysis.
Convenient for adding interactive charts to applications.
Pros:
Widely used and compatible with various applications.
Convenient for adding interactive charts to advanced applications.
Good compatibility for data visualization.
Cons:
Requires a license for business use.
Some advanced features may not be as comprehensive as in other tools.
Conclusion
The article recommends exploring various data visualization tools before making a choice. It emphasizes the importance of trial versions, demos, and comparisons with competitor tools. The mention of data monetization tools highlights the increasing role of data in driving the economy. The article suggests learning Tableau for aspiring data scientists, with the flexibility to adapt to business requirements.