Data Science

Data Visualization Tools

Posted in Data Science
Data Visualization Tools

What is Data Visualization?

We know that data visualization provides information by representing the significant amount of data into a visual representation. Data visualization involves various aspects like information technology, statistical analysis, graphics, natural science, geographic information, and interaction. It is one of the data science processes which is developed by Joe Blitzstein. It also refers to the framework for approaching data science tasks after collecting, processing, and molding the data so that users can find a conclusion from it. Data visualization is a component of the broader discipline of data presentation architecture (DPA), which identifies, locates, manipulates, formats, and presents data in the most effective way. Data visualization is becoming an essential part of every technology these days because it provides convenience as well as the best outcome possible. Majorly data visualizations combination of three branches, and they are:

1. Scientific Visualization

It is interdisciplinary research that focuses on the visual representation of three-dimensional aspects like meteorology, architecture medicine in the field of science. The primary purpose of data visualization is to represent the data in an appropriate form for scientists so that they can understand, collect patterns, and explain the information from the data. Scientific visualization helps the user to represent the data into 3D or in a more understandable format.

Scientific visualization

2. Information Visualization

Information visualization is the complete study of interactive visual representations of abstract data to improve human cognition. The abstraction of the data includes non-digital and digital data like graphical information or a text. Graphical information includes histograms, tree diagrams, trend graphs, and flow charts.

Information Visualization

3. Visual Analytics

This field evolved due to the development of information visualization and scientific visualization with an emphasis on analytical reasoning by an interactive visual interface. It is an outcome of the fields of information visualization and scientific visualization that focuses on analytical argumentation helped by interactive visual interfaces.

Visual Analytics

Data Visualization Technology

Data visualization can be achieved in various ways. Some are manual, and some are entirely automatic; the techniques are:

  • Basic mathematics: trigonometric function, the geometric algorithm, and linear algebra,
  • Engineering algorithms: basic algorithms, standard layout algorithms, and statistical algorithms.
  • Visual basis: visual coding, graphical interaction, and visual analysis.
  • Design aesthetics: design principles, color, interaction, cognition, and aesthetic judgment
  • Graphics: Canvas, computational graphics, SVG, graph theory, and WebGL.
  • Visualization solutions: correct use of charts and visualization of common business scenarios
  • Data analysis: data cleaning, data modeling, and statistics.

There are specific branches in data visualization, so there are various tools that are used to create a graphical representation, and these tools consist of different specifications.

Top Data Visualization Tools

Hence let's discuss these data visualization tools in brief:

1. HighCharts

HighchartsIt is the complete chart library written in JavaScript that makes it easy and convenient for the user to turn the data into interactive charts. It is mostly used on the web and businesses that require the purchase of a commercial license to use this tool. HighCharts consist of excellent compatibility, but it is hard to expand these charts.

Pros

  • Various charts are available with lots of options.
  • Good documentation
  • Excellent browser support
  • Supports Y axes
  • Libraries of Highchart supports microsoft.NET

Cons

  • It is not free because it is used for commercial projects.
  • Examples are not significant enough.

2. D3

D3.jD3.js is based on the JavaScript libraries according to the data manipulation documentation, and it combines data-driven DOM manipulation methods with powerful visualization components. It consists of high SVG operation capability because it can easily map the data to the SVG attribute.

Pros

  • It is different from other chart solutions because it provides complete control to the developer.
  • Its documentation is also available.

Cons

  • The process is a bit time consuming
  • It does not support the old browser and internet explorer 8.

3. Echarts

EchartA data visualization team of Baidu created an enterprise-level chart tool which is known as Echarts. It is the complete script chart library that can operate on both PCs and mobile smoothly.

Pros

  • It is free to use and easy to implement.
  • It is highly compatible with all devices.

Cons

  • It is not flexible enough as compared to other chart libraries that are based on descriptive grammars.
  • It isn't straightforward to customize some of the complex relational charts.

4. Vega

VegaIt is a complete set of essential interactive graphical grammars that uses the mapping rules from data to standard interaction grammars, graphic, and numerous graphical elements.

Pros

  • Users can create a variety of charts by combining Vega grammar.
  • It is easy to use for its interactive interface.

Cons

  • The grammar design is a bit complex
  • The cost of the process is high.

5. FineReport

FinereportIt is a tool written in pure java as it is an enterprise-level web reporting tool. The design of FineReport is based on the "no-code development" concept, and the user can create complex reports, a cool dashboard, and a decision-making platform. It is convenient and easy to customize different complex reports, and the interface of this tool is similar to Excel.

Pros

  • Easy to create complex reports by the data
  • Data entry is smart.

Cons

  • Multiple data analysis is inconvenient
  • No recommendation of chart feature

6. Tableau

TableauIt is used to visually analyze data because it is a business intelligence tool, and users can create and separate depicting trends, shareable dashboards, and densities of data in charts. This tool provides convenience to the user so that they don't have to write custom codes because it allows the data mixing and real-time collaboration.

Pros

  • Great visual image capabilities
  • This tool provides multiple information supply connections.

Cons

  • High cost and inflexible valuation
  • Security Problems and poor after-sales support

7. Power BI

Power BIIt provides insights into the organization because it is a set of business analysis tools, and it can easily connect various data sources to simplify the data preparations and provide quick analysis. Users can view Power BI reports on both mobile and web devices. It is similar to Excel, but its functions are more potent than excel.

Pros

  • This tool has excellent affordability
  • Custom visualization and excel integration

Cons

  • Tool interface is pretty crowded
  • Configuration of visuals

8. Leaflet

LeafletIt is a JavaScript library of all interactive maps for mobile devices, and it consists of useful mobile features that fulfill the needs of most developers. It is compatible with mobile because it is targeted for the map applications.

Pros

  • It provides good ROI
  • It is suitable for excellent targeting.

Cons

  • You need an appropriate mailing list for the process
  • It requires the creation of a quality product.

9. deck.gl

deck.glThis tool is based on WebGL, and it is a visual class library for big data analytics. It focuses on the 3D map visualization, and a team of Uber develops it. However, it requires good knowledge of WebGL to operate it conveniently.

Pros

  • It is easy to use
  • This tool provides high performance

Cons

  • This tool has cost issues
  • Poor BI capabilities

10. Sisense

Sisense offers immediate insights for the user anywhere in the organization. It enables a user to generate visual dashboards as well as reports to state every data, reveal underlying patterns, and make data-driven decisions.

Pros

  • It has a friendly user interface.
  • Excellent support
  • Easy upgrades
  • Integrates amazingly with various data sources.
  • This tool is flexible and provides easy customization.

Cons

  • Hard to manage and improve analytic cubes.
  • A limited type of visualizations.

Conclusion

Data visualization is a vast field, and it cannot settle with several tools because data visualization is used in various fields to have quick decisions for future profits. There are many tools available in the current scenarios, and every tool has its specialty and drawbacks. The tools discussed above are widely used in different sectors to solve problems quickly. Are you deciding on to start using a tool or already using one and thinking of making the shift? If yes then which tool do you prefer? Let us know!

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Simran Kaur Arora

Simran Kaur Arora

Simran works at Hackr as a technical writer. The graduate in MS Computer Science from the well known CS hub, aka Silicon Valley, is also an editor of the website. She enjoys writing about any tech topic, including programming, algorithms, cloud, data science, and AI. Traveling, sketching, and gardening are the hobbies that interest her. View all posts by the Author

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