Be a top Data Science Specialist with these data visualization tools

    Niti Sharma

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    Currently whole world is living in the age of data. Without it, the businesses can’t achieve its goals. However, all the data in the world can’t help the businesses if they don’t understand what it means. In this case the data visualization comes into play.

    With the help of data visualization tools one take the data and serves it up to in the form of charts, graphs and dashboards, unlocking easy-to-read insights.

    In this article, let’s understand the data visualization and its tools briefly.

    What is Data Visualization?

    Data visualization is the representation of the data in an easy-to-digest manner, which includes charts, images, maps, and graphs. It offers better insights into data-driven details. The data analysts can also support the business’s decision-makers to identify patterns and understand complex ideas at a glance.

    Human beings can process images 60,000 times faster than text. Seeing data in a visual context helps the humans to perceive insights in the form of data analytics, trends and identify problem areas much faster than if they were looking at raw data.

    A few of the most common data visualization techniques are:

    • Pie charts
    • Word clouds
    • Bar charts
    • Gantt charts
    • Line graphs
    • Heatmaps
    • Scatterplots
    • Infographics
    • Tables

    Speed is essential for any business success. So, if the data reveals any problems within the business, one must to be able to spot and resolve those issues quickly. Since one doesn’t need to be a data science specialist to interpret easy-to-digest visualizations, employees are able to use this information when making decisions — resulting in a more data-driven business.

    The Best Data Visualization Tools

    1. Tableau – This is among the most used big data tools. It is mainly used to analyze as well as to aggregate data. With the help of these tools, the analysts can create and share the dashboards and data visualizations with other team members in hassle-free way. It is considered as a highly popular and well established data visualization tool.

    2. Microsoft Power BI – It is a Microsoft’s main datavis offering, which provides classic data visualization tool elements like interactive dashboards and APIs for integration. Individuals who use the Microsoft ecosystem can use this tool as it fits nicely. The Power Business Intelligence can use the software to prep all the data for accuracy and also can choose from a wide range of visualizations.

    3. Sisense - It is full stack analytics platform and has innovative functionality with handy features. It is highly customizable and consists of drag and drop features. Data analytics professionals who are well-versed in data and are looking for advanced features Sisense is great as it boasts the advanced functionality more commonly seen in mature software on the market, like Tableau.

    4. Dundas BI - Dundas Business Intelligence gives highly-customizable data visualizations with interactive scorecards, maps, gauges, and charts, optimizing the creation of ad-hoc, multi-page reports. By offering the users full control over visual elements. This also simplifies the complex operation of cleansing, inspecting, transforming, and modeling big datasets. It has a large variety of data sources and charts with exceptional flexibility.

    5. JupyteR – It is a web-based application and is also one of the top-rated data visualization tools that enable users to create and share documents containing visualizations, equations, narrative text, and live code. JupyteR is ideal for data cleansing and transformation, statistical modeling, numerical simulation, interactive computing, and machine learning. It facilitates easy sharing of data insights, rapid prototyping and also offers visually appealing results.

    6. Xplenty - Xplenty is a cloud-based data integration platform that prepares data for the data visualization software. It can integrate data from more than 100 data stores and SaaS applications. It offers a package designer for implementing a variety of data integration use cases. It also gives a point-and-click interface to perform simple replication, complex data preparation, and transformation tasks.