Tableau is one of the most popular tools for data visualization in the field of data science. If you’re new to data analysis and business intelligence, you’ll find that you’re constantly being exposed to new terms and procedures. It will quickly advance to the status of a keyword in your vocabulary. What exactly is it? In this article, we’ll discuss and examine the various applications that data analysts utilize it.

What is Tableau?  – A Quick History

Tableau was created by three students from Stanford in 2003 as the outcome of a computer science project and is fundamentally a tool for data visualization. It was developed to make data intelligible to regular people.

The business intelligence community regards it as the most widely used visualization tool in the sector because of how essential and intuitive its functions are, making it simple to develop meaningful dashboards quickly. It is an all-encompassing platform that was created with the needs of business users. If you are completely new to this field, feel free to check out the best data analytics course, and master the basic skills you will need to succeed as a data analyst. 

What products does Tableau offer?

It offers a comprehensive range of tools that help users every step of the way—from data preparation to sharing—as they analyze data, providing governance and data management support along the way. It regularly publishes updates and patches along with new releases quarterly.

Several Tableau products are as follows:

  • Data is cleaned, combined, shaped, and transformed using the visual interface of Tableau Prep. It makes it simple to combine fields from several data sources, merge fields, replace fields, and pivot data.
  • Tableau desktop – Data connection and exploration are made using Tableau Desktop. Any data format, including Excel and web APIs, can be connected to this. After that, you can use the visual system to study the data. Data may be explored by analysts and business users, who can also create reports and dashboards that can be distributed throughout the organization.
  • Tableau Public – With the constraint that you can only share your reports and dashboards with Tableau Public, Tableau Public is free and offers all the functionality of Tableau Desktop (Google docs of Tableau). Tableau Public allows new users to see how other reports and dashboards are made and gain inspiration from them.
  • Tableau Server – Data analysts may access Tableau’s features online using Tableau Server, which eliminates the need to download and launch workbooks for use on Tableau Desktop. A Tableau server administrator can also control access to projects, workbooks, views, and data sources.
  • Tableau Online – The Tableau platform is available online through Tableau Online. Customers and users can both access and explore data visualizations. You never have to manage or install software, which is one benefit of using Online.
  • Through an iOS or Android app, Tableau Mobile makes reports and dashboards available to users on the move. Learn thoroughly about Tableau products by joining a data science certification course right away. 

Let’s take a closer look at how it is actually utilized by data analysts in their job now that we have a better grasp of what is it, how it was created, and some of its major products.

How do data analysts utilize Tableau?

Simply said, Tableau is well-liked by data analysts and their peers for its usability. After a dashboard has been developed, users may interact with the data to gain various insights, enabling them to set goals and make decisions for the business.

The Tableau user interface is simple and allows for the drag-and-drop representation of complex data sets. It is a dynamic platform with frequently added new capabilities. Thus there are always new applications for data analysis. 

Here, I have provided a summary of some of its use cases that are more widespread and have been around for a while.

  • Cleaning and preparing data

Analysts may work more productively even while compiling data from many sources and file kinds by using the built-in data connections and capabilities in Tableau Prep. Additionally, one data source can be created from many files that share the same column names, saving time on copying and pasting.

  • Combining and investigating data

Its drag-and-drop interface is simple and dynamic, encouraging experimentation and greater versatility. With the help of the Show Me function, which switches between a number of chart styles and provides a view in a few clicks, visualizations may be quickly built out. This eliminates the need to spend time formatting and aligning elements or reformatting data for each chart style.

  • What-if examination

Data analysts may easily adjust computations and test various scenarios thanks to Tableau’s robust input capabilities (no row or column constraints!). This is made possible by the drag-and-drop interface.

  • User involvement:

Users of dashboards can engage with and modify the dashboards produced by data analysts as desired. There is a lot of flexibility here, though the data analyst who creates the dashboard will undoubtedly put some constraints on the user to work within.

  • Functions and calculations:

Tableau’s powerful calculation language makes it simple to carry out complex computations and statistical functions. You can perform everything from simple aggregations to statistical calculations (including covariance and correlation) using the user-friendly interface.

  • Community participation:

Data analysts and other interested people can collaborate and learn from one another through Tableau Public’s vibrant community. New goods, product improvements, and patch updates are often added based on consumer input.

Key Conclusion

Hopefully, by this point, you have a solid understanding of i, its main features, its products, and how data analysts utilize it. It’s crucial to keep in mind that there are several data visualization platforms available and that Tableau is not the end-all and be-all of data visualization. Even if it is one of the most well-known platforms, the ideal platform for your data will be determined mainly by the demands of your company. Try out a variety of platforms before settling on one by taking advantage of free trials on some of their products, like it.

Through practical experience using applications like Tableau, Learnbay’s data science course with placement will prepare you for a job in the expanding field of data science and analytics. Without any prior knowledge or experience in data analytics, you will get a solid understanding of the fundamental concepts and refine your skills through projects, creating a portfolio of work to show prospective employers.