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How To Use Tableau for Data Science
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Data is the new oil for every industry. Every year, organizations collect massive volumes of data of all sizes. Such data help in making informed decisions and findings. Every organization is data-driven and relies on data to edge out competitors, and make profitable decisions. According to some predictions, by 2025, users will generate nearly 463 exabytes of data each day. But many organizations do not use the proper tools and applications to extract meaningful insight from the harbored structured and unstructured data. In this article, you will learn about Tableau features and how businesses utilize this visualization tool for data science. KnowledgeHut offers Tableau for Data Science course for learners looking to leverage the data science capabilities of Tableau.
What is Tableau for Data science?
Tableau is an incredible data visualization and business intelligence software that helps in generating graphics-rich reporting & analyzing enormous volumes of data. This American company started in 2003 and got acquired by Salesforce in June 2019. Enterprises use Tableau for mining actionable insights from granular data. It offers a plethora of features and customization within it. Almost all fortune 500 companies are leveraging Tableau for extracting better data-driven insights & understanding as per the market demands. More than 63,298 companies are using Tableau, and the count makes the market spread of 6.26 percent.
Through Tableau, data science professionals can generate graphs, charts, maps, data-driven stories, dashboards, etc., for visualizing and analyzing data. Rich visuals and data interpretations help business executives make efficient business decisions. There are four different products Tableau caters to for the organization. These are:
- Tableau Desktop for providing insights across the enterprise
- Tableau Prep for preparing data
- Tableau Server/Online for hosting and sharing data
- Tableau Reader for opening and interacting with packaged workbooks created using Tableau Desktop
How Professionals Can Use Tableau for Data Science
Working on data science with Tableau becomes beneficial because Tableau caters to a wide range of data visualization tools, features, and techniques that help in comprehending data easily. KnowledgeHut’s applied data science with Python can give you a clear understanding and hands-on experience on data science tools like Tableau.
- Data can be complicated to draw meaningful insights through data analysis alone. Visual presentation (through histogram, bullet chart, treemap, Gantt chart, boxplot, motion chart, etc.) of patterns in data helps managers and executives interpret it as actionable items. Tableau can help data science professionals make meaningful visuals of the extracted data so that laypersons can easily understand it.
- Another significant aspect of data science with Tableau is dealing with live and in-memory data. Tableau helps in connecting to data sources and extracting data from miscellaneous external sources as well. Such flexibility of fetching data from external sources helps data science professionals employ data from more than a single data source without any constraints.
- Tableau can also deal with large amounts of data and data provided by Big Data having millions of rows. It can work on all these data without losing efficiency and accuracy in performance.
- Tableau for data science is proficient in data analytics and helps data science team drill deep into data, uncovering insights and data patterns. That is another use of Tableau in data science, where the data analysts and data science professionals can use different algorithms on meaningfully extracted data.
- Tableau Dashboard has an amazing reporting component that permits users to customize dashboards explicitly for devices like mobile or laptops. Tableau automatically detects the user device used for viewing the report which makes automatic necessary adjustments to render the exact information.
Tableau as a Data Science Enabler
There are various data visualization tools and libraries available in the market. But most organizations prefer Tableau for data science work because of its varied features. You can enroll in Data Visualization with Tableau Training and dive deeper into visualization techniques and how Tableau makes a difference.
Some of them are:
- It provides drag & drop functionality.
- The Tableau dashboard caters to a wide range of layouts & formats, enabling the users to deploy suitable filters.
- Data science professionals prefer Tableau over other tools because it provides a convenient option for collaboration & sharing the data analysis project with other users instantly.
- Tableau also benefits data science professionals as it allows sharing data securely from myriad data sources such as cloud, on-premise, website servers, hybrid, etc.
- Tableau provides another feature that makes it an enabler for data science projects. The map in Tableau has a lot of pre-installed information about postal codes, cities, administrative boundaries, etc. That helps data science professionals get an extra layer of built-in geographical data.
- When data analysts work on data science with SQL and Tableau, they can enjoy other miscellaneous features like cross-database join, nested sorting, relational data handling, licensing views, implementing data connectors, etc.
All of these justify the fact that Tableau is an enabler for data science. Since Tableau is good at empowering meaningful data-driven acuity through rich visuals, data analysts & professionals find it a great tool to showcase their analysis through graphs, charts, and other graphical representations.
Improvements Required by Tableau for Data Scientists
Tableau plays a significant role in data science; the benefits of Tableau don’t stop at that. It also helps improvise various forms of data-driven insights generated by data scientists. With tools like Tableau, data science professionals and data scientists can extract & evaluate complex data. Tableau helps in generating a relationship among distinct variables for making predictions from messy datasets. Therefore, to become a data scientist from a data science professional, proficient knowledge of Tableau is a must. So, data science aspirants and professionals must leverage the benefit of the best online data science courses with Tableau provided by KnowledgeHut. Here are some of the pivotal points that benefit data scientists in their day-to-day work.
- Exploratory Data Analysis (EDA) is a necessary operation in Data Science. Data Scientists leverage this technique to accomplish the initial investigation on various datasets. That's where they use Tableau to discover hidden patterns, anomalies, test hypotheses, etc.
- Data science professionals who are Python programmers - usually use Seaborn and Matplotlib for data visualization. But data scientists prefer Tableau as it is better at rendering appealing visuals without writing a large set of codes. Also, it can provide easy-to-use 3D charts and graphs and hence, Tableau is better than Matplotlib and Seaborn libraries.
- Data scientists often need the metrics of the data science and ML models that can extract meaningful insight from given data. Tableau comes with models that data scientists can easily include in their performance metrics. Tableau also allows adding performance metrics through SQL databases that can help summarize the report for manual review. Other senior subject matter experts or principal data scientists can review these reports and suggest further improvements along the way.
- Sometimes data scientists have to work with fundamental technologies and applications like SQL and spreadsheets. Tableau does not restrict itself working with data sets only but also operates SQL queries or employs static Excel & CSV files. Also, data scientists can paste their SQL queries to interact with the databases through Tableau.
- Tableau is also good at performing clustering operations. Clustering is a method of discovering similarities between distinct data points in the same group or from a number of groups. Tableau facilitates data scientists to perform clustering operations and leverage popular clustering algorithms like K-means clustering on numerous datasets without composing a single line of code.
Advantages of Using Tableau for Data Science
- Quick learning and implementing: Tableau cater to a wide variety of options for implementing visualizations. It is easy to use and easy to learn compared to Python visualization libraries.
- Best for non-programmers: It is a good choice for non-programmers to use Tableau for data science because using Tableau is easy and does not require coding skills.
- Renders interactive visualization: Users leveraging Tableau can generate rich & high-quality visualization through the drag and drop functions.
- Handle large datasets: With Tableau, the data analysis team can easily perform visualization on millions of rows of data.
- Mobile friendly: IOS and Android users can use Tableau, allowing them to maintain and witness statistics and graphs at their fingertips.
- Low cost: The cost of Tableau is comparatively low compared to other data visualization and BI tools like Business Objects and Qlik.
Conclusion
There is a significant contribution of Tableau for data science. There are a lot of uses Tableau caters to in data science, and organizations are leveraging it to present the data in graphical format. That helps the top-level executives and business shareholders make informed decisions. If you are looking for no-code or low-code data-driven visualization solutions for your company, Tableau is the best and easy-learning tool. You can also check out the best online data science courses with R, Tableau, and Python provided by KnowledgeHut.
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Frequently Asked Questions (FAQs)
1. How Long Does It Take To Learn Tableau for Data Science?
Answer: It takes roughly around 2 to 6 months to learn Tableau. It is simple to understand but usually needs a longer time to master all the different functionalities. If you have experience with data visualization and data science concept plus, you give hours of dedicated learning, then 2 to 3 months makes it enough for an aspirant to master it.
2. Should I Learn Python Before Tableau?
No, it is not mandatory or a prerequisite to take up any programming language (like Python) for learning Tableau.
3. Is Tableau Necessary for Data Science?
Answer: It is not necessary to learn Tableau for data science. One can use visualization libraries or other tools like Qlik for doing the same work possible using Tableau. But since Tableau is a clear market leader in data visualization, it has become a must-have application for data analysts.
4. Do Data Analysts Use Tableau?
Yes, data analysts use Tableau. Once they refine the granular data required for making informed business decisions - these data are feed to Tableau for providing rich visuals. As we all know, better visuals help acquire a clear prospect towards decisions. Proper use of Tableau is essential to learn for data analysts. You can look for the Tableau Data science course from here.