Tableau for Data Analyst – Tableau Pie Chart

Personal Career & Learning Guide for Data Analyst, Data Engineer and Data Scientist

Tableau is a data visualization tool that makes it easy for data analysts to analyze and present complex data in a visually appealing way. One of the most commonly used visualization types in Tableau is the “Pie Chart”.

A Pie Chart is a circular chart that displays data as “slices” of the circle, with each slice representing a different category of data. The size of each slice is proportional to the quantity of that category, making it easy to see the relative proportions of the different categories. Pie Charts are particularly useful for showing the distribution of data across categories and for comparing the proportions of different categories.

For example, a Pie Chart can be used to display the sales of different products in a company, with each slice representing a different product. The size of each slice would be proportional to the sales of that product, making it easy to see which products are selling well and which are not. Pie Charts can also be used to display the distribution of customers by age, gender, or location, among many other possibilities.

Creating a Pie Chart in Tableau is easy and straightforward. First, you need to select the data that you want to analyze, and then choose the right visualization type (Pie Chart) in the toolbar. Next, you will choose the category measure and the quantity measure, and Tableau will automatically generate the Pie Chart. Finally, you can format the chart to your liking and publish it to share it with others.

In conclusion, Tableau Pie Charts are a simple and effective way for data analysts to display the distribution of data across categories and compare the proportions of different categories. Whether you are a seasoned data analyst or just starting out, Tableau Pie Charts are a great tool to add to your data analysis toolkit. With their ease of use and visually appealing design, Tableau Pie Charts are sure to help you communicate your data insights in a clear and impactful way.

Tableau for Data Analyst – Tableau Pie Chart

Loader Loading...
EAD Logo Taking too long?

Reload Reload document
| Open Open in new tab

Download PDF [792.69 KB]

Personal Career & Learning Guide for Data Analyst, Data Engineer and Data Scientist

Applied Machine Learning & Data Science Projects and Coding Recipes for Beginners

A list of FREE programming examples together with eTutorials & eBooks @ SETScholars

95% Discount on “Projects & Recipes, tutorials, ebooks”

Projects and Coding Recipes, eTutorials and eBooks: The best All-in-One resources for Data Analyst, Data Scientist, Machine Learning Engineer and Software Developer

Topics included: Classification, Clustering, Regression, Forecasting, Algorithms, Data Structures, Data Analytics & Data Science, Deep Learning, Machine Learning, Programming Languages and Software Tools & Packages.
(Discount is valid for limited time only)

Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause. The information presented here could also be found in public knowledge domains.

Learn by Coding: v-Tutorials on Applied Machine Learning and Data Science for Beginners

Please do not waste your valuable time by watching videos, rather use end-to-end (Python and R) recipes from Professional Data Scientists to practice coding, and land the most demandable jobs in the fields of Predictive analytics & AI (Machine Learning and Data Science).

The objective is to guide the developers & analysts to “Learn how to Code” for Applied AI using end-to-end coding solutions, and unlock the world of opportunities!