Tableau for Data Analyst – Tableau Desktop Workspace

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

Tableau Desktop is a powerful data visualization tool that provides data analysts with the ability to connect to, analyze, and visualize data. The Tableau Desktop workspace is the main interface where you can interact with your data and create visualizations. In this article, we will explore the different parts of the Tableau Desktop workspace and how to effectively use them.

The Tableau Desktop workspace is divided into several different areas, including the Data Window, the Worksheet, and the Marks Card. The Data Window is where you can view and manipulate the data you are working with, while the Worksheet is where you can create and customize your visualizations. The Marks Card provides access to the individual marks, or data points, within your visualization.

To get started with Tableau Desktop, you need to first connect to your data source. This can be done by clicking the “Connect to Data” button in the start screen and selecting the type of data source you want to connect to. Once you have connected to your data, you can begin working with the data in the Data Window.

In the Data Window, you can add and remove data fields, sort and filter data, and aggregate data using calculations and built-in aggregation functions. This allows you to work with your data in meaningful ways and gain valuable insights into your data.

The Worksheet is where you can create and customize your visualizations. You can add data fields to your Worksheet and use the drag-and-drop interface to create charts and graphs that help you understand your data. You can also create calculated fields, which allow you to apply custom calculations to your data, and customize the visual appearance of your data using things like color, font, and size.

The Marks Card provides access to the individual marks, or data points, within your visualization. You can use the Marks Card to interact with your data, change the size and shape of your marks, and apply custom colors and labels to your data.

In addition to the Data Window, Worksheet, and Marks Card, Tableau Desktop provides several other tools and features that can help you work with your data. These include things like the Show Me button, which provides access to different visualization types, including bar charts, line charts, and pie charts, and the Analysis menu, which provides access to a variety of advanced data analysis tools.

In conclusion, the Tableau Desktop workspace provides data analysts with the tools and support they need to effectively work with their data and gain valuable insights. Whether working with a large database, cloud data source, or Excel spreadsheet, Tableau Desktop provides the tools and support you need to get the most out of your data. Understanding how to effectively use the Tableau Desktop workspace is a key step in becoming a proficient Tableau user.

Tableau for Data Analyst – Tableau Desktop Workspace

Loader Loading...
EAD Logo Taking too long?

Reload Reload document
| Open Open in new tab

Download PDF [820.36 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!