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The Tableau Workshop

The Tableau Workshop

By : Sumit Gupta, Pinto, Shweta Savale, JC Gillet , Cherven
4.8 (32)
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The Tableau Workshop

The Tableau Workshop

4.8 (32)
By: Sumit Gupta, Pinto, Shweta Savale, JC Gillet , Cherven

Overview of this book

Learning Tableau has never been easier, thanks to this practical introduction to storytelling with data. The Tableau Workshop breaks down the analytical process into five steps: data preparation, data exploration, data analysis, interactivity, and distribution of dashboards. Each stage is addressed with a clear walkthrough of the key tools and techniques you'll need, as well as engaging real-world examples, meaningful data, and practical exercises to give you valuable hands-on experience. As you work through the book, you'll learn Tableau step by step, studying how to clean, shape, and combine data, as well as how to choose the most suitable charts for any given scenario. You'll load data from various sources and formats, perform data engineering to create new data that delivers deeper insights, and create interactive dashboards that engage end-users. All concepts are introduced with clear, simple explanations and demonstrated through realistic example scenarios. You'll simulate real-world data science projects with use cases such as traffic violations, urban populations, coffee store sales, and air travel delays. By the end of this Tableau book, you'll have the skills and knowledge to confidently present analytical results and make data-driven decisions.
Table of Contents (12 chapters)
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Preface
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2
2. Data Preparation: Using Tableau Desktop
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11. Tableau Interactivity: Part 1
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Summary

This chapter considered a number of advanced interactivity features in Tableau. We looked closely at the order of operations in Tableau, which is one of the most important concepts to master in Tableau if we want to create an efficient report for our stakeholders. We also discussed filters, sets, groups, and hierarchies, covered filters in depth, explored the difference between dimensions, measures, and date filters, and practiced using data source filters, which can be a great way to limit the data being loaded in your view.

Regarding sets, we reviewed static, dynamic, and combined sets, using the Envelope example to demo the concepts. In the section on parameters, we also defined the difference between static and dynamic parameters and learned one of the advanced use cases of parameters: dimension/measure swapping, which you can use to give end users the ability to choose the dimension/measures that they want to include in the view.

We wrapped up the chapter by working...

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