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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

Aggregation, Pivot, Join, and Union

You will often encounter certain scenarios where the data might need to be adjusted to suit the visualization requirements. For example, if you are analyzing the monthly sales for your company, you don't need the data for every single day. In this case, you need to aggregate data to the monthly level. This also reduces the amount of data being used for analysis.

Another example, is when the data for all the past years is stored as standalone files, and the current year is stored as a separate file. All the files have a similar column structure. If you were to analyze all the data together, you may need to perform a union transformation to combine all these separate files into a single file.

Such data transformations can be done in Prep. You will now learn about how to do them.

Aggregations

Aggregations help to change the granularity of data. Granularity, in this context, means the level at which the data is available. For example...

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