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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

Data Source Profile

Until now, you have only connected to different data sources. But your main objective is to understand the data better. This can be done by observing the data distribution, the data types of various columns, the values that a column contains, and so on.

A data source profile gives you an understanding of the underlying data by allowing you to observe the data distribution and frequency, along with the various data types for the fields. This helps you make appropriate changes to the data to fulfill the requirements in the flow. Some common options include checking the data distribution frequency, the number of unique records, and the associations among various columns. You will first learn about some commonly used profiling steps, and then apply them in an exercise.

Data source profiling can be performed using a clean step. A clean step can be added by hovering over the + icon next to the data source and selecting Clean Step, as follows:

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