Book Image

The Tableau Workshop

By : Sumit Gupta, Sylvester Pinto, Shweta Sankhe-Savale, JC Gillet, Kenneth Michael Cherven
Book Image

The Tableau Workshop

By: Sumit Gupta, Sylvester Pinto, Shweta Sankhe-Savale, JC Gillet, Kenneth Michael 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)
Preface

Data Preparation Using Clean, Groups, and Split

Cleaning is a very important part of data preparation, because having the right data leads to proper and efficient data analysis.

For example, imagine the sales amount for an order in a dataset is blank, but an order is processed anyway. This cannot be right, and requires some action. The order in question should either not be included, or the sales amount should be replaced with an average.

Another example would be the same customer having multiple names, or more than one customer ID. You may need to combine the names into one to correctly analyze information. All such tasks can be done using data cleaning. Prep provides a variety of options to clean data. In this section, you will learn about them.

Refer to the Orders_South dataset workflow that was created earlier:

Figure 3.25: Orders_South workflow

Right-click on the Clean 1 step to open the additional properties, as shown in the following screenshot...