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

Exploring Comparisons across Dimensional Items

Before diving into chart making, it is important to differentiate between dimensions and measures.

Every column that is present in some data has a data type associated with it, such as string, integer, or date. Also, every column that exists in some data is either a dimension or a measure. Dimensions are qualitative or categorical data, such as names, regions, dates, or geographical data, and the columns have categories of distinct values. Measures, on the other hand, are quantitative values that can be aggregated.

Consider the following: a Country column has country names such as Canada, India, and Spain. Can you sum the regions? Would a sum of Canada, India, and Spain make any sense? No, it wouldn't, so a region is a dimension. Similarly, data to which you can apply mathematical functions, such as sum, average, min/max, and so on, are measures. Hence, the rule of thumb is as follows: columns to which you can apply a...