Book Image

Tableau 2019.x Cookbook

By : Dmitry Anoshin, Teodora Matic, Slaven Bogdanovic, Tania Lincoln, Dmitrii Shirokov
Book Image

Tableau 2019.x Cookbook

By: Dmitry Anoshin, Teodora Matic, Slaven Bogdanovic, Tania Lincoln, Dmitrii Shirokov

Overview of this book

Tableau has been one of the most popular business intelligence solutions in recent times, thanks to its powerful and interactive data visualization capabilities. Tableau 2019.x Cookbook is full of useful recipes from industry experts, who will help you master Tableau skills and learn each aspect of Tableau's ecosystem. This book is enriched with features such as Tableau extracts, Tableau advanced calculations, geospatial analysis, and building dashboards. It will guide you with exciting data manipulation, storytelling, advanced filtering, expert visualization, and forecasting techniques using real-world examples. From basic functionalities of Tableau to complex deployment on Linux, you will cover it all. Moreover, you will learn advanced features of Tableau using R, Python, and various APIs. You will learn how to prepare data for analysis using the latest Tableau Prep. In the concluding chapters, you will learn how Tableau fits the modern world of analytics and works with modern data platforms such as Snowflake and Redshift. In addition, you will learn about the best practices of integrating Tableau with ETL using Matillion ETL. By the end of the book, you will be ready to tackle business intelligence challenges using Tableau's features.
Table of Contents (18 chapters)

Basic forecasting and statistical inference

The aim of this recipe is to introduce a basic forecasting method that relies on linear regression. We are going to use a built-in Tableau facility for linear regression. Simply put, regression analysis helps us discover predictors of a variable that we are interested in. We model the relationship between potential predictors and our variable of interest. Once we establish the model of the relationship between predictors and our variable, we can use it for further predictions.

To perform for casting, we will use the hormonal_response_to_excercise.csv dataset. This dataset comes from a health behavior study that aimed to explore the factors influencing cortisol response while exerting the maximal, peak effort during physical exercise (the Cortmax variable in our dataset).

Our first task is to explore how effectively we can predict the...