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)

Discovering the latent structure of the dataset

When dealing with complex topics, we usually end up with a dataset with a large number of variables. To find meaning in this kind of dataset is typically a tricky task. Luckily, there are some analytical techniques that can help us. One of those techniques is principal component analysis (PCA), which is a data reduction technique. Mathematical transformation in this analysis enables us to derive the most informative dimensions of our dataset. The mathematics underlying the analysis singular value decomposition (SVD) is somewhat complex, so we won't go into too much detail in this recipe. The basics of PCA can be described like this: you start with a dataset with many variables, then you simplify that dataset by turning your original variables into a smaller number of principal components in a way that guarantees that you&apos...