Build, design, and improve advanced business intelligence solutions using Tableau’s newest updates, including new Tableau Desktop, Tableau Prep, and Tableau Server features
Purchase of the print or Kindle book includes a free PDF eBook
Key Features
- Master new Tableau 2023 features to solve real-world analytics challenges
- Learn how to use both pre-defined and your own Machine Learning models in Tableau
- How to manage Data Governance and secure high data quality
Book Description
This edition of the bestselling Tableau guide will teach you how to leverage Tableau's newest features and offerings in various paradigms of the BI domain. Updated with fresh topics, including the newest features in Tableau Server, Prep, and Desktop, as well as up-to-date examples, this book will take you from mastering essential Tableau concepts to advance functionalities. A chapter on data governance has also been added.
Throughout this book, you'll learn how to use Tableau Hyper files and Prep Builder to easily perform data preparation and handling, as well as complex joins, spatial joins, unions, and data blending tasks using practical examples. You'll also get to grips with executing data densification and explore other expert-level examples to help you with calculations, mapping, and visual design using Tableau extensions.
Later chapters will teach you all about improving dashboard performance, connecting to Tableau Server, and understanding data visualization with examples. Finally, you'll cover advanced use cases, such as self-service analysis, time series analysis, geo-spatial analysis, and how to connect Tableau to Python and R to implement programming functionalities within Tableau.
By the end of this book, you'll have mastered Tableau 2023 and be able to tackle common and advanced challenges in the BI domain.
What you will learn
- Learn about various Tableau components, such as calculated fields, table calculations, and LOD expressions
- Master ETL (Extract, Transform, Load) techniques using Tableau Prep Builder
- Explore and implement data storytelling with Python and R
- Understand Tableau Exchange by using accelerators, extensions, and connectors
- Interact with Tableau Server to understand its functionalities
- Study advanced visualizations and dashboard creation techniques
- Brush up on powerful self-service analytics, time series analytics, and geo-spatial analytics
- Find out why data governance matters and how to implement it
Who this book is for
This book is designed for business analysts, business intelligence professionals, and data analysts who want to master Tableau to solve a range of data science and business intelligence problems. Prior exposure to Tableau will help you get to grips with the features more quickly, but it’s not a prerequisite.
Table of Contents
- Reviewing the Basics
- Getting Your Data Ready
- Using Tableau Prep Builder
- Learning about Joins, Blends, and Data Structures
- Introducing Table Calculations
- Utilizing OData, Data Densification, Big Data, and Google BigQuery
- Practicing Level of Detail Calculations
- Going Beyond the Basics
- Working with Maps
- Presenting with Tableau
- Designing Dashboards and Best Practices for Visualizations
- Leveraging Advanced Analytics
- Improving Performance
- Exploring Tableau Server and Tableau Cloud
- Integrating Programming Languages
- Developing Data Governance Practices
Marleen Meier is an accomplished analyst and author with a passion for statistics and data. By using traditional methodologies and approaches such as Machine Learning and AI, Marleen is dedicated to driving meaningful insights. Currently working as the APAC Data CoE Lead for ABN AMRO Clearing, Marleen is at the forefront of innovation and implementing data-driven strategies in a global financial environment. She has lived and worked in multiple countries, including Germany, the Netherlands, the USA, and Singapore, allowing her to bring a diverse and global perspective to her work. Through her writing and speaking engagements, she aims to empower individuals and organizations to unlock the full potential of their data assets.
