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You're reading from Mastering Tableau 2019.1 An expert guide to implementing advanced business intelligence and analytics with Tableau 2019.1

Product type Paperback

Published in Feb 2019

Publisher

ISBN-13 9781789533880

Length 558 pages

Edition 2nd Edition

Languages

MATLAB

Tools

Tableau

Concepts

Business Intelligence

Authors (2):

Marleen Meier

David Baldwin

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Table of Contents (20) Chapters

Preface

1. Section 1: Tableau Concepts, Basics FREE CHAPTER

2. Getting Up to Speed - A Review of the Basics

3. All About Data - Getting Your Data Ready

4. Tableau Prep

5. All About Data - Joins, Blends, and Data Structures

6. All About Data - Data Densification, Cubes, and Big Data

7. Table Calculations

8. Level of Detail Calculations

9. Section 2: Advanced Calculations, Mapping, Visualizations

10. Beyond the Basic Chart Types

11. Mapping

12. Tableau for Presentations

13. Visualization Best Practices and Dashboard Design

14. Advanced Analytics

15. Improving Performance

16. Section 3: Connecting Tableau to R, Python, and Matlab

17. Interacting with Tableau Server

18. Programming Tool Integration

19. Other Books You May Enjoy

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Tableau packaged data source

Take a look at the following Tableau data file details:

File format type: Compressed.
What it contains: Metadata and a data extract.
Why it's useful: The .tdsx file is useful because it can be accessed for both metadata and data. Tableau authors can access a .tdsx file located on Tableau Server as a data source, thus eliminating the need for a workbook to connect directly to an external data source. A published .tdsx file can be placed on a schedule so that it is regularly updated from the underlying data source.
How it's generated: A .tdsx file can be generated by right-clicking on a data source in the Data pane and selecting Add to Saved Data Sources..., followed by selecting Tableau Packaged Data Source in the resulting dialog box. Like the .tds file, the .tdsx file can also be generated when publishing to Tableau server via Server ...

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Authors (2)

Marleen Meier

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.

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Baldwin

Ryan Baldwin is a theatre major turned computer science geek. Hailing from the prairies of Western Canada, Ryan has been developing software on a wide array of platforms and technologies since 2001. Once, he wrote a crazy system application that compiled XSD Schema Docs into XAML forms that performed two-way binding with underlying XML documents in .NET WPF. Why? Because it had to be done. Another time, he worked on a project that would mash multiple social networks together, allowing users to find out who they were indirectly "connected" to (something akin to 6 Degrees of Kevin Bacon). It was eventually shelved. In 2012, he relocated to Toronto, where he works with the University Health Network, developing systems and tools that facilitate patient information exchange. You can often find him wearing headphones and jittering in coffee shops.

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