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Mastering Tableau 2019.1: An expert guide to implementing advanced business intelligence and analytics with Tableau 2019.1 , Second Edition

Marleen Meier

Baldwin

€18.99 per month

3.5 (6 Ratings)

Paperback Feb 2019 558 pages 2nd Edition

Marleen Meier

Baldwin

€18.99 per month

3.5 (6 Ratings)

Paperback Feb 2019 558 pages 2nd Edition

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View table of contents

Preview Book

Key benefits

Master new features in Tableau 2019.1 to solve real-world analytics challenges
Perform Geo-Spatial Analytics, Time Series Analysis, and self-service analytics using real-life examples
Build and publish dashboards and explore storytelling using Python and MATLAB integration support

Description

Tableau is one of the leading business intelligence (BI) tools used to solve BI and analytics challenges. With this book, you will master Tableau's features and offerings in various paradigms of the BI domain. This book is also the second edition of the popular Mastering Tableau series, with new features, examples, and updated code. The book covers essential Tableau concepts and its advanced functionalities. Using Tableau Hyper and Tableau Prep, you’ll be able to handle and prepare data easily. You’ll gear up to perform complex joins, spatial joins, union, and data blending tasks using practical examples. Following this, you’ll learn how to perform data densification to make displaying granular data easier. Next, you’ll explore expert-level examples to help you with advanced calculations, mapping, and visual design using various Tableau extensions. With the help of examples, you’ll also learn about improving dashboard performance, connecting Tableau Server, and understanding data visualizations. In the final chapters, you’ll cover advanced use cases such as Self-Service Analytics, Time Series Analytics, and Geo-Spatial Analytics, and learn to connect Tableau to R, Python, and MATLAB. By the end of this book, you’ll have mastered the advanced offerings of Tableau and be able to tackle common and not-so-common challenges faced in the BI domain.

Who is this book for?

This book is designed for business analysts, BI professionals and data analysts who want to master Tableau to solve a range of data science and business intelligence problems. The book is ideal if you have a good understanding of Tableau and want to take your skills to the next level.

What you will learn

Get up to speed with various Tableau components
Master data preparation techniques using Tableau Prep
Discover how to use Tableau to create a PowerPoint-like presentation
Understand different Tableau visualization techniques and dashboard designs
Interact with the Tableau server to understand its architecture and functionalities
Study advanced visualizations and dashboard creation techniques
Brush up on powerful Self-Service Analytics, Time Series Analytics, and Geo-Spatial Analytics

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Product Details

Publication date : Feb 28, 2019

Length: 558 pages

Edition : 2nd

Language : English

ISBN-13 : 9781789533880

Vendor :

Tableau

Category :

Data

Languages :

MATLAB

Concepts :

Business Intelligence

Tools :

Tableau

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19 Chapters

Section 1: Tableau Concepts, Basics

Getting Up to Speed - A Review of the Basics

Creating worksheets and dashboards

Connecting Tableau to your data

Connecting to Tableau Server

Connecting to saved data sources

Measure Names and Measure Values

Three essential Tableau concepts

Exporting data to other devices

Summary

All About Data - Getting Your Data Ready

Understanding Tableau's data handling engine, hyper

The Tableau data handling engine

Hyper takeaways

Data mining and knowledge discovery process models

CRISP–DM

Focus on data preparation

Tableau Prep

All About Data - Joins, Blends, and Data Structures

Introduction to joins

Introduction to complex joins

Exercise: observing join culling

Introduction to join calculations

Introduction to spatial joins

Introduction to unions

Understanding data blending

Order of operations

No dimensions from a secondary source

Introduction to scaffolding

Introduction to data structures

Exercise: adjusting the data structure for different questions

Summary

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

Introduction to data densification

Domain completion

Deployment of domain completion

Usefulness of domain completion

Domain padding

Working with cubes

The deprecation of cubes

Tableau and big data

Addressing Excel's row limitation

Massively parallel processing

Building a visualization with Google BigQuery

Summary

Table Calculations

A definition and two questions

Introduction to functions

Directional and non-directional table calculations

Application of functions

Summary

Level of Detail Calculations

Building playgrounds

Playground I: FIXED and EXCLUDE

Playground II: INCLUDE

Practical application

Exercise: practical FIXED

Exercise: practical INCLUDE

Exercise: practical EXCLUDE

Summary

Section 2: Advanced Calculations, Mapping, Visualizations

Beyond the Basic Chart Types

Improving popular visualizations

Custom background images

Tableau extensions

Summary

Mapping

Extending Tableau's mapping capabilities without leaving Tableau

Extending Tableau mapping with other technology

Exercise: connecting to a WMS server

Exploring the TMS file

Exploring Mapbox

Accessing different maps with a dashboard

Creating custom polygons

Converting shape files for Tableau

Exercise: polygons for Texas

Heatmaps

Summary

Tableau for Presentations

Getting the best images out of Tableau

From Tableau to PowerPoint

Embedding Tableau in PowerPoint

Animating Tableau

Story points and dashboards for Presentations

Summary

Visualization Best Practices and Dashboard Design

Visualization design theory

Formatting rules

Color rules

Visualization type rules

Compromises

Keeping visualizations simple

Advanced Analytics

Self-service Analytics

Use case – Self-service Analytics

Use case – Geo-spatial Analytics

Summary

Improving Performance

Understanding the performance-recording dashboard

Exercise: exploring performance recording in Tableau desktop

Performance-recording dashboard events

Behind the scenes of the performance- recording dashboard

Hardware and on-the-fly techniques

Hardware considerations

On-the-fly-techniques

Single Data Source > Joining > Blending

Three ways Tableau connects to data

Using referential integrity when joining

Advantages of blending

Efficiently working with data sources

Tuning data sources

Working efficiently with large data sources

Intelligent extracts

Understanding the Tableau data extract

Constructing an extract for optimal performance

Exercise: summary aggregates for improved performance

Optimizing extracts

Exercise: materialized calculations

Using filters wisely

Extract filter performance

Data source filter performance

Context filters

Dimension and measure filters

Table-calculation filters

Efficient calculations

Boolean/Numbers > Date > String

Additional performance considerations

Avoid overcrowding a dashboard

Fixing dashboard sizing

Setting expectations

Summary

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

Interacting with Tableau Server

Tableau file types

Tableau data source

Tableau packaged data source

Tableau workbook

Tableau packaged workbook

Other file types

Tableau Server architecture

Tableau Server architecture approaches to avoid

Tableau Server architecture approaches to adopt

Tableau Server revision history

Tableau Server web-authoring environment

Basic web-authoring instructions

Capabilities and limitations of web-authoring

Comparing Tableau Desktop and web-authoring

User filters

Performance-recording dashboard

Exercise: exploring Performance Recording on Tableau Server

More Tableau Server settings

Summary

Programming Tool Integration

The architecture

R installation and integration

Installation

Using R functions

Introduction to correlations

Introduction to regression analysis

Introduction to clustering

Introduction to quantiles

Troubleshooting in Tableau and R

Examples of troubleshooting in Tableau and R

R scripts and Tableau table calculations

Python installation and integration

Using Python functions

Introduction to sentiment analysis

MATLAB installation and integration

Summary

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About the authors

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.

See other products by Marleen Meier

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.

See other products by Baldwin

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Mastering Tableau 2019.1: An expert guide to implementing advanced business intelligence and analytics with Tableau 2019.1 , Second Edition

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