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Mastering Tableau
Mastering Tableau

Mastering Tableau: Smart Business Intelligence techniques to get maximum insights from your data

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Profile Icon Jen Stirrup Profile Icon Baldwin
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€18.99 per month
Full star icon Full star icon Full star icon Full star icon Half star icon 4.4 (10 Ratings)
Paperback Dec 2016 476 pages 1st Edition
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€22.99 €32.99
Paperback
€41.99
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Renews at €18.99p/m
Arrow left icon
Profile Icon Jen Stirrup Profile Icon Baldwin
Arrow right icon
€18.99 per month
Full star icon Full star icon Full star icon Full star icon Half star icon 4.4 (10 Ratings)
Paperback Dec 2016 476 pages 1st Edition
eBook
€22.99 €32.99
Paperback
€41.99
Subscription
Free Trial
Renews at €18.99p/m
eBook
€22.99 €32.99
Paperback
€41.99
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Renews at €18.99p/m

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Mastering Tableau

Chapter 2.  All about Data – Getting Your Data Ready

Perhaps the most pertinent and frequent question I'm asked when teaching Tableau is, How do I get my data ready for Tableau? I find this difficult to answer for two reasons. Firstly, it's hard to know exactly what the student means by the question, and secondly, it's outside the scope of the class. Let's consider both these reasons.

There is a range of possible meanings for How do I get my data ready for Tableau? At the simplest end of the spectrum, some who ask this question just want to know what an Excel spreadsheet prepared for use with Tableau should look like. It suffices to show such students the Sample Superstore dataset that installs with Tableau and explain that the addition of human-readable content, such as comments and text boxes, may make the file difficult or impossible to connect to and that the inclusion of totals in the body of the spreadsheet would skew results. Other students wish...

Understanding Tableau's data-handling engine

Tableau's data-handling engine is usually not well comprehended by even advanced authors because it's not an overt part of day-to-day activities; however, for the author who wants to truly grasp how to ready data for Tableau, this understanding is indispensable. In this section, we will explore Tableau's data-handling engine and how it enables structured yet organic data mining processes in the enterprise.

To begin, let's clarify a term. The phrase Data-Handling Engine (DHE) in this context references how Tableau interfaces with and processes data. This interfacing and processing is comprised of three major parts: Connection, Metadata, and VizQL. Each part is described in detail in the following section. In other publications, Tableau's DHE may be referred to as a metadata model or the Tableau infrastructure. I've elected not to use either term because each is frequently defined differently in different contexts...

Data-mining and knowledge-discovery process models

Data modeling, data preparation, database design, data architecture; how do these and other, similar terms fit together? This is no easy question to answer! Terms may be used interchangeably in some contexts and be quite distinct in others. Also, understanding the interconnectivity of any technical jargon can be challenging. In the data world, data mining and knowledge-discovery process models attempt to consistently define terms and contextually position and define the various data sub-disciplines. Since the early 1990s, various models have been proposed. The following list is adapted from A Survey of Knowledge Discovery and Data Mining Process Models by Lukasz A. Kurgan and Petr Musilek, published in The Knowledge Engineering Review Volume 21 Issue 1, March 2006.

Survey of the process models

Fayyad et al.

KDD

CRISP-DM

Cios et al

SEMMA

Developing and Understanding of the Application Domain

Selection

Business Understanding...

CRISP-DM

The Cross Industry Standard Process for Data Mining (CRISP-DM) model was created between 1996 and 2000 as a result of a consortium including SPSS, Teradata, Daimler AG, NCR Corporation, and OHRA. It divides the process of data mining into six major phases, as shown in the following CRISP-DM reference model. This model provides a bird's-eye view of a data-mining project lifecycle. Although the sequence of the phases shown in the diagram is typical, it is not rigid; that is, jumping back and forth from phase to phase is allowed and expected. Note that the outer circle communicates the ongoing data-mining lifecycle. Data mining does not cease upon the completion of a particular project. Instead, it exists as long as the business exists and should be constantly revisited to answer new questions as they arise:

CRISP-DM

We will consider each of the six phases that comprise CRISP-DM and explore how Tableau can be used effectively throughout the lifecycle. We will particularly focus on the...

Focusing on data preparation

As discussed previously, Tableau can be effectively used throughout the CRISP-DM phases. Unfortunately, a single chapter is not sufficient to thoroughly explore how Tableau can be used in each phase. Indeed, such a thorough exploration may be worthy of an entire book! Our focus, therefore, will be directed to data preparation, since that phase has historically accounted for up to 60% of the data-mining effort. Our goal will be to learn how Tableau can be used to streamline that effort.

Surveying data

Tableau can be a very effective tool for simply surveying data. Sometimes in the survey process, you may discover ways to clean the data or populate incomplete data based on existing fields. Sometimes, regretfully, there are simply not enough pieces of the puzzle to put together an entire dataset. In such cases, Tableau can be useful to communicate exactly what the gaps are and this, in turn, may create initiative for the organization to more fully populate the underlying...

Understanding Tableau's data-handling engine


Tableau's data-handling engine is usually not well comprehended by even advanced authors because it's not an overt part of day-to-day activities; however, for the author who wants to truly grasp how to ready data for Tableau, this understanding is indispensable. In this section, we will explore Tableau's data-handling engine and how it enables structured yet organic data mining processes in the enterprise.

To begin, let's clarify a term. The phrase Data-Handling Engine (DHE) in this context references how Tableau interfaces with and processes data. This interfacing and processing is comprised of three major parts: Connection, Metadata, and VizQL. Each part is described in detail in the following section. In other publications, Tableau's DHE may be referred to as a metadata model or the Tableau infrastructure. I've elected not to use either term because each is frequently defined differently in different contexts, which can be quite confusing.

Tableau...

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Key benefits

  • • Arm yourself with an arsenal of advanced chart types and geocoding to efficiently and engagingly present information
  • • Map a grid over a network node diagram and use that grid to demonstrate loads, processing time, and more in Tableau
  • • Integrate R with Tableau by utilizing R functions, libraries, and saved models

Description

Tableau has emerged as one of the most popular Business Intelligence solutions in recent times, thanks to its powerful and interactive data visualization capabilities. This book will empower you to become a master in Tableau by exploiting the many new features introduced in Tableau 10.0. You will embark on this exciting journey by getting to know the valuable methods of utilizing advanced calculations to solve complex problems. These techniques include creative use of different types of calculations such as row-level, aggregate-level, and more. You will discover how almost any data visualization challenge can be met in Tableau by getting a proper understanding of the tool’s inner workings and creatively exploring possibilities. You’ll be armed with an arsenal of advanced chart types and techniques to enable you to efficiently and engagingly present information to a variety of audiences through the use of clear, efficient, and engaging dashboards. Explanations and examples of efficient and inefficient visualization techniques, well-designed and poorly designed dashboards, and compromise options when Tableau consumers will not embrace data visualization will build on your understanding of Tableau and how to use it efficiently. By the end of the book, you will be equipped with all the information you need to create effective dashboards and data visualization solutions using Tableau.

Who is this book for?

If you are a business analyst without developer-level programming skills, then this book is for you. You are expected to have at least a fundamental understanding of Tableau and basic knowledge of joins, however SQL knowledge is not assumed. You should have basic computer skills, including at least moderate Excel proficiency.

What you will learn

  • • Create a worksheet that can display the current balance for any given period in time
  • • Recreate a star schema from in a data warehouse in Tableau
  • • Combine level of detail calculations with table calculations, sets, and parameters
  • • Create custom polygons to build filled maps for area codes in the USA
  • • Visualize data using a set of analytical and advanced charting techniques
  • • Know when to use Tableau instead of PowerPoint
  • • Build a dashboard and export it to PowerPoint

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Dec 06, 2016
Length: 476 pages
Edition : 1st
Language : English
ISBN-13 : 9781784397692
Vendor :
Tableau
Category :
Languages :
Tools :

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Product Details

Publication date : Dec 06, 2016
Length: 476 pages
Edition : 1st
Language : English
ISBN-13 : 9781784397692
Vendor :
Tableau
Category :
Languages :
Tools :

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Table of Contents

13 Chapters
1. Getting Up to Speed – a Review of the Basics Chevron down icon Chevron up icon
2. All about Data – Getting Your Data Ready Chevron down icon Chevron up icon
3. All about Data – Joins, Blends, and Data Structures Chevron down icon Chevron up icon
4. All about Data – Data Densification, Cubes, and Big Data Chevron down icon Chevron up icon
5. Table Calculations Chevron down icon Chevron up icon
6. Level of Detail Calculations Chevron down icon Chevron up icon
7. Beyond the Basic Chart Types Chevron down icon Chevron up icon
8. Mapping Chevron down icon Chevron up icon
9. Tableau for Presentations Chevron down icon Chevron up icon
10. Visualization Best Practices and Dashboard Design Chevron down icon Chevron up icon
11. Improving Performance Chevron down icon Chevron up icon
12. Interacting with Tableau Server Chevron down icon Chevron up icon
13. R Integration Chevron down icon Chevron up icon

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Full star icon Full star icon Full star icon Full star icon Half star icon 4.4
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4 star 10%
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j v Apr 27, 2018
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Good
Amazon Verified review Amazon
Raghu Apr 15, 2019
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It’s easy to practice and have various examples to understand the basics in tableau. This book has helped professionally. I will advice this book
Amazon Verified review Amazon
Don88 Jan 20, 2017
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This book is very well-planned, well-researched, well-designed, well-written, and well-edited with extremely thorough presentation. I've purchased 5 different books on Tableau. However, I've learned more from this book than I ever did in all the other books combined. The problem I've had with most books dealing with Tableau is, there are no hands-on exercises and the examples provided in those books are essentially trivial for practical purposes. Not so with “Mastering Tableau”, there are online support files and example codes you can download for the hands-on exercises which are well written and thoroughly explained. This is indisputably the best Tableau book that I have ever come across.This is a must have book for Tableau users - whether you are new to Tableau or more advanced. The novice will feel like less than a novice by the first five chapters. Those who are more advanced can use this book as a great reference (it helps refresh forgotten knowledge). The material is arranged with the most elegant and simple descriptions of complex concepts unfolding into sophisticated working prototypes. Chapter by chapter, the introduction to the reader of “Mastering Tableau” and other intricacies within the object model builds understanding of fundamentals and delivers where most books fail - without extraneous and misleading detail. I highly recommend this book.
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Eric Sep 12, 2018
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Easy to read, I use it as my reference book in my office.
Amazon Verified review Amazon
gennaro coppa Jul 03, 2017
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lots of good info
Amazon Verified review Amazon
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