Mastering Tableau 2023: Implement advanced business intelligence techniques, analytics, and machine learning models with Tableau

✍ Scribed by Marleen Meier

• Publisher: Packt Publishing
• Year: 2023
• Tongue: English
• Leaves: 685
• Edition: 4
• Category: Library

✦ Synopsis

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

1. Reviewing the Basics
2. Getting Your Data Ready
3. Using Tableau Prep Builder
4. Learning about Joins, Blends, and Data Structures
5. Introducing Table Calculations
6. Utilizing OData, Data Densification, Big Data, and Google BigQuery
7. Practicing Level of Detail Calculations
8. Going Beyond the Basics
9. Working with Maps
10. Presenting with Tableau
11. Designing Dashboards and Best Practices for Visualizations
12. Leveraging Advanced Analytics
13. Improving Performance
14. Exploring Tableau Server and Tableau Cloud
15. Integrating Programming Languages
16. Developing Data Governance Practices

✦ Table of Contents

Cover
Copyright
Contributors
Table of Contents
Preface
Chapter 1: Reviewing the Basics
Creating worksheets and dashboards
Creating worksheets
Creating a visualization
Beyond the default behavior
Show Me
Creating dashboards
Building a dashboard
Adding interactivity to a dashboard
Connecting Tableau to your data
Connecting to a file
Connecting to Tableau Server
Connecting to saved data sources
Measure Names and Measure Values
Measure Names and Measure Values shortcuts
Three essential Tableau concepts
Dimensions and measures
Row-level, aggregate-level, and table-level calculations
Continuous and discrete
Exporting data to other devices
Exporting data to a mobile phone
Tableau Mobile
Summary
Chapter 2: Getting Your Data Ready
Understanding Hyper
The Tableau data-handling engine
Hyper takeaways
Focusing on data preparation
Surveying data
Establishing null values
Extrapolating data
Cleaning messy data
Cleaning the data
Extracting data
Summary
Chapter 3: Using Tableau Prep Builder
Connecting to data
The Prep GUI
Getting to know Prep
Data quality
Cleaning data
Unions and joins
Adding unions
Wildcard unions
Adding joins
Aggregating
Pivoting
Scripting
Additional options with Prep
Insert Flow
Incremental refresh
Bulk rename
Tableau Prep Conductor
Exporting data
Summary
Chapter 4: Learning about Joins, Blends, and Data Structures
Relationships
Joins
Join queries
Join calculations
Spatial joins
Unions
Blends
Exploring the order of operations
Adding secondary dimensions
Introducing scaffolding
Understanding data structures
Summary
Chapter 5: Introducing Table Calculations
Partition and direction of addressing
Directional and non-directional addressing
Exploring each unique table calculation function
Lookup and Total
Previous Value
Running
Window
First and Last
Index
Rank
Size
Application of functions
Building a playground
Partitioning and addressing with one dimension
Partitioning and addressing with two dimensions
Partitioning and addressing with three dimensions
Guidelines: a reminder
Summary
Chapter 6: Utilizing OData, Data Densification, Big Data, and Google BigQuery
Using the OData connector
Introducing data densification
Domain completion
Deploying domain completion
The usefulness of domain completion
Removing unwanted domain completion
Domain padding
Deploying domain padding
The usefulness of domain padding
Problems of domain padding
Data densification in predictive modeling
Tableau and big data
Building a visualization with Google BigQuery
Summary
Chapter 7: Practicing Level of Detail Calculations
Introducing LODs
FIXED and EXCLUDE
Setting up the workbook
Understanding FIXED
Table-scoped expressions
Quick LODs
Understanding EXCLUDE
Understanding Tableau’s order of operations
INCLUDE
Setting up the workbook
Understanding INCLUDE
Building practical applications with LODs
Using the LOD FIXED calculation
Using the LOD INCLUDE calculation
Using the LOD EXCLUDE calculation
Summary
Chapter 8: Going Beyond the Basics
Improving popular visualizations
Bullet graphs
Using bullet graphs
Bullet graphs – beyond the basics
Pies and donuts
Pies and donuts on maps
Pies and donuts – beyond the basics
Pareto charts
Using Pareto charts
Pareto charts – beyond the basics
Custom background images
Creating custom polygons
Drawing a square around Null Island
Creating an interactive bookshelf using polygons
Analyzing a game of chess in Tableau
Creating an SVG file in Tableau
Creating a grid
Using a grid to generate a dataset
Visualizing a chess game
Creating polygons on a background image
Tableau Exchange
Tableau extensions
Using an extension
Accelerators
Connectors
Einstein Discovery
Summary
Chapter 9: Working with Maps
Extending Tableau’s mapping capabilities without leaving Tableau
Creating custom polygons
Polygons for Texas
Heatmaps
Dual axes and layering maps
Using dual axes
Adding map layers
Extending Tableau mapping with other technology
Using custom maps with a WMS
Exploring Mapbox
Swapping maps
Custom geocoding
Summary
Chapter 10: Presenting with Tableau
Getting the best images out of Tableau
Tableau’s native export capabilities
From Tableau to PowerPoint
Creating a template
Creating a dashboard for print
Semi-automating a PowerPoint presentation
Embedding Tableau into PowerPoint
Embedding Tableau into Google Slides
Animating Tableau
Using an animation to export many images
Using an animation in Tableau to create an animation in PowerPoint
Story points and dashboards for presentations
Presentation resources
Summary
Chapter 11: Designing Dashboards and Best Practices for Visualizations
Visualization design theory
Formatting rules
Keep the font choice simple
Use lines in order of visibility
Use bands in groups of three to five
Color rules
Keep colors simple and limited
Respect the psychological implications of colors
Be colorblind-friendly
Use pure colors sparingly
Choose color variations over symbol variation
Visualization type rules
Keep shapes simple
Use pie charts sparingly
Make the dashboard simple and robust
Present dense information well
Tell a story
Maximize documentation on a dashboard
Visualization types
Keep visualizations simple
Dashboard design
Dashboard layout
The Golden Ratio layout
The quad layout
The small multiple layout
Utilize sheet swapping
Create a collapsible menu
Dashboard best practices
Actions
Filter actions
Highlight actions
URL actions
Navigation actions
Parameter actions
Set actions
Download
Item hierarchy
Used In
Something extra
What works?
Why it works better
What works?
Why it works better
Why it works better
Summary
Chapter 12: Leveraging Advanced Analytics
Visualizing world indices correlations
Plotting a scattergraph
Adding axis distributions
Adding a correlation matrix
Finalizing the dashboard
Geo-spatial analytics with Chicago traffic violations
Preparing the data
Building a map of intersections
Adding a corresponding heatmap worksheet
Finalizing the dashboard
Extending geo-spatial analytics with distance measures
Adding measure points to the map
Adding the distance line
Summary
Chapter 13: Improving Performance
Understanding the performance-recording dashboard
Hardware and on-the-fly techniques
The Run Update feature
Small extracts
Connecting to data sources
Working efficiently with large data sources
Defining primary and foreign keys
Defining columns as NOT NULL
Indexing
Working with extracts
Constructing an extract
Aggregation
Optimizing extracts
Using filters wisely
Extract filters
Data source filters
Context filters
Dimension and measure filters
Table calculation filters
Using actions instead of filters
Efficient calculations
Prioritizing code values
Level-of-detail calculations or table calculations
Other ways to improve performance
Avoid overcrowding a dashboard
Fixing dashboard sizing
Use Tableau Prep Builder
Setting expectations
Workbook Optimizer
Summary
Chapter 14: Exploring Tableau Server and Tableau Cloud
Publishing a data source to Tableau Server
Tableau file types
Tableau data source
Tableau packaged data source
Tableau workbook
Tableau packaged workbook
Other file types
Web authoring
Editing an existing workbook with web authoring
Understanding the Tableau Server web authoring environment
Maintaining workbooks on Tableau Server
Revision history
User filters
More Tableau Server settings and features
Features on the worksheet level
Features on the view level
Summary
Chapter 15: Integrating Programming Languages
Integrating programming languages
R installation and integration
Implementing R functionality
Reproducing native Tableau functionality in R
Using R for regression calculations
Clustering in Tableau using R
Introducing quantiles
Performance challenges
Python installation and integration
Implementing Python functionality
Random and random normal
Generating random numbers
Random normal
Calculating sentiment analysis
Deploying models with TabPy
Predeployed TabPy functions
Honing your R and Python skills
Einstein Discovery
Summary
Chapter 16: Developing Data Governance Practices
What is data governance?
Data governance principles
Lawfulness, fairness, and transparency
Purpose limitation
Data minimization
Accuracy
Storage limitation
Integrity and confidentiality
Accountability
Data governance in Tableau
Data source connectivity
Data access controls
Data security
Metadata management
Data certification
Version control
Monitoring and auditing
Collaboration and documentation
Follow-along examples
Certifying data sources
Data quality warnings
Tableau Lineage
Data Details
Data Guide
PacktPage
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Index