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Learning Tableau

Learning Tableau

By : Joshua N. Milligan
3.8 (22)
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Learning Tableau

Learning Tableau

3.8 (22)
By: Joshua N. Milligan

Overview of this book

If you want to understand your data using data visualization and don't know where to start, then this is the book for you. Whether you are a beginner or have years of experience, this book will help you to quickly acquire the skills and techniques used to discover, analyze, and communicate data visually. Some familiarity with databases and data structures is helpful, but not required.
Table of Contents (13 chapters)
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Preface
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Free Chapter
1
1. Creating Your First Visualizations and Dashboard
1. Creating Your First Visualizations and Dashboard
Connecting to data in Access
Foundations for building visualizations
Visualizing data
Bringing everything together in a dashboard
Summary
2
2. Working with Data in Tableau
2. Working with Data in Tableau
The Tableau paradigm
Connecting to data
Working with extracts instead of live connections
Metadata and sharing data source connections
Joins and blends
Filtering data
Summary
3
3. Moving from Foundational to Advanced Visualizations
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3. Moving from Foundational to Advanced Visualizations
Comparing values across different dimensions
Visualizing dates and times
Relating parts of the data to the whole
Visualizing distributions
Visualizing multiple axes to compare different measures
Summary
4
4. Using Row-level and Aggregate Calculations
4. Using Row-level and Aggregate Calculations
Creating and editing calculations
Three levels of calculation
Parameters
Practical examples of calculations and parameters
Ad hoc calculations
Performance considerations
Summary
5
5. Table Calculations
5. Table Calculations
An overview of table calculations
Quick table calculations
Scope and direction
Addressing and partitioning
Advanced table calculations
Practical examples
Summary
6
6. Formatting a Visualization to Look Great and Work Well
6. Formatting a Visualization to Look Great and Work Well
Formatting considerations
How formatting works in Tableau
Adding value to visualizations
Summary
7
7. Telling a Data Story with Dashboards
7. Telling a Data Story with Dashboards
Dashboard objectives
Example – is least profitable always unprofitable?
How actions work
Example – a regional scorecard
Story points
Summary
8
8. Adding Value to Analysis – Trends, Distributions, and Forecasting
8. Adding Value to Analysis – Trends, Distributions, and Forecasting
Trends
Distributions
Forecasting
Summary
9
9. Making Data Work for You
9. Making Data Work for You
Structuring data for Tableau
Dealing with data structure issues
An overview of advanced fixes for data problems
Summary
10
10. Advanced Techniques, Tips, and Tricks
10. Advanced Techniques, Tips, and Tricks
Sheet swapping and dynamic dashboards
Leveraging sets to answer complex questions
Mapping techniques
Using background images
Summary
11
11. Sharing Your Data Story
11. Sharing Your Data Story
Presenting, printing, and exporting
Sharing a workbook with users of Tableau Desktop or Tableau Reader
Sharing data with users of Tableau Server, Tableau Online, and Tableau Public
Summary
12
Index
Index

Comparing values across different dimensions

Often, you will want to compare the differences of measured values across different dimensions. You might find yourself asking questions like these:

  • How much profit did I generate in each department?
  • How many views did each of my websites get?
  • How many cases did each doctor in the hospital treat last year?

In each case, you are looking to make a comparison (among departments, websites, or doctors) in terms of some quantitative measurement (profit, number of views, and the count of cases).

Bar charts

Here is a simple bar chart, created using the Superstore Sales data source, similar to the one we built in Chapter 1, Creating Your First Visualizations and Dashboard:

Bar charts

The sum of sales is easily compared for each category of item sold in the chain of stores. Category is used as a discrete dimension in the view, which defines row headers (because it is discrete) and slices the sum of sales for each category (because it is a dimension). Sales defines an axis...

End of Section 2
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