Statistical Tableau

Book description

To make sense of the vast amount of data in today's business landscape, you not only need to visualize data, but incorporate statistics into your visualizations as well. This practical book walks intermediate to advanced Tableau users through ways that statistics can help you incorporate decision science into the visualizations you create. Data analysts, business analysts, and business intelligence specialists will greatly benefit from this book.

Author Ethan Lang, data visualization designer and engineer, explains the decision science process and then demonstrates how you, your stakeholders, and your business can take action and make informed decisions with data much faster than before. You'll learn how this process will help you make more sense of your data and make data analysis more actionable and insightful.

This book helps you:

  • Get up to speed with the basic statistics & Tableau concepts you need to know
  • Understand how to incorporate statistical models into your visualizations and/or analysis
  • Understand how to explore your data to ensure you implement the correct models
  • Explore multiple ways to detect and visualize anomalies in your data
  • Use and understand tools native to Tableau to implement different regression models, forecasting, and clustering
  • Download the necessary software and connect to external connections
  • Explore examples that show you how to build and implement models using external connections

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Table of contents

  1. 1. Introduction to Tableau
    1. Download and start using Tableau Desktop
    2. Introduction to Statistics
      1. Hypothesis test
      2. Chi-square test
      3. Conclusions drawn from statistical analysis
    3. Data Visualization and Statistics
    4. Summary
  2. 2. Overview of the Analytics Pane
    1. Implementing Summarization Options from the Analytics Pane
    2. Implementing Model Options from the Analytics Pane
    3. Implementing Custom Options from the Analytics Pane
    4. Summary
  3. 3. What is a Confidence Interval
    1. How to Calculate Confidence Intervals
    2. Interpreting the Results
    3. How to Calculate Confidence Intervals in Tableau
      1. Check the confidence interval you solved by hand
      2. Implement confidence intervals on sample dataset
    4. Summary
  4. 4. Benchmarking in Tableau
    1. What Is a Benchmark
      1. Internal Benchmarking
      2. External Benchmarking
    2. Implementing Benchmarks in Tableau
      1. Static Reference Line
      2. Dynamic Reference Line
      3. Median With Quartiles
    3. Summary
  5. 5. Understanding Normal Distribution Using Histograms
    1. Types of distribution
      1. Uniform Distribution
      2. Bernoulli Distribution
      3. Exponential Distribution
      4. Normal Distribution
    2. Normal Distribution and Skewness
      1. What Is Skewness
      2. Accounting For Skewness
      3. Parametric and Non-Parametric Models
    3. How to Visualize Distributions in Tableau Using Histograms
    4. Summary
  6. 6. Anomaly Detection on Normalized Data
    1. Standard Deviations
      1. How to Use Standard Deviations in Tableau to Find Anomalies
    2. Median With Quartiles
      1. How to Use Median with Quartiles in Tableau to Find Anomalies
    3. Z-score Testing
      1. How to Use Z-Scores in Tableau to Find Anomalies
    4. Summary
  7. 7. Anomaly Detection on Non-Normalized Data
    1. Median Absolute Deviation (MAD)
      1. How to Implement Median Absolute Deviations in Tableau
    2. Modified Z-Score
      1. How to Implement Modified Z-Scores in Tableau
    3. Tukeys Fences
      1. How to Implement Tukey’s Fences in Tableau
    4. Why is Knowing the Distribution of Your Data Before Model Selection Important
    5. Summary
  8. 8. Linear Regression in Tableau
    1. Linear Regression Model
      1. Linear Regression Expressed Mathematically
      2. Simple Linear Regression Example
      3. Assumptions of Linear Regression Models
    2. Implementing Linear Regression in Tableau
      1. R-Squared
      2. P-Value
      3. Interpreting the Detailed Summary Statistics
      4. Optimizing Your Model
    3. Summary
  9. 9. Polynomial Regression in Tableau
    1. What is Polynomial Regression?
    2. Polynomial Regression Equation
      1. Polynomial Regression Expressed Mathematically
      2. Choosing The Right Model
    3. How to Implement Polynomial Regression in Tableau
      1. Changing the Parameters of the Polynomial Regression Model
    4. Summary
  10. 10. Forecasting in Tableau
    1. What is Exponential Smoothing
    2. Exponential Smoothing Equations
    3. How to Implement Forecast Models in Tableau
      1. Forecast Options
      2. Describe Forecast
    4. Summary
  11. 11. Clustering in Tableau
    1. What is K-Means Clustering
      1. K-Means Conceptual Example
      2. K-Means Example With Real Values
    2. Supervised vs Unsupervised Learning Methods
      1. Supervised Learning
      2. Unsupervised Learning
    3. How to Implement Clustering in Tableau
    4. Summary
  12. 12. Creating External Connection to Python Using Tableau
    1. What is Python?
      1. History of Python
      2. Key Features
      3. What is Anaconda
    2. Installing Python and Anaconda
      1. Installing Anaconda
    3. Establishing External Connection to Python in Tableau
      1. Connecting to TabPy from Tableau
    4. Summary
  13. 13. Creating External Connection to R Using Tableau
    1. What is R Code?
      1. History of R
      2. Key Features
      3. What is RStudio
    2. Installing R and RStudio
      1. Installing R
      2. Installing RStudio
    3. Establishing External Connection in Tableau
    4. Summary
  14. About the Author

Product information

  • Title: Statistical Tableau
  • Author(s): Ethan Lang
  • Release date: June 2024
  • Publisher(s): O'Reilly Media, Inc.
  • ISBN: 9781098151799