The rapid visual analysis that is possible using Tableau is incredibly useful for answering numerous questions and making key decisions. But it only barely scratches the surface of the possible analysis. For example, a simple scatterplot can reveal outliers, but often, you want to understand the distribution or identify clusters of similar observations. A simple time series helps you to see the rise and fall of a measure over time, but many times, you want to see the trend or make predictions of future values.

Tableau enables you to quickly enhance your data visualizations with statistical analysis. Built-in features such as trend models, clustering, distributions, and forecasting allow you to quickly add value to your visual analysis. Additionally, Tableau integrates with R and Python platforms, which opens up endless options for the manipulation and analysis of your data.

This chapter will cover the built-in statistical models and analysis, including the following topics:

• Trending
• Clustering
• Forecasting
• Distributions

We'll take a look at these concepts in the context of a few examples using some sample datasets. You can follow and reproduce these examples using the Chapter 8 workbook.