#### Overview of this book

Visualize This is a guide on how to visualize and tell stories with data, providing practical design tips complemented with step-by-step tutorials. It begins with a description of the huge growth of data and visualization in industry, news, and gov't and opportunities for those who tell stories with data. Logically it moves on to actual stories in data-statistical ones with trends and human stories. the technical part comes up quickly with how to gather, parse and format data with Python, R, Excel, Google docs, and so on, and details tools to visualize data-native graphics for the Web like ActionScript, Flash libraries, PHP, JavaScript, CSS, HTML. Every chapter provides an example as well. Patterns over time and kinds of data charts are followed by proportions, chart types and examples. Next, examples and descriptions of outliers and how to show them, different kinds of maps, how to guide your readers and explain the data "in the visualization". The book ends with a value-add appendix on graphical perception.
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Contents
Chapter 1: Telling Stories with Data
Chapter 2: Handling Data
Chapter 3: Choosing Tools to Visualize Data
Chapter 4: Visualizing Patterns over Time
Chapter 5: Visualizing Proportions
Chapter 6: Visualizing Relationships
Chapter 7: Spotting Differences
Chapter 8: Visualizing Spatial Relationships
Chapter 9: Designing with a Purpose
Introduction

Distribution

You’ve probably heard of mean, median, and mode. Schools teach you this stuff in high school; although they should teach it much sooner. The mean is the sum of all data points divided by the number of points. To find the median, you order your data from least to greatest and mark the halfway point. The mode is the number that occurs the most. These are well and good and super easy to find, but they don’t give you the whole story. They describe how parts of your data are distributed. If you visualize everything though, you can see the full distribution.

A skew to the left means most of your data is clustered in the lower side of the full range. A skew to the right means the opposite. A flat line means a uniform distribution, whereas the classic bell curve shows a clustering at the mean and a gradual decrease in both directions.

Next take a look at a classic plot, mainly to get a feel for distribution, and then move on to the more practical histogram and density...