## Chapter 10. Exploratory Data Analysis and Visualization

Exploratory Data Analysis (EDA) is the process of examining a dataset without preconceived assumptions about the data and its behavior. Real-world datasets are messy and complex, and require progressive filtering and stratification in order to identify phenomena that are worth using for alarms, anomaly detection, and forensics. Attackers and the Internet itself are a moving target, and analysts face a constant influx of weirdness. For this reason, EDA is a constant process.

The point of EDA is to get a better grip on a dataset before pulling out the math. To understand why this is necessary, I want to walk through a simple statistical exercise. In Table 10-1, there are four datasets, each consisting of a vector X and a vector Y. For each dataset, calculate these values:

• The mean of X and Y
• The variance of X and Y
• The correlation between X and Y

You will find that the mean, variance, and correlation are identical for each dataset, but simply by looking at the numbers, you should suspect something fishy. A visualization will show just how diverse they are. Figure 10-1 plots these sets and shows how each dataset results in a radically different distribution. The Anscombe Quartet was designed to show the impact of outliers (such as in dataset IV) and visualization on data analysis.

As this example shows, simple visualization will identify significant features of the dataset that aren’t identified by reaching for the stats. The ...

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