Introduction

Exploratory analysis techniques are one part of the larger process of collecting data, learning from data, acting on data, and exploring data to uncover a meaningful pattern. The Exploratory Data Analysis (EDA) is a crucial step to take before diving into advanced analytics and machine learning, as it provides the context needed to develop an appropriate model for the problem at hand and to correctly interpret its results through visualization techniques to tease apart hidden patterns. In this chapter, we will discuss some of EDA's most common and essential practices, in order to summarize and visualize data so that the task of finding trends and patterns becomes causally easier.