Introduction

Chapter 1, R for Advanced Analytics, introduced to you the R language and its ecosystem for data science. We are now ready to enter a crucial part of data science and machine learning, that is, Exploratory Data Analysis (EDA), the art of understanding the data.

In this chapter, we will approach EDA with the same banking dataset used in the previous chapter, but in a more problem-centric way. We will start by defining the problem statement with industry standard artifacts, design a solution for the problem, and learn how EDA fits in the larger problem framework. We will then tackle the EDA for the direct marketing campaigns (phone calls) of a Portuguese banking institution use case using a combination of data engineering, data wrangling, and data visualization techniques in R, backed up by a business-centric approach.

In any data science use case, understanding the data consumes the bulk of the time and effort. Most data science professionals spend around 80% of their time understanding...