The main objective of this introductory chapter is to revise the fundamentals of Exploratory Data Analysis (EDA), what it is, the key concepts of profiling and quality assessment, the main dimensions of EDA, and the main challenges and opportunities in EDA.
Data encompasses a collection of discrete objects, numbers, words, events, facts, measurements, observations, or even descriptions of things. Such data is collected and stored by every event or process occurring in several disciplines, including biology, economics, engineering, marketing, and others. Processing such data elicits useful information and processing such information generates useful knowledge. But an important question is: how can we generate meaningful and useful information from such data? An answer to this question is EDA. EDA is a process of examining the available dataset to discover patterns, spot anomalies, test hypotheses, and check assumptions using statistical measures. In this chapter, we are going to discuss the steps involved in performing top-notch exploratory data analysis and get our hands dirty using some open source databases.
As mentioned here and in several studies, the primary aim of EDA is to examine what data can tell us before actually going through formal modeling or hypothesis formulation. John Tuckey promoted EDA to statisticians to examine and discover the data and create newer hypotheses that could be used for the development of a newer approach in data collection and experimentations.
In this chapter, we are going to learn and revise the following topics:
- Understanding data science
- The significance of EDA
- Making sense of data
- Comparing EDA with classical and Bayesian analysis
- Software tools available for EDA
- Getting started with EDA