In this chapter, we revisited the most fundamental theory behind data analysis and exploratory data analysis. EDA is one of the most prominent steps in data analysis and involves steps such as data requirements, data collection, data processing, data cleaning, exploratory data analysis, modeling and algorithms, data production, and communication. It is crucial to identify the type of data under analysis. Different disciplines store different kinds of data for different purposes. For example, medical researchers store patients' data, universities store students' and teachers' data, real estate industries store house and building datasets, and many more. A dataset contains many observations about a particular object. Most of the datasets can be divided into numerical data and categorical datasets. There are four types of data measurement scales: nominal, ordinal, interval, and ratio.
We are going to use several Python libraries, including NumPy, pandas, SciPy, and Matplotlib, in this book for performing simple to complex exploratory data analysis. In the next chapter, we are going to learn about various types of visualization aids for exploratory data analysis.