Before building models or drawing definitive conclusions, understanding your data is fundamental. This chapter lays the groundwork for Exploratory Data Analysis (EDA), a systematic process for inspecting, summarizing, and visualizing datasets to gain initial insights.

You will begin by defining EDA and understanding its primary objectives within the data analysis process. We will outline a structured workflow for conducting EDA and introduce the essential Python libraries commonly used, including Pandas, NumPy, Matplotlib, and Seaborn. Finally, we'll guide you through setting up the necessary software environment to follow along with the practical examples in subsequent chapters. By the end of this chapter, you will grasp the 'why' and 'what' of EDA and be prepared with the tools to start analyzing data.