Exploratory Data Analysis (EDA) is a critical process in data analysis that involves examining data sets to uncover underlying patterns, trends, and relationships before applying more formal statistical methods. The primary goal of EDA is to gain a deeper understanding of the data’s structure and the insights it may provide. This process typically includes a variety of techniques, such as summarizing the data with descriptive statistics, visualizing data distributions through plots like histograms and scatter plots, and exploring correlations between variables. By identifying patterns and trends early on, EDA helps analysts form hypotheses, guide further analysis, and make informed decisions. It serves as a foundation for more complex modeling and ensures that subsequent analyses are built on a solid understanding of the data.