Exploratory Data Analysis (EDA) is the process of examining and visualizing data sets to uncover patterns, relationships, anomalies, and initial insights before applying formal statistical modeling or machine learning. It involves summarizing key features of the data using descriptive statistics, visualizations (such as histograms, box plots, scatter plots), and correlation matrices to form hypotheses, validate assumptions, and guide further analysis.
EDA is a critical early step in any data science or analytics workflow. It helps data professionals identify data quality issues, detect outliers, spot missing values, and understand variable distributions. EDA is often performed iteratively using tools like Python (Pandas, Seaborn, Matplotlib), R, or BI platforms. By building intuition and context around the data, EDA improves model selection, feature engineering, and the overall effectiveness of downstream analytical tasks.