Exploratory Data Analysis (EDA) is the process of analyzing datasets to understand their structure, patterns, and anomalies before applying formal modeling or reporting. EDA helps analysts ask the right questions and avoid incorrect assumptions.

EDA typically involves:

• Summary statistics

• Distribution analysis

• Correlation checks

• Outlier detection

• Visual exploration

EDA is often done using notebooks, BI tools, or ad hoc queries.

EDA is critical for data quality validation and hypothesis generation. It ensures analytics is built on sound understanding rather than assumptions.