What is a primary objective of Exploratory Data Analysis (EDA)?

A primary objective of Exploratory Data Analysis (EDA) is to check for missing data and uncover the data structure. This process involves summarizing the main characteristics of the data, often using visual methods, to gain insights into its structure, patterns, and anomalies. By examining the data in this way, a data scientist can identify potential issues such as missing values, outliers, or unexpected correlations, which may impact the performance of machine learning models.

The emphasis on checking for missing data is crucial because handling incomplete data properly is essential for meaningful analysis and model building. Identifying the structure of the data helps in understanding variables' relationships and informs decisions on feature engineering or selection for machine learning applications. This foundational understanding obtained through EDA lays the groundwork for subsequent modeling and analysis steps, making it a critical phase in the data science workflow.