What does exploratory data analysis aim to achieve?

Exploratory data analysis (EDA) is a crucial step in the data analysis process that focuses on understanding the underlying structure of data. It aims to identify patterns, relationships, and anomalies within a dataset, which can inform further analysis or model development. By employing various techniques such as visualizations, summary statistics, and correlations, EDA enables analysts to gain insights into the data's distribution, trends, and potential outliers.

The insights gained from EDA help clarify assumptions and guide the selection of appropriate modeling techniques and data transformations necessary for more advanced analyses. Through this process, analysts can surface important characteristics and relationships that may not be immediately apparent, making it foundational in effective data science practices.

In contrast, finalizing data models, training machine learning models directly, and streamlining data storage represent later stages in the data processing pipeline. These activities build upon the insights gained during exploratory data analysis but do not encompass its primary objective. Therefore, the focus of EDA on pattern and relationship identification distinguishes it as a critical precursor to more advanced data modeling and analysis activities.