What does exploratory data analysis primarily focus on?

Exploratory data analysis (EDA) primarily focuses on understanding data distributions and patterns. This phase of analysis allows data scientists to observe and summarize the characteristics of the data set, often using visual tools and summary statistics. Through EDA, analysts can identify trends, anomalies, and relationships within the data, which can guide further analysis or modeling.

Engaging in this practice enables a clearer understanding of the data's underlying structure and informs the selection of appropriate analytical techniques moving forward. By examining how variables interact and the shape of their distributions, one can derive insights that would otherwise be overlooked if only relying on predefined hypotheses or complex models.

Other approaches mentioned, such as testing predefined hypotheses, building predictive models, or optimizing storage solutions, are more structured and focused on specific goals rather than the broad, flexible exploration of data that defines EDA. In contrast, EDA emphasizes the importance of intuition and discovery, making it a fundamental step in the data analysis process and valuable context for subsequent analyses.