What does exploratory data analysis (EDA) typically involve?

Exploratory Data Analysis (EDA) is a critical phase in data analysis that focuses on understanding the dataset's characteristics. This process typically involves examining datasets with statistical graphics and plots, which helps analysts visualize the underlying structures in the data. By utilizing various visualization techniques such as histograms, box plots, scatter plots, and density plots, EDA allows practitioners to identify patterns, spot anomalies, and uncover relationships among variables.

This visual exploration is essential for generating hypotheses and guiding further statistical testing and predictive modeling. It encourages a comprehensive understanding of the data before applying formal modeling techniques, thereby setting a strong foundation for subsequent analyses.

In contrast, other options focus on aspects that are not central to EDA. Building predictive models is a different analytical stage that follows EDA. Storing data in databases pertains to data management practices rather than analysis. Additionally, analyzing only numerical data limits the scope of EDA, which encompasses both numerical and categorical data to provide a holistic view of the dataset.