Define "exploratory data analysis" (EDA).

Exploratory data analysis (EDA) is indeed best defined as an approach to analyzing data sets to summarize their main characteristics, often using visual methods. This process involves a variety of techniques that help to uncover patterns, spot anomalies, and test hypotheses within the data without having predefined notions or theories in mind.

By utilizing visualizations such as graphs, charts, and plots, EDA allows analysts to gain insights into the data's structure and distribution, facilitating a deeper understanding of its properties. This method is particularly valuable for identifying correlations and trends, which can inform further analysis or decision-making processes.

The focus of EDA is not merely on collecting data, validating theories, or encrypting information, but rather on understanding and interpreting the data in a flexible and creative way. This foundational step is essential in the data analytics process, as it serves as the groundwork for more complex analyses and predictive modeling.