Why is exploratory data analysis (EDA) important in data analytics?

Exploratory Data Analysis (EDA) plays a crucial role in data analytics because it helps analysts and data scientists to understand the underlying patterns, trends, and relationships within the data before applying any modeling techniques. By visualizing and summarizing the data, EDA enables the identification of important characteristics such as distributions, outliers, correlations, and potential biases. This foundational understanding is essential for making informed decisions about subsequent steps in the analysis process, including feature selection, model choice, and tuning parameters.

This understanding is also vital for highlighting any data quality issues that need to be addressed, ensuring that the final models are built on sound, clean, and relevant data. Thus, the assistance in comprehending data patterns and tendencies makes EDA a critical step in the data analytics process.