In exploratory data analysis, which of the following methods is commonly used?

In exploratory data analysis (EDA), data summarization is a fundamental method used to gain insights into the data set. It involves calculating key statistics and metrics such as mean, median, mode, range, and standard deviation. This process allows analysts to identify patterns, trends, and anomalies within the data. By summarizing the data, one can better understand its distribution, central tendency, and variability, which are critical for making informed decisions about further analyses or modeling.

While other methods like statistical modeling, data cleansing, and data aggregation might play important roles in the broader analytics process, they do not primarily focus on the initial exploration and summarization of data properties. Data summarization serves as the foundation for EDA, enabling deeper analysis and informing subsequent data processing steps.