Exploratory Data Analysis (EDA) is a crucial step in the data analysis process, where analysts examine and visualize data to understand its underlying patterns, trends, and relationships. By exploring and interrogating the data early on, analysts can gain valuable insights that inform subsequent analysis and decision-making.
One of the primary goals of exploratory data analysis is to gain a deeper understanding of the data's distribution and characteristics. This involves techniques such as summary statistics, histograms, box plots, and density plots to visualize the distribution of variables and identify outliers or anomalies.
Moreover, EDA enables analysts to uncover relationships and correlations between variables, helping to identify potential predictors or factors that influence the outcome of interest. Techniques such as scatter plots, correlation matrices, and heatmaps are commonly used to visualize relationships and dependencies within the data.