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Exploratory Data Analysis (EDA) is a critical step in the data science workflow. It involves analyzing and visualizing data to gain insights and understand the underlying patterns and relationships.

Techniques for EDA include __ and __. Summary statistics provide a high-level overview of the data, including measures of central tendency, dispersion, and distribution. Data visualization is a powerful tool for understanding data. It allows us to visually explore patterns, trends, and relationships in the data. Common types of visualizations include scatter plots, histograms, bar charts, and line plots.

By performing EDA, we can identify any anomalies, outliers, or patterns in the data, which can help us make informed decisions in subsequent steps of the data science process.