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Exploratory Data Analysis (EDA) is an iterative, methodical approach to examining datasets using visualization, summary statistics and simple transformations. The goal is to uncover patterns, outliers and hypotheses for further analysis. EDA reduces uncertainty and informs model selection, feature engineering and business questions.
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Exploratory Data Analysis (EDA) is a structured first pass over a dataset in which visualization, simple statistics, and targeted transformations help reveal patterns, outliers, and open assumptions.
EDA grew out of statistical practice that examines data visibly and descriptively before committing to a fixed model. John Tukey promoted the approach from 1970 and described it in detail in his 1977 book; later work distinguished it from the narrower Initial Data Analysis. The goal was to use graphics, summaries, and simple transformations to surface hypotheses, outliers, and suitable preprocessing steps.
Think of EDA as a repeated inspection loop: look, summarize, then ask a sharper question. Start with raw data and check distribution, spread, missing values, and extremes. Follow up suspicious patterns with simple segmentations or transformations. Each pass either sharpens the next question or shows that the data needs cleaning before any formal model makes sense.
Charts make shape, spread, and irregularities easier to see than tables alone.
Mean, median, quartiles, and spread provide a compact first overview.
Unusual values may be errors, special cases, or important signals of structure.
Observations from the dataset become testable questions for later analysis.
EDA helps decide which cleaning, transformation, or filtering steps are needed.
EDA is especially useful at the start of an analysis, data science, or reporting effort when data structure and quality are still uncertain. It also helps before modeling, when checking assumptions, and when prioritizing questions. EDA is not a substitute for confirmatory analysis: patterns can mislead, and without context, good sampling, and clean data, conclusions can easily be wrong.
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