Exploratory Data Analysis (EDA)
Exploratory Data Analysis (EDA) is a critical initial phase of data analysis that focuses on understanding the structure, patterns, relationships, and anomalies within datasets before applying formal modeling techniques. EDA uses statistical summaries and visualizations to uncover trends, detect outliers, identify missing values, and assess data quality. By enabling analysts and researchers to form hypotheses and make informed analytical choices, EDA supports robust, transparent, and data-driven insights across disciplines such as science, engineering, healthcare, business, and social research.
Exploratory Data Analysis, EDA, Data Exploration, Descriptive Statistics, Data Visualization, Pattern Discovery, Outlier Detection, Data Profiling, Summary Statistics, Correlation Analysis, Distribution Analysis, Feature Understanding, Data Cleaning, Data Quality Assessment, Hypothesis Generation, Univariate Analysis, Bivariate Analysis, Multivariate Analysis, Statistical Graphics, Data Analytics.
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