Use exploratory data analysis to inspect distribution, missingness, outliers, and assumptions before modeling.
EDA is the pause between data loading and believing. Inspect structure Check rows, columns, dtypes, keys, duplicate labels, and the grain of each row. Many later errors are structural, not statistical. Inspect missingness Count missing values by column and by segment. Missingness is not only a cleaning nuisance; it can reveal process failure or bias. Inspect shape and outliers Use distributions, time plots, and scatterplots. Averages and correlations are fragile when skew, seasonality, or outliers dominate. Inspect assumptions Ask what the next method assumes. If you plan to compare groups, check sample sizes. If you plan to model, check leakage,…
Sign up free — one personalized lesson every day, matched to your role and goals.
Already have an account? Sign in