Panel-ready Data Science track questions for freshers and experienced developers. Practice at Coding Cadre in Faridabad, or Online from Delhi NCR.
Duplicates inflate counts, double-weight users in means, and can be real repeated events or pipeline bugs.
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Cardinality is the number of distinct values.
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MCAR means missingness is unrelated to observed and unobserved values.
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Median imputation is more robust on skewed numeric columns because a few giants do not dominate the fill value.
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The fact of missingness can itself predict the outcome, for example users who skip billing fields.
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Listwise deletion drops a whole row if any analysis variable is missing.
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Values below Q1 minus one-and-a-half IQRs or above Q3 plus one-and-a-half IQRs are conventional box-plot outliers.
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A common rule flags |z| greater than 3 under an approximate normal model.
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Keep them when they are valid rare events you care about, such as whale spenders, outages, or fraud.
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Winsorization caps extreme values at chosen percentiles instead of dropping rows, so sample size stays the same.
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A box plot compactly compares medians, IQRs, and flagged extremes across groups, which is handy for many categories.
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Heatmaps work well for correlation matrices, confusion matrices, or intensity over two discrete axes such as hour-by-weekday.
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Lines preserve temporal order and make trends, seasonality, and shocks easy to see.
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A confounder is a variable that influences both the treatment-like factor and the outcome, opening a back-door association.
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Simpson's paradox is when a trend that appears in several groups reverses or vanishes when the groups are combined.
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Reverse causality means the outcome is driving the supposed cause, not the other way around.
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Stratified sampling draws separately within subgroups, such as country or plan tier, often in proportion to size.
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Cluster sampling selects groups first, such as cities or schools, then observes units inside those groups.
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The bootstrap resamples the observed dataset with replacement to approximate the sampling distribution of a statistic.
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Sampling error is random fluctuation from drawing a finite sample, and it shrinks with n under good design.
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