A data scientist has just loaded a 2-million-row cuDF DataFrame of website session logs and wants a fast first look at the data. As the opening step of exploratory data analysis, what is the primary purpose of generating per-column summary statistics such as count, mean, minimum, maximum, and the quartiles?
- ATo understand the central tendency, spread, and range of each numeric column and to surface anomalies such as impossible values or missing entries before modelling. Correct
- BTo select the final set of predictive features by ranking columns according to their correlation with the target variable.
- CTo train a baseline regression model whose coefficients quantify each feature's contribution to the outcome.
- DTo permanently impute every missing value using the column mean so the dataset is immediately ready for production.
Why A is correct: Correct. Summary statistics are an early EDA step whose purpose is to characterise distributions and reveal data-quality issues, for example a negative session duration or a count below the row total signalling nulls, before any modelling begins.
Why B is wrong: Tempting because feature selection is also part of a data pipeline, but summary statistics describe individual columns and do not measure relationships to a target, so they cannot rank predictive power on their own.
Why C is wrong: Tempting because a baseline model is a reasonable later step, but computing descriptive statistics fits no model and produces no coefficients, so it cannot establish a baseline predictor.
Why D is wrong: Tempting because the mean is part of the summary, but reporting statistics does not modify the data, and blanket mean imputation is a deliberate later choice rather than the purpose of describing the data.