Data Science interview questions and answers

The novelty effect is a short-run change in behavior because the UI is new, not because the design is durably better.

A primary metric is the one decision that determines ship or no-ship, which protects you from fishing across twenty dashboards.

MDE is the smallest true effect the test is designed to detect with the planned power, alpha, and sample size.

Random assignment balances both observed and unobserved confounders in expectation, which observational matching cannot guarantee.

groupby.agg reduces each group to summary values and returns a smaller frame keyed by group.

merge joins on keys like SQL, aligning rows by column values or index.

apply with a Python function per row is convenient but often loops in the interpreter and is slow on large frames.

A pivot table reshapes long data into a grid of aggregations, with one field as rows, one as columns, and a value aggregated in cells.

Object columns of strings use much more memory than categorical or string dtypes, and float64 IDs can corrupt large integers.

Broadcasting expands arrays of compatible shapes so elementwise operations work without explicit copies of the data.

axis specifies which dimension you collapse: axis=0 reduces over rows for a 2D array, leaving columns.

Vectorized ufuncs run in compiled code over contiguous memory and can use SIMD.

A boolean mask of the same shape selects positions where the mask is True, returning a one-dimensional collection of those values for a 1D array.

LEFT JOIN keeps every row from the left table and fills unmatched right columns with NULL.

Window functions compute aggregates or ranks over a partition while keeping row-level detail, using an OVER clause with partition and order keys.

A common table expression is a named subquery introduced with WITH, which you can reference like a temporary table.

DISTINCT removes duplicate full rows but does not compute sums, and it can hide that you joined explosively and then deduped.

NULL means unknown, so NULL = NULL is not true and WHERE col = NULL filters nothing

A correlation matrix screens pairwise linear associations among numeric features and can flag redundant predictors.

Leakage shows up as features that would not be known at prediction time, perfect separation, or timestamps after the label.