Exploratory Data Analysis and Data Quality Questions
Understanding and preparing an unfamiliar dataset before analysis or modeling. Covers systematic profiling through summary statistics, distribution and outlier inspection, and relationships between variables to form initial hypotheses, alongside turning raw data into a trustworthy base: handling missing values, deduplication, outlier treatment, type and consistency checks, and validation. Includes critical thinking about sampling and measurement bias and what a dataset can and cannot support.
A dashboard scatter plot shows a negative correlation between average nightly price and occupancy rate across listings. What hypotheses would you generate to explain that pattern, what additional data would you request, and what analyses would you run to test those hypotheses?
Before you start analyzing a new dataset, what explicit assumptions do you write down about it? For each, what's a concrete check you'd run to validate rather than assume it?
Given a small sample table with a few missing cells across two columns, decide whether the missingness in each column looks most consistent with MCAR, MAR, or MNAR based on what else you can see in the rows, and name two diagnostics you'd run to confirm your read on a larger dataset.
Explain what a pivot table (or a GROUP BY aggregation) is doing conceptually, and walk through an example where pivoting sales by region and month during EDA surfaces an actionable pattern you wouldn't have spotted from the raw rows.
How do you structure an EDA project so it's reproducible and shareable with the rest of your team: notebook vs script organization, where raw vs processed data and charts live, versioning of datasets and code, and what you'd automate so someone else can regenerate your findings?
Unlock Full Question Bank
Get access to all Exploratory Data Analysis and Data Quality interview questions and detailed answers.
Sign in to ContinueJoin thousands of developers preparing for their dream job.