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.

EasyTechnical

57 practiced

What is data leakage in the context of an analysis or a dashboard? Give two concrete examples (one temporal, one schema-derived) of a field that would leak future information, and explain how that would mislead whoever consumes the result.

Sample Answer

Direct answer Data leakage is when information that wouldn't actually be available at decision time sneaks into your analysis or dashboard, making a result look better or more certain than it really is. Two common flavors: temporal leakage, where a field reflects something that happened AFTER the point in time you're supposedly making the decision at, and schema-derived leakage, where a field is mechanically DERIVED from the very outcome you're trying to explain or predict. Why it misleads whoever consumes the result Leakage inflates apparent performance or apparent insight in a way that evaporates the moment the analysis meets reality, because the leaking information simply won't exist yet when someone tries to act on the finding. A dashboard or model built on leaked data looks great in testing and then quietly fails (or never actually helps) in production, since the shortcut it learned isn't available going forward. Worked example Temporal leakage: a churn dashboard includes "total number of support tickets filed," computed using ALL tickets a customer ever filed, including ones filed after they already churned (often a spike of complaints right around cancellation). The dashboard will show "high ticket count strongly predicts churn," but that's a symptom recorded after the fact, not a usable early-warning signal. Schema-derived leakage: a "days until first purchase" field is included as a predictor of "will this customer make a purchase," but the field is undefined (or null) for anyone who never purchases, meaning the field mechanically encodes the answer. Trade-offs and pitfalls The fix is always to ask "would this exact value have been available and true at the moment I'd need to act on it," not just "does this field correlate with the outcome." A field can be perfectly legitimate for a retrospective report ("how many tickets did churned customers file, in total") while being leakage if the same field is used to predict or dashboard something forward-looking.

EasyTechnical

65 practiced

You're exploring a labeled dataset with two or more annotators. Explain inter-annotator agreement and how Cohen's kappa measures it beyond chance agreement. What would a low kappa alongside a high raw percent-agreement tell you about the labels?

Sample Answer

EasyTechnical

82 practiced

Name and give an example of the main types of bias that can distort an analysis: selection bias, measurement bias, confirmation bias, and survivorship bias (the classic WWII bomber-armor story is the canonical example of the last one). Why does noticing these matter more during EDA than at any later stage?

Sample Answer

MediumTechnical

73 practiced

Distinguish random measurement error from systematic measurement error (like a sensor that slowly drifts, or a logging pixel that changes mid-series). How would you detect a systematic shift in your data during EDA, and how would you estimate its magnitude before deciding whether to correct historical records?

Sample Answer

HardTechnical

74 practiced

During EDA you find a strong correlation between a protected attribute (or a close proxy like ZIP code) and your target variable. What would you do next: how do you document the finding, what fairness checks would you run before anyone builds on this data, and who do you loop in?

Sample Answer

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Exploratory Data Analysis and Data Quality Questions

Sample Answer

Direct answer

Why it misleads whoever consumes the result

Worked example

Trade-offs and pitfalls

Sample Answer

Direct answer

Why kappa can diverge sharply from percent agreement

Worked example

Trade-offs and pitfalls

Sample Answer

Direct answer

Why noticing these matters most during EDA

Worked example

Trade-offs and pitfalls

Sample Answer

Direct answer

Detecting drift versus noise

Worked example

Trade-offs and pitfalls

Sample Answer

Direct answer

Why this needs more than a technical fix

Worked example

Trade-offs and pitfalls

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