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Grokking the Data Science Interview

Take-Home Assignments

Explore how to structure and execute a reproducible data science take-home assignment using Python and pandas. Learn to define a clear scope, clean and merge datasets properly, validate results, and write interpretable summaries that highlight findings and limitations. This lesson guides you in producing a defendable analysis suitable for review.

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A three-hour take-home asks us to analyze sessions and purchases, recommend one change, and leave behind an artifact another analyst can rerun. Computing another metric is not the constraint. The constraint is choosing a small question whose answer remains interpretable after cleaning, merging, and review.

For this prompt, we can scope the deliverable to one descriptive decision aid: compare purchase conversion by acquisition channel, after defining a session as eligible only when user_id and session_date are present. We will report the number of eligible sessions, purchasing sessions, and the conversion rate for each channel. We will defer causal claims about why one channel converts differently, detailed funnel diagnosis, and any model.

A useful scope statement makes both the boundary and the evidence concrete:

Within three hours, we will produce a reproducible notebook that compares eligible-session conversion across acquisition channels, validates the session-to-purchase merge, and states whether the observed difference is large enough to prioritize further investigation. We will not claim that acquisition channel causes conversion, because the supplied observational data does not establish causality.

The notebook outline follows directly from that decision:

  • Question

  • Data & Assumptions

  • Method

  • Results

  • Sanity Checks

  • Limitations & Next Steps

This scope protects time for the checks that make the recommendation defensible. A broad notebook with five charts and no denominator policy cannot answer the narrower question reliably.

A notebook that another analyst can inspect

Once the scope fixes the question, notebook structure makes the data flow reviewable rather than forcing a reader to infer it from cells. Each section should answer a distinct question.

  • Question states the decision the analysis informs and names the primary metric. For example, purchase_conversion_rate is purchasing eligible sessions divided by eligible sessions.

  • Data and assumptions state the analysis unit, source-table grain, time range, and policies for duplicates and missing values. If sessions has one row per session while purchases has one row per purchase, the notebook must say so.

  • Method shows staged transformations. Name the cleaned tables and the aggregated tables rather than nesting the entire analysis in one expression.

  • Results presents one metric table or one plot, then ...