Data Analysis and Insight Generation Questions

Ability to convert raw data into clear, evidence based business insights and prioritized recommendations. Candidates should demonstrate end to end analytical thinking including data cleaning and validation, exploratory analysis, summary statistics, distributions, aggregations, pivot tables, time series and trend analysis, segmentation and cohort analysis, anomaly detection, and interpretation of relationships between metrics. This topic covers hypothesis generation and validation, basic statistical testing, controlled experiments and split testing, sensitivity and robustness checks, and sense checking results against domain knowledge. It emphasizes connecting metrics to business outcomes, defining success criteria and measurement plans, synthesizing quantitative and qualitative evidence, and prioritizing recommendations based on impact feasibility risk and dependencies. Practical communication skills are assessed including charting dashboards crafting concise narratives and tailoring findings to non technical and technical stakeholders, along with documenting next steps experiments and how outcomes will be measured.

EasyTechnical

1 practiced

Define 'conversion rate' for an e-commerce checkout funnel. Provide two concrete definitions (a narrow and a broad version), explain how numerator and denominator choices change interpretation, and give a short example where ambiguous definition led to conflicting decisions across teams.

HardTechnical

0 practiced

With limited engineering capacity, propose a prioritization framework to score incoming analytical work (ad-hoc requests, dashboards, experiments, data-quality fixes). Define scoring dimensions (impact, effort, risk, dependencies), provide example scores for 3 hypothetical requests, and explain how you'd operationalize the intake and periodic review with stakeholders.

EasyBehavioral

0 practiced

Tell me about a time you had to push back on a stakeholder who requested a dashboard that would surface misleading metrics due to poor data quality. Use the STAR structure and explain what evidence you presented, how you proposed an alternative, and what the final outcome was.

HardTechnical

0 practiced

Leadership asks for a robustness analysis of a reported 5% improvement after a policy change. Lay out a plan of sensitivity tests across sample selection, model specifications, and external shocks; describe how you'd compute confidence intervals for alternative specifications and how you'd present a worst-case/best-case scenario to the executive team.

EasyTechnical

0 practiced

Given a pandas DataFrame `orders` with columns (order_id, user_id, category, amount, order_ts, currency), write a short Python/pandas snippet (or describe it) to compute the top 5 product categories by revenue in the last 30 days, handling missing category values and a simple currency conversion table. Also list sanity checks you'd run on the output.

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