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.

HardTechnical

56 practiced

Provide an optimized strategy with example queries to compute conversion funnels and the top-k conversion paths from event logs containing billions of rows, minimizing joins and data shuffling. Describe use of pre-aggregations, daily rollups, approximate algorithms (HyperLogLog/Count-Min Sketch), and how you'd validate approximate results versus exact ones.

MediumSystem Design

101 practiced

You must build an executive Tableau dashboard showing weekly business health (revenue, MAU, conversion, NPS). Outline dashboard layout, top-level KPIs, drilldowns, filters, recommended data model (live connection vs extract), and security/access considerations for executives and analysts.

EasyTechnical

62 practiced

Write a SQL query or describe the approach to compute weekly retention rates for cohorts defined by user signup week. Input tables: `users(user_id, signup_date)` and `events(user_id, event_date)`. Return a cohort table where each row is signup_week and retention for week 0..12 as percent of cohort active in that week. Explain handling of users with multiple events per week.

EasyTechnical

47 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.

EasyTechnical

49 practiced

A new CSV feed of product price updates is being integrated into your BI system. Describe the validation checks you would run before allowing it to feed dashboards: include schema validation, value ranges, null-rate thresholds, row-count expectations, and freshness checks. Provide example SQL/Python checks you would automate.

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