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

0 practiced

Explain propensity score matching end-to-end and provide pseudocode or Python-style code snippets for: 1) estimating propensity scores, 2) matching treated and control units (nearest neighbor), and 3) checking covariate balance. Discuss trade-offs in caliper selection and matching with/without replacement.

MediumTechnical

0 practiced

You fit a linear regression predicting purchase amount and observe: high VIFs (>10) for some categorical dummies, a funnel-shaped residuals vs fitted plot, and one large leverage point. Describe concerns these diagnostics raise and propose concrete remediation steps for each (feature engineering, transformations, robust methods).

HardTechnical

0 practiced

You suspect survivorship bias in a dataset that records only customers who made repeat purchases. Propose an analysis plan to estimate the true churn distribution and correct for survivorship bias. What auxiliary data would you need, and what statistical techniques (weighting, imputation, bounds) would you use to produce reliable estimates?

EasyTechnical

0 practiced

Given a transactions table with schema:

sql

transactions(transaction_id bigint, user_id bigint, product_id bigint, amount decimal, occurred_at timestamp)

Write an SQL query that returns the number of weekly active users (unique user_id) per product for the last 12 weeks, with week starting on Monday. The result should have columns: week_start_date, product_id, weekly_active_users. Use standard SQL (window functions acceptable).

HardTechnical

0 practiced

You want to model customer churn using survival analysis. Explain censoring, Kaplan-Meier estimator, and Cox proportional hazards model. Describe how you would prepare data (start/end, censor indicator), check proportional hazards assumption, and interpret hazard ratios to a business stakeholder.

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