Data Analyst Toolkit

Writes correct, vectorized pandas for cleaning, joining, reshaping, and aggregating tabular data, with validated joins and deliberate dtype and missing-data handling. Use when someone asks "why did my merge duplicate rows", "how do I clean this CSV in pandas", "my groupby numbers look wrong", "this apply is too slow", or is transforming a DataFrame for analysis or a pipeline. Do NOT use for first-pass profiling of an unfamiliar dataset - use eda-playbook instead; for data too large for one machine use spark-jobs; for answering business questions directly in SQL use sql-to-insights.

Analyzes an A/B experiment to a defensible verdict - sample-size and minimum-detectable-effect math, two-proportion significance testing with confidence intervals, and checks for the traps that fake a win (peeking, sample ratio mismatch, multiple comparisons). Use when someone asks "is this test significant", "did variant B win", "how long should I run this experiment", "what sample size do I need", or shows experiment results and asks whether to ship. Do NOT use for logging and organizing ML training runs - use experiment-tracking instead; for causal questions without a randomized experiment use causal-inference; for general funnel or metric investigation use funnel-analysis or product-analytics.

Builds conversion funnels from raw event data with drop-off attribution, segment comparison, and significance testing, and delivers a filled funnel table with a diagnosed bottleneck and next action. Use when someone asks "where are users dropping off", "why did checkout conversion fall this week", "build a signup-to-activation funnel", or "is mobile converting worse than desktop". Do NOT use for rewriting page copy or layout to lift a known weak step - use landing-page-cro instead; for decomposing active-user growth into new, retained, resurrected, and churned - use growth-accounting instead; for choosing which events to instrument and which product metrics to track - use product-analytics instead; for designing and reading a controlled experiment - use ab-test-analyzer instead.

Turns transaction data into cohort retention, lifetime value, RFM segments, and churn predictions tied to concrete actions. Use when someone asks "what's our customer LTV", "build a cohort retention table", "which customers are about to churn", "how do repeat purchase rates look by cohort", or "who should we target with a win-back offer". Do NOT use for event-level product usage and feature adoption analysis - use product-analytics instead; for defining market segments for positioning and messaging - use segmentation-strategy instead; for designing the interventions that reduce churn once at-risk customers are identified - use churn-reduction instead; for step-by-step conversion drop-off - use funnel-analysis instead.

Turns data and charts into a decision-driving narrative structured as headline finding, trend, implication, and recommended action - with finding-led chart titles, context for every number, annotation guidance, and honest flags on any conclusion the data cannot support. Use when someone says "turn these numbers into a story", "what's the takeaway from this data", "help me present these results to leadership", or has charts but no narrative. Do NOT use for compressing a long document into a one-pager - use executive-summary instead - or for running the analysis that produces the findings - use eda-playbook instead.

Builds SaaS revenue models - a reconciling MRR/ARR bridge, cohort retention forecasting, a driver tree from leads to new ARR, and base/upside/downside scenarios. Use when someone asks "model my ARR for next year", "what will MRR be if churn doubles", "build a revenue forecast for the board", "why doesn't my MRR bridge tie out", or "what NRR do we need to hit our plan". Do NOT use for sizing the market opportunity - use market-sizing instead; for CAC, LTV, and payback analysis use unit-economics; for a full P&L or headcount-driven operating model use fpa-model; for cash timing and runway use cash-flow-forecast.