The Practical Data Analyst.

Turn vague stakeholder questions into measurable analytical problems, hypotheses and decision criteria.

Learn grain, keys, event data, dimensions, facts, relationships and how business systems become tables.

Use SQL to answer business questions with filters, joins, CTEs, dates and window functions.

Define metrics precisely so revenue, conversion, retention and churn mean the same thing to everyone.

Find duplicate rows, broken joins, missing data, bad denominators and results that fail simple sanity checks.

Choose charts for the question, design useful dashboards and explain what a decision-maker needs to know.

Use distributions, confidence, variance, seasonality and correlation carefully in everyday analysis.

Use pandas and notebooks for cleaning, exploratory analysis and work that becomes awkward in SQL.

Give AI the schema, business context and analytical brief it needs to generate useful first-pass work.

Audit generated SQL, calculations and explanations for double counting, logic errors and invented conclusions.

Design repeatable analytical workflows that combine metrics, verified queries, business rules and AI.

Analyze activation, funnels, cohorts, retention, engagement and feature adoption.

Reason about demand, supply, matching, cancellations, incentives and geographic performance.

Measure acquisition, CAC, ROAS, funnel conversion, attribution limits and customer quality.

Break down revenue, AOV, repeat purchase, categories, discounts, returns and refunds.

Investigate SLA, capacity, turnaround time, backlog, productivity and service quality.

Separate correlation from causation and reason about A/B tests, controls, bias and confounding.

Clarify requests, push back when needed, communicate uncertainty and turn evidence into recommendations.

Practice SQL screens, metric questions, product cases, dashboard critiques and analytical communication.

Solve one ambiguous business problem from raw data to SQL, visualization, executive memo and recommendation.