Use AI to draft and debug SQL without losing SQL skills. This guide covers grain, joins, aggregation, validation and deliberate practice.

Ten useful AI prompt patterns for Data Analysts, with guidance on when to use them, what to verify, and how to avoid turning prompts into a substitute for judgment.

A verification-first workflow for using AI in data analysis: define context, inspect assumptions, test outputs, check edge cases and document decisions.

Learn how Data Analysts can design dashboard first screens around decisions, KPI context, trends, drivers and clear storytelling instead of visual clutter.

Use this Data Analyst data validation checklist to catch duplicates, NULLs, bad joins, date gaps, category errors, outliers and reconciliation problems before reporting.

Learn cohort analysis for Data Analysts, including cohort definition, retention tables, time-based behavior and common data-quality mistakes.

Understand the difference between a KPI and a metric, and learn how Data Analysts can define measures with clear formulas, owners, targets and business decisions.

Learn a practical root cause analysis process for Data Analysts: baseline the metric, validate data quality, segment the change and test causal explanations.