SQL for Data Analysis: Queries Every Analyst Should Know

Why SQL Still Dominates Data Work

Python and R get attention, but most business data lives in relational databases accessed through SQL. Analysts, backend developers, and data scientists all need SELECT fluency. SQL is declarative — you describe what data you want, and the engine figures out how to fetch it efficiently.

Start with SQLite or PostgreSQL locally. Write queries against sample datasets until joins and aggregations feel natural before touching ORMs that hide the underlying logic.

Core Query Patterns

Practice the classic analyst questions: monthly revenue, top customers, cohort retention, and funnel conversion. These patterns repeat across companies.

• SELECT with WHERE filters and ORDER BY sorting.
• JOINs: INNER, LEFT, and when each preserves rows.
• GROUP BY with COUNT, SUM, AVG for aggregations.
• HAVING to filter grouped results.
• Subqueries and CTEs (WITH clauses) for readable multi-step logic.

Performance and Clean SQL

Avoid SELECT * in production queries — fetch only needed columns. Index awareness matters at scale: filtering on unindexed columns slows dashboards. EXPLAIN plans show how the database executes your query.

Use consistent formatting and aliases. Future you will thank present you when debugging a 40-line report query at month-end.