Why SQL is essential

Most analytics work happens where the data lives:

• Relational databases (PostgreSQL, MySQL, SQLite)
• Data warehouses (BigQuery, Snowflake, Redshift)

SQL is used to:

• Retrieve data efficiently
• Aggregate metrics (DAU, revenue, churn)
• Join multiple tables (users + orders + events)

Core mental model

• Data is stored in tables.
• A query reads rows/columns and returns a result set.

SELECT *
FROM users
LIMIT 10;

SELECT *
FROM users
LIMIT 10;

Typical analytics tables

• users(user_id, created_at, country, plan)users(user_id, created_at, country, plan)
• orders(order_id, user_id, order_ts, amount)orders(order_id, user_id, order_ts, amount)
• events(user_id, event_ts, event_name, device)events(user_id, event_ts, event_name, device)

What you’ll learn in this phase

• SELECTSELECT, WHEREWHERE, ORDER BYORDER BY, LIMITLIMIT
• Aggregations: COUNTCOUNT, SUMSUM, AVGAVG
• GROUP BYGROUP BY, HAVINGHAVING
• Joins
• Window functions
• CTEs
• Using SQL from Python (pandas)