SQL for Data Analytics · Lesson 3 of 3

SQL for Data Analytics

Query relational data with filters, joins, groups, constraints, transactions, and reusable database objects.

Lesson

1. Introduction

SQL is the standard language for asking questions of relational data. Analysts use it to select the needed records, connect related tables, aggregate at a declared grain, and return a manageable result for reporting or further analysis.

2. Core concepts

A query is evaluated logically from its source and joins through filtering, grouping, and projection. A join can multiply rows when the key is not unique, so verify cardinality before trusting totals. Constraints and transactions protect data; indexes can improve selected access patterns but do not make every query faster.

3. Topics covered in this module

Database, table, schema, primary key, foreign key, and constraints
SELECT, WHERE, ORDER BY, LIMIT, CASE, and date functions
INSERT, UPDATE, DELETE, transactions, and safe predicates
INNER/LEFT joins, UNION, GROUP BY, HAVING, subqueries, views, indexes, and procedures

4. Syntax and how to read it

SELECT region, SUM(amount) AS revenue
FROM orders
WHERE order_date >= '2026-01-01'
GROUP BY region
HAVING SUM(amount) > 1000
ORDER BY revenue DESC;

WHERE filters individual rows before grouping; HAVING filters groups after aggregation. Use explicit columns and stable date boundaries rather than SELECT * in reusable reports.

5. Practical workflow

Explore table definitions and row counts, test the join keys on a small sample, then build the query in stages. Compare totals before and after each join and explain the grain of the final result.

6. Common mistakes

Updating or deleting rows without first checking the WHERE predicate in a transaction or safe environment.
Joining tables on a non-unique key and silently inflating sums.

7. Best practices

Qualify ambiguous column names and use parameterized queries from application code.
Check row counts and key uniqueness before and after a join.

8. Practice task

Write SQL to find each product category with more than 100 units sold.

9. One possible solution

SELECT category, SUM(quantity) AS units_sold
FROM order_items
GROUP BY category
HAVING SUM(quantity) > 100;

10. Summary

Query relational data with filters, joins, groups, constraints, transactions, and reusable database objects. Qualify ambiguous column names and use parameterized queries from application code. Check row counts and key uniqueness before and after a join.

11. What comes next

The next module, Advanced Python, builds on this foundation and applies it to a new part of the analytics workflow.

Visual guide

A reliable SQL for Data Analytics workflow

Step 01 Frame
Define the question, input scope, and intended output.
Step 02 Work
Apply the method and validate assumptions as you go.
Step 03 Check
Compare results with known cases and communicate limitations.

Code examples

Count completed orders per customer

SQL

SELECT customer_id, COUNT(*) AS order_count
FROM orders
WHERE status = 'completed'
GROUP BY customer_id;

Expected output

One row per customer with at least one completed order.

Join orders to customer names

SQL

SELECT c.customer_name, o.order_id, o.amount
FROM customers AS c
JOIN orders AS o ON o.customer_id = c.customer_id;