Introduction to SQL for Data Science

Understand what SQL is, how relational databases work, and why SQL is the most important language for anyone working with data.

What is SQL?

SQL (Structured Query Language) is the standard language for managing and querying relational databases. Pronounced "sequel" or "S-Q-L," it allows you to create, read, update, and delete data stored in tables.

For data scientists, SQL is often the first tool used to access and explore data. Whether you are pulling data from a data warehouse, running analytics queries, or feeding machine learning pipelines, SQL is essential.

Relational Databases

A relational database organizes data into tables (also called relations) with rows and columns. Each table represents an entity, and relationships between tables are defined through keys.

+----+----------+-------------------+------------+
| id | name     | email             | created_at |
+----+----------+-------------------+------------+
|  1 | Alice    | alice@example.com | 2024-01-15 |
|  2 | Bob      | bob@example.com   | 2024-02-20 |
|  3 | Charlie  | charlie@ex.com    | 2024-03-10 |
+----+----------+-------------------+------------+

Key Concepts

Popular Database Systems

Why SQL for Data Science?

1. Data Access

   Query millions of rows from production databases, data warehouses, and data lakes directly.

2. Data Exploration

   Quickly profile datasets, check distributions, find missing values, and identify patterns.

3. Feature Engineering

   Create features for machine learning models using aggregations, window functions, and joins.

4. Reporting & Dashboards

   Power BI, Tableau, Looker, and other tools all rely on SQL queries under the hood.

Your First SQL Query

SELECT name, email
FROM users
WHERE created_at >= '2024-02-01'
ORDER BY name;

This query selects the name and email columns from the users table, filters for users created after February 1, 2024, and sorts the results alphabetically by name.