SQL for Data Science

SQL is one of the most practical, widely-used skills in data science - most organizational data still lives in relational databases, and SQL is how data scientists reliably pull, shape, and validate that data before any analysis or modeling begins.

Where SQL Fits in the Data Science Workflow

• Data extraction - pulling the exact rows and columns needed for a project
• Data cleaning - filtering out invalid rows, handling NULLs, standardizing text with string functions
• Feature engineering - creating aggregated or derived columns directly in a query (e.g. total orders per customer)
• Exploratory analysis - quickly summarizing data with GROUP BY and aggregate functions before deeper analysis in Python
• Validation - sanity-checking a model's predictions or a report's numbers against the source data

A Typical Data Science Query

SELECT c.city,
       COUNT(DISTINCT o.customer_id) AS active_customers,
       SUM(o.amount) AS total_revenue,
       AVG(o.amount) AS avg_order_value
FROM customers c
JOIN orders o ON c.customer_id = o.customer_id
WHERE o.order_date >= DATE_SUB(CURRENT_DATE, INTERVAL 90 DAY)
GROUP BY c.city
ORDER BY total_revenue DESC;

Pairing SQL with Python

A common workflow is running an initial SQL query to extract and pre-aggregate data, then loading the result into a pandas DataFrame in Python for deeper statistical analysis, visualization, or model building.

You've Completed This Section

This wraps up the SQL for Data Science module - from filtering, joining, and aggregating data, through string and date functions, CASE logic, and views, to seeing how it all comes together in a real data science workflow. With this foundation, you're ready to confidently pull and shape data straight from a database for any analysis.