Welcome

Welcome to SQL for Data Analysis with DuckDB. You already know the basics — SELECT, WHERE, JOIN, maybe GROUP BY — and you can read a simple query without flinching. This course is about the next leap: using SQL not to power an application, but to think with data.

That shift is bigger than it sounds. The same SELECT keyword that fetches one user's profile in an app can, with a different mindset, summarize ten million sales into a single trend line. Learning how analysts wield SQL — and why analytical queries are shaped the way they are — is the whole point of what follows.

Who this course is for

You will feel at home here if any of these describe you:

• You can write basic SELECT queries and understand tables, rows, and joins — but you have never used SQL to analyze anything.
• You have heard terms like OLAP, window function, aggregation, or exploratory data analysis and want to know what they really mean.
• You use spreadsheets or a BI tool for analysis and suspect SQL would let you go faster and further.
• You keep hearing about DuckDB and want to understand why analysts are so excited about it.

You do not need:

• Any experience with data analysis, statistics, or BI tools.
• Any experience with DuckDB specifically.
• Anything installed on your computer.

What you do need is comfort with basic SQL: SELECT, FROM, WHERE, ORDER BY, and a rough idea of what a JOIN does. If those feel shaky, our Introduction to SQL and Relational Databases course builds them from scratch.

What you will be able to do

By the end of the course you will be able to:

• Explain how analytical (OLAP) work differs from transactional (OLTP) application work — and why that difference shapes every query.
• Explore an unfamiliar dataset quickly: profile it, slice it, and find what is interesting.
• Aggregate and summarize millions of rows into the handful of numbers a decision actually needs.
• Use window functions to rank, compute running totals, and compare each row to its neighbors.
• Transform and reshape data — conditional logic, dates, and pivots.
• Compose large analytical queries from small, readable building blocks with CTEs and subqueries.
• Turn raw rows into business metrics and build reproducible analytical workflows.

What this course is not

This is an analytics course. It deliberately spends little or no time on database administration topics — production deployment, high availability, replication, security hardening, transactions, multi-user concurrency, ORMs, query-planner internals, or cloud infrastructure. Those matter for running a database in production, but they are not what an analyst reaches for when asking questions of data.

How the course is organized

We start with mindset, not syntax. The first section explains how analytical thinking differs from the application-development SQL you may have seen, and why a tool like DuckDB exists. Only then do we start querying — and from that point on, every new technique answers a real analytical question you already care about.

The three kinds of interactive widgets

You will meet three kinds of interactive elements:

1. Runnable SQL blocks. A live DuckDB editor. Some blocks pre-load sample data for you; edit the query, click Run, and inspect the result table.
2. Challenge cards. Small problems with hidden tests — write a query, run the tests, and see which ones pass.
3. Multiple-choice questions. Quick conceptual checks, with an explanation for every choice, so you can confirm an idea has landed before moving on.

A first taste

You do not need to understand this yet. Just click Run and notice how few lines it takes to turn a pile of individual sales into a ranked, per-region summary — the essence of analytical SQL.

That query says, in almost plain English: "For each region, how many orders were placed, how much money came in, and how big was the typical order — and show me the biggest regions first." Reading the raw rows, you could not answer that in your head. The query does it instantly, and would do it just as instantly over ten million rows.