SQL and Business Data Analysis

In every company past a certain size, the same argument runs on a loop: marketing says retention is 62%, product says it's 48%, finance has a third number, and nobody is technically wrong. They are answering different questions, but each one is showing it as *the* number. The person who can end that argument — quietly, without taking sides — is the person who can read the database, write the query, and pin the definition. This course is how you become that person. It assumes you can navigate a spreadsheet and that you have, at some point, copied a `SELECT` off the internet and gotten it to work once. It does not assume you know what a join is. By the end, given a stakeholder Slack message and a warehouse you've never seen, you can list the tables, work out which ones connect, write a multi-step query as readable CTEs, compute cohort retention or funnel conversion or DAU/MAU the way the industry actually defines them, validate the row counts so you don't ship a number inflated by a silent join blow-up, and write the answer up with the four things it needs to survive contact with other humans — window, population, definition, caveats. A lot of what separates a trusted analyst from a frustrated one is not syntax. It's grain discipline. It's noticing that `NOT IN` silently broke because of a NULL. It's recomputing last week's number and explaining why it's different. It's saying *which* definition of "active user" you used. The lessons here are arranged so each of those habits becomes obvious — not because the course preaches them, but because the order makes the wrong move feel wrong before you make it. The SQL is the easy part. The judgment underneath it is what you'll leave with.