Welcome to the third module of this course. There are certainly a lot of cool new tools for data scientists to use, and SQL hardly feels like the cutting edge, but it's an incredibly powerful tool for moving data around, and it just never seems to go out of style. I think about SQL like a hand drill on a building project. Sure, there is equipment that is fancier, newer, more suited to certain specific tasks, and yet the hand drill is still an essential tool. Perhaps not a tool you brag about using, but when you're always happy to have nearby. The joins and built-in functions themselves are only part of using SQL. Much in the same way that being able to fit a drill bit and turn it on are only part of using a drill. Even if you're just building a garden bed, there's outlining and planning that goes into the project before you ever have time to drill anything. For a garden bed, you need to be familiar with using wood as a building material, and you need to be able to cut it precisely, so it can be arranged in the right shape. Similarly, in complicated analysis, there's often separate pieces that need to be prepared and planned out before the joins occur. One wouldn't say that they're good at using a drill unless they were also capable of doing the planning and design required to execute the project. Being good at SQL means being familiar with the dataset, and being able to create and plan using different tables and sub-queries. So in this section, I'm going to give you lots of practice answering data questions and sketching out a strategy to answer it. I'll often call this process scaffolding a query as a reference to this building metaphor. Sometimes also call it a wireframe, which is a word borrowed from design. I suspect most folks don't have a word for the process when it comes to writing a query though. By the end of this section, you'll be able to map out your joins and be able to highlight the level of detail needed for different kinds of questions. Essentially, you'll be able to practice answering data questions, which should help you feel ready to get a whole, after a whole bunch of questions, big questions, ambiguous questions, even poorly worded questions. Finally, you'll develop a strategy for answering those questions using data. Let's get started.