# Solving Stuff with Solver

Many of the techniques you'll study in this book can be boiled down to *optimization models*. An optimization problem is one where you have to make the best decision (choose the best investments, minimize your company's costs, find the class schedule with the fewest morning classes, or so on). In optimization models then, the words “minimize” and “maximize” come up a lot when articulating an objective.

In data science, many of the practices, whether that's artificial intelligence, data mining, or forecasting, are actually just some data prep plus a model-fitting step that's actually an optimization model. So it'd make sense to teach optimization first. But learning all there is to know about optimization is tough to do straight off the bat. So you'll do an in-depth optimization study in Chapter 4 *after* you do some more fun machine learning problems in Chapters 2 and 3. To fill in the gaps though, it&apos...