Book Image

Data Smart

By : John W. Foreman
Book Image

Data Smart

By: John W. Foreman

Overview of this book

Data Science gets thrown around in the press like it's magic. Major retailers are predicting everything from when their customers are pregnant to when they want a new pair of Chuck Taylors. It's a brave new world where seemingly meaningless data can be transformed into valuable insight to drive smart business decisions. But how does one exactly do data science? Do you have to hire one of these priests of the dark arts, the "data scientist," to extract this gold from your data? Nope. Data science is little more than using straight-forward steps to process raw data into actionable insight. And in Data Smart, author and data scientist John Foreman will show you how that's done within the familiar environment of a spreadsheet. Why a spreadsheet? It's comfortable! You get to look at the data every step of the way, building confidence as you learn the tricks of the trade. Plus, spreadsheets are a vendor-neutral place to learn data science without the hype. But don't let the Excel sheets fool you. This is a book for those serious about learning the analytic techniques, math and the magic, behind big data.
Table of Contents (18 chapters)
Free Chapter
About the Author
About the Technical Editors
End User License Agreement

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...