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

Where Am I? What Just Happened?

You may have started this book with a rather ordinary set of skills in math and spreadsheet modeling, but if you're here, having made it through alive (and having not just skipped the first 10 chapters), then I imagine you're now a spreadsheet modeling connoisseur with a good grasp of a variety of data science techniques.

This book has covered topics ranging from classic operations research fodder (optimization, Monte Carlo, and forecasting) to unsupervised learning (outlier detection, clustering, and graphs) to supervised AI (regression, decision stumps, and naïve Bayes). You should feel confident working with spreadsheet data at this higher level.

I also hope that Chapter 10 showed you that now that you understand data science techniques and algorithms, it's quite easy to use those techniques from within a programming language such as R.

And if there's a particular topic that really grabbed you in this book, dive deeper! Want...