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
1
Cover
2
Credits
3
About the Author
4
About the Technical Editors
5
Acknowledgments
18
End User License Agreement

Wrapping Up

You've just seen how a bunch of simple models can be combined via bagging or boosting to form an ensemble model. These approaches were unheard of until about the mid-1990s, but today, they stand as two of the most popular modeling techniques used in business.

And you can boost or bag any model that you want to use as a weak learner. These models don't have to be decision stumps or trees. For example, there's been a lot of talk recently about boosting naïve Bayes models like the one you encountered in Chapter 3.

In Chapter 10, you'll implement some of what you've encountered in this chapter using the R programming language.

If you'd like to learn more about these algorithms, I'd recommend reading about them in The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani, and Jerome Friedman (Springer, 2009).