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

Outliers Are (Bad?) People, Too

Consider when your credit card company calls you after you make a transaction that is potentially fraudulent. What's your credit card company doing? They're detecting that transaction as being an outlier based on your past behavior. Rather than ignoring the transaction because it's an outlier, they're purposefully flagging the potential fraud and acting on it.

At MailChimp when we predict spammers before they send, we're predicting outliers. These spammers are a small group of people whose behavior lies outside of what we as a company consider normal. We use supervised models similar to those in Chapters 6 and 7 to predict based on past occurrences when a new user is going to send spam.

So in the case of MailChimp, then, an outlier is no more than a small but understood class of data in the population that can be predicted using training data. But what about the cases when you don't know what you're looking for? Like those...