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

Chapter 9
Outlier Detection: Just Because They're Odd Doesn't Mean They're Unimportant

Outliers are the odd points in a dataset—the ones that don't fit somehow. Historically, that's meant extreme values, meaning quantities that were either too large or small to have come naturally from the same process as the other observations in the dataset.

The only reason people used to care about outliers was because they wanted to get rid of them. Statisticians a hundred years ago had a lot in common with the Borg: a data point needed to assimilate or die. However, this was done with good reason (in the case of the statistician)—outliers can move averages and mess with spread measurements in the data. A good example of outlier removal is in gymnastics, where the highest and lowest judges' scores are always trimmed from the data before taking the average score.

Outliers have a knack for messing up machine learning models. For example, in Chapters 6 and...