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

Between the graph modularity chapter and this chapter on outlier detection, you've been exposed to the power of analyzing a dataset by “graphing” your data, that is, assigning distances and edges between your observations.

Although in the clustering chapters, you mined groups of related points for insights, here you mined the data for points outside of communities. You saw the power of something as simple as indegree to demonstrate who's influential and who's isolated.

For more on outlier detection, check out the 2010 survey put together by Kriegel, Kroger, and Zimek at http://www.siam.org/meetings/sdm10/tutorial3.pdf for the 2010 SIAM conference. All the techniques in this chapter show up there along with a number of others.

Note that these techniques don't require any kind of arbitrarily long-running process the way optimization models might. There are a finite number of steps to get LOFs, so this kind of thing can be coded in production...