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

For More Information

If you just love supervised AI, and this chapter wasn't enough for you, then let me make some reading suggestions:

  • Data Mining with R by Luis Torgo (Chapman & Hall/CRC, 2010) is a great next step. The book covers machine learning in the programming language, R. R is a programming language beloved by statisticians everywhere, and it's not hard to pick up for AI modeling purposes. In fact, if you were going to productionalize something like the model in this chapter, R would be a great place to train up and run that production model.
  • The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani, and Jerome Friedman (Springer, 2009) takes an academic look at various AI models. At times a slog, the book can really up your intellectual game. A free copy can be found on Hastie's Stanford website.

For discussion with other practitioners, I usually head to the CrossValidated forum at StackExchange (stats.stackexchange.com). Oftentimes, someone...