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

This chapter is super short compared to others in this book. Why? Because naïve Bayes is easy! And that's why folks love it. Naïve Bayes appears to be working some kind of complex magic when in reality it just relies on the computer to have a good memory of how often each token in the training data showed up in each class.

There's a proverb that goes, “Experience is the father of wisdom and memory the mother.” Nowhere is this truer than with naïve Bayes. Its entire faux-wisdom stems from a combination of past data and storage with a little bit of mathematical duct tape.

Naïve Bayes lends itself particularly well to simple implementations in code. For example, here's a C# implementation:

http://msdn.microsoft.com/en-us/magazine/jj891056.aspx

Here's a tiny version someone posted online in Python:

http://www.mustapps.com/spamfilter.py

Here's one in Ruby:

http://blog.saush.com/2009/02/11/naive-bayesian-classifiers-and...