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.
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Cover
Credits
Acknowledgments
Introduction
Chapter 2: Cluster Analysis Part I: Using K-Means to Segment Your Customer Base
Chapter 3: Naïve Bayes and the Incredible Lightness of Being an Idiot
Chapter 4: Optimization Modeling: Because That “Fresh Squeezed” Orange Juice Ain't Gonna Blend Itself
Chapter 5: Cluster Analysis Part II: Network Graphs and Community Detection
Chapter 6: The Granddaddy of Supervised Artificial Intelligence—Regression
Chapter 7: Ensemble Models: A Whole Lot of Bad Pizza
Chapter 8: Forecasting: Breathe Easy, You Can't Win
Chapter 9: Outlier Detection: Just Because They're Odd Doesn't Mean They're Unimportant
Chapter 10: Moving From Spreadsheets into R
Conclusion

The World's Fastest Intro to Probability Theory

In the next couple sections I'm going to use the notation p() to talk about probability. For instance:

1. p(Michael Bay's next film will be terrible) = 1
2. p(John Foreman will ever go vegan) = 0.0000001

Sorry, it's extremely unlikely that I'll ever give up Conecuh smoked sausage—the one thing I like that comes out of Alabama.

Totaling Conditional Probabilities

Now, the previous two examples are simple probabilities, but what you're going to be working with a lot in this chapter are conditional probabilities. Here's a conditional probability:

1. p(John Foreman will go vegan | you pay him \$1B) = 1

Although the odds of me ever going vegan are extremely low, the probability of me going vegan given you pay me a billion dollars is 100 percent. That vertical bar | in the statement is used to separate the event from what it's being conditioned on.

How do you reconcile the 0.0000001 overall vegan probability...