#### 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.
Free Chapter
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
Go ahead and flip back to the Basketball Game Sales tab. You can still reference a cell here from the previous tab, Calories, by simply placing the tab name and “!” in front of a referenced cell. For example, `Calories!B2` is a reference to the calories in beer regardless of what sheet you're working in.
Now, what if you wanted to toss the calorie data into a column back on the sales sheet so that next to each item sold the appropriate calorie count was listed? You'd somehow have to look up the calorie count of each item sold and place it into a column next to the transaction. Well, it turns out there's a formula for that called `VLOOKUP`.
Go ahead and label Column F in the spreadsheet Calories for this purpose. Cell F2 will include the calorie count for the first beer transaction from the Calories table. Using the `VLOOKUP` formula, you supply the item name from cell A2, a reference to the table `Calories!\$A\$1:\$B\$15`, and the relative...