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, the math and the magic, behind big data.

 Each chapter will cover a different technique in a spreadsheet so you can follow along:

• Mathematical optimization, including non-linear programming and genetic algorithms
• Clustering via k-means, spherical k-means, and graph modularity
• Data mining in graphs, such as outlier detection
• Supervised AI through logistic regression, ensemble models, and bag-of-words models
• Forecasting, seasonal adjustments, and prediction intervals through monte carlo simulation
• Moving from spreadsheets into the R programming language

You get your hands dirty as you work alongside John through each technique. But never fear, the topics are readily applicable and the author laces humor throughout. You'll even learn what a dead squirrel has to do with optimization modeling, which you no doubt are dying to know.

Introduction xiii

1 Everything You Ever Needed to Know about Spreadsheets but Were Too Afraid to Ask 1

2 Cluster Analysis Part I: Using K-Means to Segment Your Customer Base 29

3 Naïve Bayes and the Incredible Lightness of Being an Idiot 77

4 Optimization Modeling: Because That "Fresh Squeezed" Orange Juice Ain't Gonna Blend Itself 101

5 Cluster Analysis Part II: Network Graphs and Community Detection 155

6 The Granddaddy of Supervised Artificial Intelligence—Regression 205

7 Ensemble Models: A Whole Lot of Bad Pizza 251

8 Forecasting: Breathe Easy; You Can't Win 285

9 Outlier Detection: Just Because They're Odd Doesn’t Mean They're Unimportant 335

10 Moving from Spreadsheets into R 361

Conclusion 395

Index 401