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 "e;data scientist,"e; 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.

Data Science gets thrown around in the press like it'smagic. Major retailers are predicting everything from when theircustomers are pregnant to when they want a new pair of ChuckTaylors. It's a brave new world where seemingly meaningless datacan be transformed into valuable insight to drive smart businessdecisions.But how does one exactly do data science? Do you have to hireone of these priests of the dark arts, the "data scientist," toextract this gold from your data? Nope.Data science is little more than using straight-forward steps toprocess raw data into actionable insight. And in DataSmart, author and data scientist John Foreman will show you howthat's done within the familiar environment of aspreadsheet.Why a spreadsheet? It's comfortable! You get to look at the dataevery step of the way, building confidence as you learn the tricksof the trade. Plus, spreadsheets are a vendor-neutral place tolearn data science without the hype.But don't let the Excel sheets fool you. This is a book forthose serious about learning the analytic techniques, the math andthe magic, behind big data.Each chapter will cover a different technique in aspreadsheet so you can follow along:* Mathematical optimization, including non-linear programming andgenetic algorithms* Clustering via k-means, spherical k-means, and graphmodularity* Data mining in graphs, such as outlier detection* Supervised AI through logistic regression, ensemble models, andbag-of-words models* Forecasting, seasonal adjustments, and prediction intervalsthrough monte carlo simulation* Moving from spreadsheets into the R programming languageYou get your hands dirty as you work alongside John through eachtechnique. But never fear, the topics are readily applicable andthe author laces humor throughout. You'll even learnwhat a dead squirrel has to do with optimization modeling, whichyou no doubt are dying to know.

Introduction xiii1 Everything You Ever Needed to Know about Spreadsheets but WereToo Afraid to Ask 12 Cluster Analysis Part I: Using K-Means to Segment YourCustomer Base 293 Naïve Bayes and the Incredible Lightness of Being anIdiot 774 Optimization Modeling: Because That "Fresh Squeezed" OrangeJuice Ain't Gonna Blend Itself 1015 Cluster Analysis Part II: Network Graphs and CommunityDetection 1556 The Granddaddy of Supervised ArtificialIntelligence--Regression 2057 Ensemble Models: A Whole Lot of Bad Pizza 2518 Forecasting: Breathe Easy; You Can't Win 2859 Outlier Detection: Just Because They're Odd Doesn't MeanThey're Unimportant 33510 Moving from Spreadsheets into R 361Conclusion 395Index 401