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    Data Smart: Using Data Science to Transform Information into Insight (Paperback) By (author) John W. Foreman

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    DescriptionData 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.

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    Data Smart
    Using Data Science to Transform Information into Insight
    Authors and contributors
    By (author) John W. Foreman
    Physical properties
    Format: Paperback
    Number of pages: 432
    Width: 188 mm
    Height: 234 mm
    Thickness: 20 mm
    Weight: 722 g
    ISBN 13: 9781118661468
    ISBN 10: 111866146X

    BIC E4L: COM
    B&T Book Type: NF
    BIC subject category V2: UT
    Nielsen BookScan Product Class 3: S10.6
    B&T General Subject: 229
    B&T Merchandise Category: COM
    Ingram Subject Code: XG
    LC classification: TK
    BISAC V2.8: BUS049000
    Warengruppen-Systematik des deutschen Buchhandels: 16360
    BISAC V2.8: COM043000
    DC22: 006.31
    LC subject heading:
    BISAC V2.8: COM004000
    LC subject heading:
    BISAC V2.8: COM021030
    DC23: 004.6
    Thema V1.0: UT, KJT, UNF
    Illustrations note
    illustrations (black and white)
    John Wiley & Sons Inc
    Imprint name
    John Wiley & Sons Inc
    Publication date
    01 December 2013
    Publication City/Country
    New York
    Author Information
    John W. Foreman is Chief Data Scientist for MailChimp.com, where he leads a data science product development effort called the Email Genome Project. As an analytics consultant, John has created data science solutions for The Coca-Cola Company, Royal Caribbean International, Intercontinental Hotels Group, Dell, the Department of Defense, the IRS, and the FBI.
    Back cover copy
    "Data Smart makes modern statistic methods and algorithms understandable and easy to implement. Slogging through textbooks and academic papers is no longer required!"--Patrick Crosby, Founder of StatHat & first CTO at OkCupid"When Mr. Foreman interviewed for a job at my company, he arrived dressed in a 'Kentucky Colonel' kind of suit and spoke about nonsensical things like barbecue, lasers, and orange juice pulp. Then, he explained how to de-mystify and solve just about any complex 'big data' problem in our company with simple spreadsheets. No server clusters, mainframes, or Hadoop-a-ma-jigs. Just Excel. I hired him on the spot. After reading this book, you too will learn how to use math and basic spreadsheet formulas to improve your business or, at the very least, how to trick senior executives into hiring you as their data scientist.""--"Ben Chestnut, Founder & CEO of MailChimp"You need a John Foreman on your analytics team. But if you can't have John, then reading this book is the next best thing.""--"Patrick Lennon, Director of Analytics, The Coca-Cola CompanyMost people are approaching data science all wrong. Here's how to do it right.Not to disillusion you, but data scientists are not mystical practitioners of magical arts. Data science is something you can do. Really. This book shows you the significant data science techniques, how they work, how to use them, and how they benefit your business, large or small. It's not about coding or database technologies. It's about turning raw data into insight you can act upon, and doing it as quickly and painlessly as possible.Roll up your sleeves and let's get going.Relax -- it's just a spreadsheetVisit the companion website at www.wiley.com/go/datasmart to download spreadsheets for each chapter, and follow them as you learn about: Artificial intelligence using the general linear model, ensemble methods, and naive BayesClustering via k-means, spherical k-means, and graph modularityMathematical optimization, including non-linear programming and genetic algorithmsWorking with time series data and forecasting with exponential smoothingUsing Monte Carlo simulation to quantify and address riskDetecting outliers in single or multiple dimensionsExploring the data-science-focused R language
    Table of contents
    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 Naive 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