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Data Smart: Using Data Science to Transform Information into Insight

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en Limba Engleză Carte Paperback – 22 Nov 2013
"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 Company
Most 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 spreadsheet
Visit 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 Bayes
  • Clustering via k–means, spherical k–means, and graph modularity
  • Mathematical optimization, including non–linear programming and genetic algorithms
  • Working with time series data and forecasting with exponential smoothing
  • Using Monte Carlo simulation to quantify and address risk
  • Detecting outliers in single or multiple dimensions
  • Exploring the data–science–focused R language
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Specificații

ISBN-13: 9781118661468
ISBN-10: 111866146X
Pagini: 432
Dimensiuni: 187 x 235 x 20 mm
Greutate: 0.73 kg
Editura: John Wiley & Sons, Inc.
Locul publicării: Hoboken, United States

Public țintă

Primary audience: Data–driven managers and marketers, management consultants, business intelligence analysts, demand forecasters, and revenue managers
Secondary audience: Web and software developers, intelligence analysts, supply chain analysts, STEM (acronym for the fields of study in the categories of science, technology, engineering, and mathematics undergraduates)

Textul de pe ultima copertă

"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 Company
Most 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 spreadsheet
Visit 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 Bayes
  • Clustering via k–means, spherical k–means, and graph modularity
  • Mathematical optimization, including non–linear programming and genetic algorithms
  • Working with time series data and forecasting with exponential smoothing
  • Using Monte Carlo simulation to quantify and address risk
  • Detecting outliers in single or multiple dimensions
  • Exploring the data–science–focused R language

Cuprins

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

Notă biografică

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.