Data Smart
Using Data Science to Transform Information into Insight
By John W. Foreman
Publisher: Wiley
Final Release Date: October 2013
Pages: 432

"Data Smart makes modern statistic methods and algorithmsunderstandable and easy to implement. Slogging through textbooksand academic papers is no longer required!"
Patrick Crosby, Founder of StatHat & first CTO atOkCupid

"When Mr. Foreman interviewed for a job at my company, hearrived dressed in a 'Kentucky Colonel' kind of suit and spokeabout nonsensical things like barbecue, lasers, and orange juicepulp. Then, he explained how to de-mystify and solve just about anycomplex 'big data' problem in our company with simple spreadsheets.No server clusters, mainframes, or Hadoop-a-ma-jigs. Just Excel. Ihired him on the spot. After reading this book, you too will learnhow to use math and basic spreadsheet formulas to improve yourbusiness or, at the very least, how to trick senior executives intohiring you as their data scientist."
Ben Chestnut, Founder & CEO ofMailChimp

"You need a John Foreman on your analytics team. But if youcan't have John, then reading this book is the next bestthing."
Patrick Lennon, Director of Analytics, TheCoca-Cola Company

Most people are approaching data science all wrong. Here'show to do it right.

Not to disillusion you, but data scientists are not mysticalpractitioners of magical arts. Data science is something you cando. Really. This book shows you the significant data sciencetechniques, how they work, how to use them, and how they benefityour business, large or small. It's not about coding or databasetechnologies. It's about turning raw data into insight you can actupon, 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 todownload spreadsheets for each chapter, and follow them as youlearn about:

  • Artificial intelligence using the general linear model,ensemble methods, and naive Bayes
  • Clustering via k-means, spherical k-means, and graphmodularity
  • Mathematical optimization, including non-linear programming andgenetic algorithms
  • Working with time series data and forecasting with exponentialsmoothing
  • 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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Ebook: $45.00
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