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Data Smart: Using Data Science to Transform Information into Insight

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Imported from USA

From the Back Cover

"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
—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 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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About the Author

John W. Foreman is Chief Data Scientist for, 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.

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