Data Smart: Using Data Science to Transform Information into Insight
"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