Synopses & Reviews
"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
Synopsis
The book provides nine tutorials on optimization, machine learning, data mining, and forecasting all within the confines of a spreadsheet. Each tutorial uses a real-world problem and the author guides the reader using query’s the reader might ask as how to craft a solution using the correct data science technique. Hosting these nine spreadsheets for download will be necessary so that the reader can work the problems along with the book.
Important topics covered by the book:
- Linear and integer programming
- K-nearest neighbors graphs and clustering
- Logistic regression
- Demand forecasting with seasonal adjustments
- Price sensitivity, revenue optimization, and price-sensitive forecasting
- Naïve Bayes classification
- Outlier detection using graphs and Local Outlier Factors
- Multi-criteria decision analysis
Synopsis
A straightforward approach to implementing data science techniquesAlthough many organizations continue to grow increasingly dependent on analytics to make sense of their data, many of these data science practices are hidden under layers of code and complex database technologies. That's where this book comes in. Using straightforward, easy-to-understand language, author and chief data scientist John Foreman shows you how to solve data problems of optimization, machine learning, data mining, and forecasting in a non-intimidating tutorial format.
- Features nine tutorials that each use a real-world problem to which the author guides you through crafting a solution
- Covers linear and integer programming, logistic regression, and demand forecasting with seasonal adjustments
- Examines price sensitivity, revenue optimization, and price-sensitive forecasting
- Addresses outlier detection using graphs and local outlier factors as well as multi-criteria decision analysis
Data Smart is smart reading for anyone eager to use data science to make sense of data and drive smart business decisions.
Synopsis
Data 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.
About the Author
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.
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 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