John W. Foreman 
Data Smart 
Using Data Science to Transform Information into Insight

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.

Inhaltsverzeichnis

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

Über den Autor

John W. Foreman is Chief Data Scientist for Mail Chimp.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.