Data Science gets thrown around in the press like it'smagic. Major retailers are predicting everything from when theircustomers are pregnant to when they want a new pair of ChuckTaylors. It's a brave new world where seemingly meaningless datacan be transformed into valuable insight to drive smart businessdecisions.
But how does one exactly do data science? Do you have to hireone of these priests of the dark arts, the "data scientist," toextract this gold from your data? Nope.
Data science is little more than using straight-forward steps toprocess raw data into actionable insight. And in DataSmart, author and data scientist John Foreman will show you howthat's done within the familiar environment of aspreadsheet.
Why a spreadsheet? It's comfortable! You get to look at the dataevery step of the way, building confidence as you learn the tricksof the trade. Plus, spreadsheets are a vendor-neutral place tolearn data science without the hype.
But don't let the Excel sheets fool you. This is a book forthose serious about learning the analytic techniques, the math andthe magic, behind big data.
Each chapter will cover a different technique in aspreadsheet so you can follow along:
- Mathematical optimization, including non-linear programming andgenetic algorithms
- Clustering via k-means, spherical k-means, and graphmodularity
- Data mining in graphs, such as outlier detection
- Supervised AI through logistic regression, ensemble models, andbag-of-words models
- Forecasting, seasonal adjustments, and prediction intervalsthrough monte carlo simulation
- Moving from spreadsheets into the R programming language
You get your hands dirty as you work alongside John through eachtechnique. But never fear, the topics are readily applicable andthe author laces humor throughout. You'll even learnwhat a dead squirrel has to do with optimization modeling, whichyou no doubt are dying to know.
|Product dimensions:||7.30(w) x 9.10(h) x 0.80(d)|
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
1 Everything You Ever Needed to Know about Spreadsheets but WereToo Afraid to Ask 1
2 Cluster Analysis Part I: Using K-Means to Segment YourCustomer Base 29
3 Naïve Bayes and the Incredible Lightness of Being anIdiot 77
4 Optimization Modeling: Because That "Fresh Squeezed" OrangeJuice Ain't Gonna Blend Itself 101
5 Cluster Analysis Part II: Network Graphs and CommunityDetection 155
6 The Granddaddy of Supervised ArtificialIntelligence—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 MeanThey're Unimportant 335
10 Moving from Spreadsheets into R 361