Data Smart: Using Data Science to Transform Information into Insight Author: John W. Foreman | Language: English | ISBN:
111866146X | Format: EPUB
Data Smart: Using Data Science to Transform Information into Insight Description
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.
- Paperback: 432 pages
- Publisher: Wiley; 1 edition (November 4, 2013)
- Language: English
- ISBN-10: 111866146X
- ISBN-13: 978-1118661468
- Product Dimensions: 9.3 x 7.4 x 0.8 inches
- Shipping Weight: 1.4 pounds (View shipping rates and policies)
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
Disclaimer: I served as a paid technical editor for Data Smart. I am not affiliated with the publisher, but I did receive a small fee for double-checking the book's mathematical content before it went to press. I also went to elementary school with the author. So as you read the rest of the review, keep in mind that this reviewer's judgment could be clouded by my lifelong allegiance to Lookout Mountain Elementary School, as well as the Scarface-esque pile of one dollar bills currently sitting on my kitchen table.
Anyway, books about "Data" seem to fit into one of the following categories:
* Extremely technical gradate-level mathematics books with lots of Greek letters and summation signs
* Pie-in-the-sky business bestsellers about how "Data" is going to revolutionize the world as we know it. (I call these "Moneyball" books)
* Technical books about the hottest new "Big Data" technology such as R and Hadoop
Data Smart is none of these. Unlike "Moneyball" books, Data Smart contains enough practical information to actually start performing analyses. Unlike most textbooks, it doesn't get bogged down in mathematical notation. And unlike books about R or the distributed data blah-blah du jour, all the examples use good old Microsoft Excel. It's geared toward competent analysts who are comfortable with Excel and aren't afraid of thinking about problems in a mathematical way. It's goal isn't to "revolutionize" your business with million-dollar software, but rather to make incremental improvements to processes with accessible analytic techniques.
I don't work at a big company, so I can't attest to the number of dollars your company will save by applying the book's methods.
When I began to read the introduction for this book, after receiving it as a gift - I was a bit disheartened. I am not one of personas listed in the 'Who Are You" section - a CEO or VP of an online startup, a beginner BI analyst. Instead, I am a software developer specializing in data visualization and data analysis.
Furthermore, Excel is far from my preferred research tool of choice. I like code instead of screenshots. Python, Ruby, and R are where I turn when I want to look at data.
*Even* with this mismatch of intended audience, I found myself engrossed in this book, reading it cover to cover in a few days.
Data Smart is a wonderful resource. The use of Excel as a primary means for exploring data science concepts is surprisingly effective. It strips away all the code magic. You can't rely on SciKit-learn, or Weka, or even proper functions when all you have are cells and sheets.
Instead, it provides a way for John Foreman to break down these complex concepts into the fundamental components that make them tick. You start to see the patterns between seemingly disparate technologies that are actually built off the same few bits of logic. Things start to click.
The writing and real-world situations are really what make it fun and worth reading through and enjoying the ride. John's style hits the sweet spot between clarity and comical. Each chapter is well scoped. You understand the rational behind why someone might want to use the particular tool being described to solve the problem at hand. The whimsy and flare added by the author moves the plot along at a good pace. The problems are simple enough to wrap your head around - but not toys. The datasets generated for this book must have taken a while to curate.
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