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

Data Smart: Using Data Science to Transform Information into Insight

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.

List Price: $ 45.00

Price: $ 24.30

Related Web Design Tutorial Products





3 responses to “Data Smart: Using Data Science to Transform Information into Insight Reviews”

  1. Evan Miller Avatar
    Evan Miller
    38 of 38 people found the following review helpful
    5.0 out of 5 stars
    Insightful, practical, and colorful. Perspective from a biased reviewer., November 5, 2013
    Evan Miller (Chicago, IL) –

    Amazon Verified Purchase(What’s this?)
    This review is from: Data Smart: Using Data Science to Transform Information into Insight (Paperback)

    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. But I can attest that the author makes difficult mathematical concepts accessible with his quirky sense of humor and gift for metaphor. For example, I previously had not been exposed to the nitty-gritty of clustering techniques. After a couple of hours with the clustering chapters, which include illuminating diagrams and spreadsheet formulas, I felt like I had a good handle on the concepts, and would feel comfortable implementing the ideas in Excel — or any other language, for that matter.

    What I like most about the book is that it doesn’t try to wave a magic data wand to cure all of your company’s ills. Instead it focuses on a few areas where data and analytic techniques can deliver a concrete benefit, and gives you just enough to get started. In particular:

    * Optimization techniques (Ch. 4) can systematically reduce the cost of manufacturing inputs

    * Clustering techniques (Ch. 2 and 5) can deliver insights into customer behavior

    * Predictive techniques (Ch. 3, 6, and 7) can increase margins with better predictions of uncertain outcomes

    * Forecasting techniques (Ch. 8) can reduce waste with better demand planning

    It may take some creativity to figure out how to apply the methods to your own business processes, but all of the techniques are “tried and true” in the sense of being widely deployed at large companies with big analytics budgets and teams of Ph.D.’s on staff. This book’s contribution is to make these techniques available to anyone with a little background in applied mathematics and a copy of Excel. For that reason, despite the absence of glitter and/or Jack Welch on the book’s cover, I think Data Smart is an important business book.

    I had a few criticisms of the book as I was reading drafts, but almost all of them were addressed before the final revision. For the sake of completeness, I’ll tell you what they were. Some of the chapters ran on a bit long, but these have been split up into manageable pieces. The Optimization chapter is a bit of a doozie, and used to be at the very beginning, but the reader can now “warm up” with some easier chapters on clustering and simple Bayesian techniques. The Regression chapter originally didn’t discuss Receiver Operating Characteristic curves, which are important for evaluating predictive models visually, but now ROC curves are abundant.

    Only one real criticism from me remains: I would have liked to see more on quantile regression, which is only mentioned in passing. It’s a great technique for dealing with outlier-heavy data. The book by Koenker has good but highly mathematical coverage, and I would have loved to see this subject given the Foreman treatment. But, you can’t have everything, and I suppose John needs to leave some material for Data Smart 2: The Spreadsheet of Doom.

    In sum, Data Smart is a well-written and engaging guide to getting new insights from data using familiar tools. The techniques aren’t really cutting-edge — in fact, most have been around for decades — but to my knowledge this is the first time they’ve been presented in a way that Excel-slinging…

    Read more

    Help other customers find the most helpful reviews 

    Was this review helpful to you? Yes

  2. Jeff Feinsmith Avatar
    Jeff Feinsmith
    12 of 13 people found the following review helpful
    5.0 out of 5 stars
    Data Science and Advanced Analytic Techniques for the Masses!!, November 4, 2013
    Jeff Feinsmith (Atlanta, GA USA) –

    This review is from: Data Smart: Using Data Science to Transform Information into Insight (Paperback)
    This book is perfect for the business or technical person that needs to understand the “magic” the analysts or data scientists are doing, as well as anyone that needs to be conversant in the techniques and avoid being bamboozled by consultants and software sellers.

    Rather than focus on the data scientist or provide yet another useless big data overview, with very easy to understand language and a nice touch of humor, Mr. Foreman makes the nuts and bolts of analytic techniques easily understood and relevant for anyone with basic math skills and a spreadsheet program on their PC or Mac.

    Mr. Foreman, with many easily understood real world-ish examples (e.g., Joey Bag O’Donuts Wholesale Wine Emporium) teaches a wide variety of AI, clustering, mathematical optimization, time series/forecasting, simulation and other techniques as well as when to employ them.


    Help other customers find the most helpful reviews 

    Was this review helpful to you? Yes

  3. Jim Vallandingham Avatar
    Jim Vallandingham
    7 of 7 people found the following review helpful
    5.0 out of 5 stars
    Reminds you that technical books can be insightful and fun to read, December 20, 2013
    Jim Vallandingham (Lawrence, KS United States) –

    This review is from: Data Smart: Using Data Science to Transform Information into Insight (Paperback)
    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. The book is really fun to read.

    I think for me this book provides a great reminder of the landscape of data science tools, as well as a story-telling process to describe and relate these tools to non-programmy non-programmers.

    Even if you aren’t a startup CEO… yet – this book is worth having on your shelf. Check it out today!


    Help other customers find the most helpful reviews 

    Was this review helpful to you? Yes

Leave a Reply