File Size: 83683 KB
Print Length: 409 pages
Publisher: Wiley; 1 edition (October 31, 2013)
Publication Date: October 31, 2013
First, a drop about me from the perspective of this book. We have been an IT professional for many many years specializing in programming, database, plus MS Office add-ons. Portion of my job involves self enrichment, that is usually, expand my working understanding in areas potentially essential for my job. I chose Foreman's book to assist using this task for a number of reasons: a) Data Science is a hot area and my company does have a Data Science group, b) I have lots of data experience under my belt - I experienced which it would be nice for once to find helpful information from the data, and c) I have a excellent Excel history - so I figured that Foreman's approach would be ideal for me - little did I know that I would seriously add to my Excel bag of techniques.
The author makes the assumptions that: a) the reader is usually somewhat technical, b) he or she knows nothing about Information Science, and c) he is relatively comfortable working within Excel.
Reading through the book is a joy because Foreman has a cozy, chummy type. He definitely doesn't throw all the technical products at the reader rat-tat-tat machine gun style such as many other authors. Instead, Honcho, chief, gaffer boss gently introduces his topics and then ramps up specialized details carefully. This many definitely helps the learning process.
Communicating of learning, by typically the ending of the a person will have learned essential concepts in " machine learning" and I believe you may be ready for typically the next step. I certain was. I found typically the topics interesting and We wanted to learn more. This specific is where the book's only problem area will come into play - the next phase. Foreman has 3 references - one good, yet minor, one terrible, plus the other is unacceptable. Let me explain.
Foreman recommends a free resource as a follow-on to his Forecasting Chapter. This is a good reference, but We believe Forecasting is a minor topic in Information Science, unless, of program, Forecasting becomes your thing.
Foreman's main reference is: " Information Mining with R" by Luis Torgo. Foreman recommends this as the next step after his book. We tried to read this particular several times, but could not. It certainly wasn't my next step.
The other reference, " The Elements of Statistical Learning" by Trevor Hastie, et. al, is totally unacceptable for Data Science newbies. You can checkout typically the Amazon reviews for this book and you'll see that you have to have a pretty severe background in statistics in order to get anything out of that reference. In fact , typically the author Hastie says all the in his next book " An Introduction in order to Statistical Learning- with Programs in R". This is usually the appropriate next step, but I'll get in order to that inside a moment.
Listed here are my suggestions:
A. Go through Foreman's book and follow along with him within working through the Stand out spreadsheets. This is a first step in getting comfy with Machine Learning.
B. Take typically the Coursera courses: 1) Device Learning Foundations: A Situation Study Approach, and 2) Machine Learning: Regression. Typically the courses are free unless a person want completion certificates, within which case there is usually nominal cost.
C. You now are ready for: An Introduction to Statistical Understanding: with Applications in L (Springer Texts in Statistics) This book is also available for free of charge by the authors : check online., Disclaimer: We served as a compensated technical editor for Information Smart. I am not really affiliated with the publisher, yet Used to do receive a little charge for double-checking the book's mathematical content before it attended press. I also went to elementary school together with the author. So since you read the sleep of the review, take into account that this reviewer's judgment could be clouded by my lifelong allegiance to Lookout Mountain Elementary School, as properly as the Scarface-esque pile of one dollar charges currently sitting on my kitchen table.
Anyway, books about " Data" seem to be to fit into 1 from the following categories:
2. Extremely technical gradate-level math concepts books with lots of Greek letters and summation signs
* Pie-in-the-sky business bestsellers about how precisely " Data" is going to better the entire world as we know it. (I call these kinds of " Moneyball" books)
2. Technical books about the best new " Big Data" technology such as L and Hadoop
Data Intelligent is none of these kinds of. Unlike " Moneyball" books, Data Smart contains adequate practical information to really start performing analyses. As opposed to most textbooks, it won't get bogged down within mathematical notation. And as opposed to books about R or even the distributed data blah-blah du jour, all typically the examples use good aged Microsoft Excel. It's geared in the direction of competent analysts that are comfortable with Stand out and aren't afraid of considering problems in a mathematical way. It's objective isn't to " revolutionize" your business with million-dollar software, but rather to create incremental improvements to procedures with accessible analytic strategies.
I don't work in a major company, so We can't confirm the quantity of dollars your business will save by applying typically the book's methods. But We can attest that the author makes difficult mathematical principles accessible with his nice sense of humor plus surprise for metaphor. With regard to example, I previously was not exposed to the nitty-gritty of clustering techniques. After a few hours together with the clustering chapters, which often include illuminating diagrams plus spreadsheet formulas, I seemed I had a great handle within the concepts, plus would feel at ease implementing typically the ideas in Excel -- or any other terminology, for that matter.
What I like most about typically the book is that it doesn't try to influx a magic data wand to cure all your carrier's ills. Instead it concentrates on a few places where data and inductive techniques can deliver a concrete benefit, and gives a person just enough to acquire started. Particularly:
* Optimisation techniques (Ch. 4) may systematically reduce the cost of manufacturing inputs
* Clustering strategies (Ch. 2 and 5) can deliver insights in to customer behavior
* Predictive techniques (Ch. 3, six, and 7) can boost margins with better forecasts of uncertain outcomes
2. Forecasting techniques (Ch. 8) can reduce waste together with better demand planning
This may take some creativity to determine how to utilize the methods to your own business processes, but all of the strategies are " tried plus true" in the sense of being widely deployed in large companies with huge analytics budgets and clubs of Ph. D. is on staff. This book's contribution is always to make these kinds of techniques offered to anyone together with a little background within applied mathematics and a copy of Excel. With regard to that reason, despite the shortage of glitter and/or Plug Welch on the book's cover, I think Information Smart is a crucial business book.
I had formed a couple of criticisms of the book as I was reading drafts, but almost just about all of them were tackled before the final revision. For the sake of completeness, I'll inform you what they were. Some of the chapters ran on a lttle bit long, but these kinds of have been split up into manageable pieces. Typically the Optimization chapter is a lttle bit of a doozie, and applied to be on the very beginning, but the reader are now able to " warm up" with some easier chapters on clustering and simple Bayesian techniques. The Regression chapter formerly didn't discuss Receiver Operating Characteristic curves, which often are important for analyzing predictive models visually, great ROC curves are ample.
Only one real criticism from me remains: We would have liked in order to see more on quantile regression, which is only pointed out in passing. It's a great technique for working with outlier-heavy data. The book by Koenker has great but highly mathematical protection, and I might have loved to see this issue given the Foreman therapy. But, you can't have everything, and I imagine John has to leave a few material for Data Intelligent 2: The Spreadsheet of Doom.
In sum, Information Smart is a well-written plus engaging guide to getting new insights from data using familiar tools. Typically the techniques aren't really cutting-edge -- in fact, many have developed for a long time -- but in order to my knowledge this is usually the first time they've been presented in a way that Excel-slinging business analysts can apply typically the methods without the need for her very own team of procedures researchers and data scientists. In case you're not sure whether the book's sophistication is usually on par with your own skills, you may download a complete sample chapter (as well since example spreadsheets) from typically the author's website.
One final thing: unlike many books with a technical curved, the prose is interesting and extremely clear. I consider this can be tracked to John's childhood. Whenever John misbehaved, his daddy (who is a professor of English) would penalize John by forcing him to read a novel by Charles Dickens. Minor infractions resulted in A Christmas Jean being meted out, plus when having been really poor he had to read Fantastic Expectations. This is a true story which a person should ask John about if you see him at a book-signing occasion.
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