Paperback: 456 pages
Publisher: Wiley; 1 edition (April 14, 2014)
Product Dimensions: 7.4 x 0.9 x 9.3 inches
Shipping Weight: 2 pounds (View shipping rates and policies)
Average Customer Review: 4.1 out of 5 stars See all reviews (24 customer reviews)
Best Sellers Rank: #343,542 in Books (See Top 100 in Books) #105 in Books > Computers & Technology > Databases & Big Data > Data Warehousing #361 in Books > Textbooks > Computer Science > Database Storage & Design
I’ve read dozens of books on data mining. I’m also lead author on a data book that specifically uses IBM SPSS Modeler. Full disclosure: the author of this book and I coauthored the book about Modeler.This book takes a unique, and badly needed, approach to the subject. It is a “how-to” without being a software book. Too many software instruction books focus so much on features and functions that you lose sight of the big picture. Also, too many data mining books focus solely on algorithms – often one chapter per algorithm. While many of those books are good, and necessary, there are plenty of them already.This book invests approximately equal coverage to the six phases of the Cross Industry Standard Process for Data Mining (CRISP-DM). The evidence that the author is an expert is easy to find. Rather than merely providing the usual boilerplate on statistical significance, he reminds the reader that data miners interpret the ability of their model to generalize differently and with different tools. Rather than writing a section on regression right out of a introductory statistics book, he shows how he sometimes uses regression for classification, an approach that is technically against the rules. Rather than just a laundry list of algorithms he dedicates an entire chapter to ensembles, describing it not as another algorithm, but as a way of thinking about problems. His descriptions of boosting and bagging are clear and succinct. The essence of the book is in someways captured by the fact that one brief section is entitled “Models Ensembles and Occam’s Razor,” a section that praises ensembles even though they seem to threaten parsimony.Perhaps, most importantly, he gives lots of advice. A book like this, on a topic like this, can be overwhelming in its factual detail.
I like to compare this book with a very similar one from O'Reilly, entitled "Data Science for Business" by Foster Provost and Tom Fawcett.Both books are organized around the Cross-Industry Standard Process Model for Data Mining (CRISP-DM), which groups data mining / predictive analytics project tasks into the following six distinct stages:* Business Understanding: Define the project (e.g., what are the business and data modeling objectives, how are they aligned, what would be the target and/or input variables, what criteria would be used for evaluating the models, how would the models be deployed, etc)* Data Understanding: Examine the data; identify potential problems with the data* Data Preparation: Fix problems in the data (e.g., decide what to do with outliers and missing values; standardize data formats etc.); create derived variables; transform and/or normalize data* Modeling: Build predictive or descriptive models* Evaluation: Assess models; report on the expected effects of models* Deployment: Use the models; monitor model performanceIn terms of coverage, this book provides guidance for all of the above-mentioned stages, with particular attention to the Data Understanding, Data Preparation, and Evaluation stages, while the Provost and Fawcett book provides guidance mostly for the Business Understanding, Data Understanding, Modeling, and Evaluation stages only.This book covers more modeling algorithms than the Provost and Fawcett book, but both books tend to keep discussions of the covered algorithms to qualitative descriptions only, instead of the in-depth mathematical discussions found in more theoretically-oriented books.
This groundbreaking contribution to the field of predictive analytics delivers a unique gift: A how-to that is accessible, yet quite comprehensive, taking the reader through much of the established teachings of one of the industry's preeminent hands-on instructors. The author, Dean Abbott, is renowned as both a leading "rock star" hands-on consultant in predictive analytics, as well as a fantastic, 5-star-rated conference speaker and an acclaimed training workshop instructor. You get the best of all worlds with this particular expert: deep analytical insights, stellar execution, clear communication, and contagious enthusiasm. And he has translated these assets nicely into a book.Abbott's stated mission with this book (as mentioned in its "Introduction" at the end of the book) is to provide very practical guidance for executing on predictive analytics, as if chatting to someone peering over his shoulder as he works through a project. This mission is accomplished, and in doing so it accomplishes something even more significant: The book takes much of Abbott's well-honed training agenda (do attend his in-person sessions if you can!), along with the accessibility of his casual speaking style, and translates them onto the page. As a result, this book reads in a much more conducive and engaging manner than, say, a more formally structured textbook.The book is extremely practical. It is mostly organized around project execution steps, rather than around analytical methods, application areas, or industry verticals."Applied Predictive Analytics" focuses on the issues and tasks that consume the vast majority of any hands-on predictive analytics project.
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