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Applied Predictive Analytics: Principles and Techniques for the Professional Data Analyst
 
 

Applied Predictive Analytics: Principles and Techniques for the Professional Data Analyst [Kindle Edition]

Dean Abbott
5.0 out of 5 stars  See all reviews (1 customer review)

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Product Description

Product Description

Learn the art and science of predictive analytics — techniques that get results

Predictive analytics is what translates big data into meaningful, usable business information. Written by a leading expert in the field, this guide examines the science of the underlying algorithms as well as the principles and best practices that govern the art of predictive analytics. It clearly explains the theory behind predictive analytics, teaches the methods, principles, and techniques for conducting predictive analytics projects, and offers tips and tricks that are essential for successful predictive modeling. Hands-on examples and case studies are included.

  • The ability to successfully apply predictive analytics enables businesses to effectively interpret big data; essential for competition today
  • This guide teaches not only the principles of predictive analytics, but also how to apply them to achieve real, pragmatic solutions
  • Explains methods, principles, and techniques for conducting predictive analytics projects from start to finish
  • Illustrates each technique with hands-on examples and includes as series of in-depth case studies that apply predictive analytics to common business scenarios
  • A companion website provides all the data sets used to generate the examples as well as a free trial version of software

Applied Predictive Analytics arms data and business analysts and business managers with the tools they need to interpret and capitalize on big data.


Product details

  • Format: Kindle Edition
  • File Size: 14968 KB
  • Print Length: 408 pages
  • Page Numbers Source ISBN: 1118727967
  • Publisher: Wiley; 1 edition (31 Mar 2014)
  • Sold by: Amazon Media EU S.à r.l.
  • Language: English
  • ASIN: B00JHEWQSY
  • Text-to-Speech: Enabled
  • X-Ray:
  • Average Customer Review: 5.0 out of 5 stars  See all reviews (1 customer review)
  • Amazon Bestsellers Rank: #285,369 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
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Most Helpful Customer Reviews
1 of 1 people found the following review helpful
Format:Paperback
This is a clear and concise overview of what is a burgeoning field. What Dean manages to do is relate the hype around this area to consistent and well established techniques and paradigms.

Whilst the amount of data available has grown and the lack of prior definition, structure and setup is clear, there is a need to approach the techniques with a level head, and help bring some order and standardisation to the way problems are addressed.

This book does just that.
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Amazon.com: 4.0 out of 5 stars  16 reviews
11 of 11 people found the following review helpful
5.0 out of 5 stars Comprehensive Approach 29 May 2014
By Keith McCormick - Published on Amazon.com
Format:Kindle Edition|Verified Purchase
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. Knowledge of how the technique works does not imply action in and of itself. You need to know what you should do with this information. Applied Predictive Analytics is a coaching and mentoring session with someone that has been doing it for more than 20 years.
11 of 12 people found the following review helpful
5.0 out of 5 stars The Established Teachings a Preeminent Hands-on Instructor 31 May 2014
By Eric Siegel - Published on Amazon.com
Format:Kindle Edition|Verified Purchase
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. Some reviewers of this book - as well as others in the industry in general - appear to believe you must understand the theory behind the analytical modeling methods in order to be an effective hands-on practitioner of the art. There's a religious debate to be had over this. But, either way, this book covers necessary knowledge; no one book covers all this as well as all the in-depth math behind analytical modeling methods. In the end, executing on predictive analytics in a commercial context is an empirical exercise more than an exercise in applying theory. For example, pragmatic choices in the data preparation often makes a much bigger difference than the choice of predictive modeling method. Also, regardless of the modeling method employed and its theoretically sound capabilities, the proof is always in the pudding: The results of modeling must be empirically validated over unseen test data. It's a kind of experimental science.

I do feel this book can serve as a great follow-on for "digging in" after reading my book, "Predictive Analytics," which, unlike Abbott's book, is not a how-to, but rather introduces the concepts and provides an industry overview.

Eric Siegel, Ph.D.
Founder, Predictive Analytics World
Author, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die
6 of 6 people found the following review helpful
4.0 out of 5 stars Different books for different people 11 Jun 2014
By Lee - Published on Amazon.com
Format:Paperback|Vine Customer Review of Free Product (What's this?)
[Good]

There are few books on predictive analytics. Most of them are either university textbooks with prices to match or encompass other areas of analytics such as classification (which isn't a bad thing).

[Ok]

It's not really a pure introduction to subject, nor is it a definitive guide. You will need some context and some really basic knowledge of statistics. This will not satisfy either the expert or the beginner.

[Bad]

There are no exercises to apply and test your knowledge. It's really hard to gauge your understanding of a subject, when you're just being spoon fed content.

[Verdict]

It's a good starter for someone who's vaguely familiar with analytics in general, who needs some light knowledge very quickly.
5 of 5 people found the following review helpful
5.0 out of 5 stars Excellent Review Predictive Analyticand Data Mining Techniques for Business Decision Making 18 Jun 2014
By Ira Laefsky - Published on Amazon.com
Format:Paperback|Vine Customer Review of Free Product (What's this?)
This handbook for the data analysts emphasizes the tools which predictive analytics and data provide for decision maker, rather than particular software tools or the detailed mathematical analysis which underlies these techniques. It provide a framework for using and choosing analytic methods for decision making based upon the CRISP-DM model. It does not draw a strict line between
"Predictive Analytics" and "Decision Analytics" and cover such methodologies as Classification, Clustering and Text Data Mining, which I might have thought lie within the domain of Decision Analytics.

This handbook is a valuable addition to the library of a business analyst or decision maker attempting to make the bet use of modern data mining and analytic techniques.
8 of 10 people found the following review helpful
3.0 out of 5 stars Some improvements could make this a better book 9 Jun 2014
By Techie Evan - Published on Amazon.com
Format:Paperback|Vine Customer Review of Free Product (What's this?)
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 performance

In 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. The Provost and Fawcett book does provide better and slightly deeper descriptions of the covered algorithms, however.

Both books cover Decision Trees, for example. Whereas this book only mentions that Decision Trees belong to a class of recursive partitioning algorithms that use concepts such as "Information Gain" or "Gini Index" as possible partitioning criteria, the Provost and Fawcett book goes further by illustrating how "Information Gain" can actually be computed using simple formulas and a small enough but still interesting dataset. By learning how to hand-build the resulting Decision Tree from scratch using the illuminating but still simple example, readers of that book are likely to have more memorable insights about Decision Trees than those they can acquire from this book.

Compared to the Provost and Fawcett book, which I think is the better book pedagogically speaking because it does more things right for its readers, this book could also probably benefit from the following suggested improvements:

* Be more selective regarding what gets discussed in the Business Understanding chapter. Although it is true that the project plan that is being drawn at this stage should include deliberations about how models are going to get evaluated, a few statements indicating this should suffice. There is probably no need to make specific mentions of things such as Lift, Gain, ROC, Area Under the Curve, and Confusion Matrices which don't really get defined and discussed much, much later in the book. By doing so, the author is being laudably meticulous but risks unnecessarily distracting his readers to details they might not yet be equipped to process.

* Reconsider the ordering of some chapters. An earlier modeling chapter discusses Kohonen Self-Organizing Maps (SOM)-- a type of neural networks -- before the more basic neural networks discussion takes place in a later chapter. A chapter on how to interpret Clusters discusses the use of Decision Trees for this purpose before Decision Trees themselves are discussed in a later chapter. Having now read the book, I think reversing those chapters would make the book more readable for those who may not yet know much about neural networks or decision trees.

* Consider using color graphics. Some texts in the book read as though readers were looking at color graphics but in print those graphics were actually in grey-scale.

For readers interested in knowing what modeling algorithms are covered in this book, they include: Itemsets and Association Rules (or Market Basket Analysis), Principal Components Analysis, Clustering (K-Means and Kohonen SOM), Decision Trees, Logistic Regression, Neural Networks, K-Nearest Neighbor, Naive Bayes, Linear Regression, and Model Ensembles (Bagging, Boosting, etc.).

Finally, for readers curious about possible prerequisites for this book, I would say they include basic knowledge of statistics including understanding of concepts such as z-scores, correlations, and ANOVA (Analysis of Variance), and some SQL concepts such as group by and where clauses. The more knowledge you have of these concepts, the easier time you would have reading this book.
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