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Data Mining

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"This is a great book if you are looking for a concept-driven textbook ..." - by E. Ritchie
This is a great book if you are looking for a concept-driven textbook and strong overview of data mining. I find myself reaching for this book more than my more traditionally academic books when working with others. The people I work with want to understand how the techniques work in general; they aren't after impressive equations or technical language, impressive though it might sound. The more conversational tone is accessible and ease to dip back into for a reminder when used as a high level reference text.

Overall, an especially good library addition for people working with non-stats people.

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""Torture the data enough and it will confess to anything." Ronald Coase, Nobel Prize Laureate in Economics" - by Robert Morris (Dallas, Texas)
As Foster Provost and Tom Fawcett explain in the Preface, they examine concepts that fall within one of three types:

"1. Concepts about how data science fits into the organization and the competitive landscape, including ways to attract, structure, and nurture data science teams; ways for think about how data science leads to competitive advantage; and tactical concepts for doing well with data science projects.

2. General ways of thinking data, analytically. These help in identifying appropriate data and consider appropriate methods. The concepts include the [begin italics] data mining process [end italics] as well as the collection of different [begin italics] high-level data mining tasks. [end italics]

3. General concepts for actually extracting knowledge from data, which undergird the vast array of data science tasks and their algorithms."

There you have the nature and extent of the WHAT on which the information, insights, and counsel focus. Provost and Fawcett devote most of their ... full review

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"Data mining for all?" - by Edafe Onerhime
I'm going to start by saying I really enjoyed this book. I've worked with data for nearly 20 years but there's always something new, interesting and energising to learn.

However, my bugbear is creating a bubble for data geeks, data nerds and big data consultants. Insights are for everyone and data is one resource we can tap into for that. I am always on the lookout for books that help non-techies understand techie concepts and most importantly, what's in it for them. This book falls squarely into that plain English, no bull approach.

Meta S. Brown aims this book squarely at domain experts - people who already have the know how that comes from working daily in their chosen fields. She then goes on to demonstrate how they can benefit from one method of manipulating data for insight: data mining.

I won't pretend this book is perfect - it's not. I understand some ... full review

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"magnificent" - by EFB
the authors are over 30 years masters in their work. it is hardto make a better overview of statistical learning (or machine learning, data science).
beside the formulas and tables there are many figures, in color. these figures give a very good idea how al types of analyses work. After reading a lot of books in this field one can say that this is the best.

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"Good book to start reading about Data Mining" - by Wojciech Starosta
Good overview. Cross-section by every important technique with mentioning other stuff like OLAP cubes. Will give You a notion about every important thing in data mining, but won't tell much about details of described techniques. But still worth reading!

"Two Paths to Prediction" - by John M. Ford (near DC, MD USA)
This is a good text on machine learning techniques from both the statistics and the machine learning perspectives. The authors note that these fields have developed in parallel with many researchers and practitioners working in each, but few familiar with the full range of techniques in both disciplines. Some procedures, such as tree induction and nearest neighbor clustering techniques, have been developed independently in both fields. However, for the most part statistics has focused on hypothesis testing and machine learning has tried to optimize search through the space of possible hypotheses. This book presents techniques from both traditions.

The organizational structure of the book supports its use as either a comprehensive text or a modular reference. The first section's five chapters introduce the foundations of data mining. In addition to concepts and definitions, there are simple example data sets and accessible descriptions of how both ... full review

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"A worth addition to any data scientists reading list" - by Peadar Coyle
Clearly written book in a genre that has plenty of alternatives.
* Excellent introduction to data structures throughout
* Useful practical considerations of the underlying algorithms
* Functional programming style which is unusual in this genre
* Lively style by Joel

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"Good textbook for data mining!" - by Mr SEAN REILLY
It's a great book for data mining - lots of pretty examples covering clustering, classification, and some other concepts too. There are some exercises after each chapter, but the answers are not included within this book. My course didn't go into too much detail about the topics covered in this book, but it was still a nice alternative to reading my university's lecture slides (which tend to lack some information). You will need to find a free PDF document online somewhere with the answers in it (not hard, just google it).

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"A soIid overview" - by Kindle convert
Clear and concise intro to the topic. Leaves you wanting more but sets you up well. Good value.

NB It’s 99 pages not 120 (the difference is taken up with a taster of Blockchain)

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"Mapping the interface between predictive analytics and business" - by Dr Ross Gayler (Melbourne, VIC Australia)
There are plenty of books available on predictive analytics (and data mining and big data - depending on your tolerance for hype), so why should you buy this one? (That's why you are reading this review. Right?)

This book is about predictive analytics as a tool to aid business (in the broadest sense). If that's not your interest this book is not for you. The point is that if predictive analytics is approached as an isolated technical exercise it won't deliver the expected business benefits. In order to be successful the analytics needs to be done with proper recognition of the business context and the business needs to be an informed consumer of analytics. You'd think that would be obvious, but project failures due to a poor interface between the business and the analytics are distressingly common – hence the need for a book like this.

The intended readership appears to be ... full review

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"Best book in astrostatistics nowadays" - by Gabriel
This is the best choice for learning modern statistical methods for advance undergraduate and graduate student,s not only for astrophysicists but for any physical science.

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"Superb easy to read book" - by Mr. Brian D Meehan (Leixlip, Kildare Ireland)
Superb easy to read book. Algorithms and rapid miner explained in straight forward fashion. Great book

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"Business Intelligence in Plain Language" - by Love Reading
This book de-mystifies business intelligence. It explains why you would want to use it and how to go about getting the best from it.

It takes you through all the stages and tells you what to look for and what questions to ask. It offers examples that show the benefit and is a great place for anyone contemplating business intelligence to start.

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"For the "person in the street", not "The Street"" - by P. Bell (Brighton, UK)
This won't become a set book for a University Finance course; its "feet on the ground", not "head in the clouds" stuff. Nor should it be your first book about trading. I feel it's for people who've traded for a year or so, probably with strategies they've bought and been dissatisfied with, and who now want to move on. Its about the process of building a strategy you can have confidence in - but you need to be able to provide your own ideas from which the process will start.

The first two thirds of the book are plainly written and easy to read - starting with the author's history of using other people's strategies and becoming dissatisfied with them; he's "been there, done that, now has better clothes and doesn't need the T-shirt". Its very common sense rather than theoretical, and I'd guess there are fewer than half a dozen ... full review