Paperback: 888 pages
Publisher: Wiley; 3 edition (April 12, 2011)
Product Dimensions: 7.4 x 1.9 x 9.3 inches
Shipping Weight: 2.9 pounds (View shipping rates and policies)
Average Customer Review: 4.2 out of 5 stars See all reviews (27 customer reviews)
Best Sellers Rank: #236,176 in Books (See Top 100 in Books) #55 in Books > Computers & Technology > Databases & Big Data > Data Warehousing #118 in Books > Computers & Technology > Databases & Big Data > Data Mining #688 in Books > Business & Money > Marketing & Sales > Sales & Selling
In a field evolving as dynamically as data science, 2011 seems a long time ago, and I've since bought a number of the newer titles out there. Still, however, I often find myself reverting to Linoff and Barry's text for a lucid explanation of, or interesting take on a particular data mining subject area.The book is thorough (at 800+ pages this should be the expectation) and technical, but isn't really a how-to manual in that it stops short of containing actual code or instructions. That's not an issue, however, as such instructional information is available elsewhere if needed.My only complaint about the work is that it is a little redundant and otherwise verbose at times. I hope a fourth edition is forthcoming, and that it is a little more tightly edited.---Z. KhandwalaInstitute for Advanced AnalyticsBellarmine University - Louisville, KY
This book has useful nuggets but one needs to be patient to weed through ill-structured content.Problem1: Examples and content repeats quite a bit across chapters, but unfortunately never discusses things properly at one place. In every edition authors have added chapters but seemed to have forgotten what they have already discussed in earlier chapters.Problem2: Many suggestions, scenarios have been incompletely discussed. Without enough information one has to assume quite a bit about the scenario, problem, solution and the value of it. It is okay if it had happened once in a while, but this sprinkling of anecdotes without fully discussing is rampant in this book.Problem3: It is quite verbose.Problem4: Keeps on changing the depth of the discussion. The discussion is overall at high-level, however at times authors would go really deep to discuss details around some random topic eg calculation of silhouette scores. The primary focus seemed to be business people and not statistics students. Going deep "selectively" is also a big problem in this book.This book has the potential to become a really good book, but it needs major restructuring.
I got this book for a class on Data-Mining and I found it to be a very good book. It has good visuals to help the reader understand the concepts in the book and maintains a good sense of humor throughout so reading it doesn't seem as dense as some of my typical statistics books. My only criticism of the book would be that it never discusses common software platforms for performing these tasks. While I understand that he probably didn't want to favor a particular platform over another, it seems that introducing the major ones could be helpful for people that may be very used to using just one.
I haven't made it through the entire book, but this serves as a solid reference for different topics in data mining. I used it in a graduate level course I took this spring and it was easy to read and understand.
I taught myself data mining using this book. I also was in my MBA Decision Science class the next year and they used the exact same book, but I had already read the book front to back. I aced that class. The book is comprehensive and allows people to grasp all the basic concepts of data mining.
This is not a book for a beginner. It really should be used for somebody who already uses a lot of data and is comfortable working with various programs. It's good to advance your knowledge but not begin it.
I have read a couple of books about data science. Reading this one is most enjoyable. I cannot put it down. I found a lot of useful information from examples in different industries. Highly recommend. I do have years of hands on experience on data mining.
Similar to Excel 15 years ago, Data Mining Techniques are the new required skill set for business professionals. Learning techniques from a professionals Gordon Linoff and Michael Berry provides an excellent foundation
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