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Galit Shmueli. Register a free business account. Tell the Publisher! I'd like to read this book on Kindle Don't have a Kindle? Customer reviews.
How does Amazon calculate star ratings? The model takes into account factors including the age of a rating, whether the ratings are from verified purchasers, and factors that establish reviewer trustworthiness. Top Reviews Most recent Top Reviews. There was a problem filtering reviews right now. Please try again later. Verified Purchase. While this book does try to engage the learner by offering examples using storytelling, it fails on an instructional level.
It doesn't fully explain the when or why behind the theory or application of data mining, and each aspect of it. It includes many equations that it just expects people to know. If you do know know orthogonal equations or linear regression by heart, this book will throw you for a loop. I am taking an online class using this book, and I am ready to tear my hair out. Right now I am searching for books online to supplement my learning.
If I find a better book, I will definitely add it to my post. As I have shared with the lead author Dr. Galit Shumeli: I believe this book is outstanding, well-written and full of intuitive advice based on strong mathematics.
I like many of the one-liners I highlighted throughout the book on how to think about data mining. In the past, I had been able to recommend a number of statistically-based books, which examine the algorithms. I recommend this book which has so many contributors too beyond the coauthors as a valuable resource to people I know who want to provide more statistical depth in their application of SQL Server Data Mining.
I believe this book could be used by undergraduates. The consistent business focus provides many ideas on how to think about data mining value. I enjoyed not just seeing familiar equations, but also seeing the common words and phrases from statistics being used to describe the data mining process. And I'm happy to have the early chapters talk about what to do for data mining, and not just splash a CRISP-DM model and assume people would know what to do.
In my data mining presentations, I even take another step back and ask people in audiences about the scientific method. I liked the data visualization chapter since I believe the software interfaces should and will grow in that area in coming years. With the extension for this second edition into time series, this book covers most the algorithms from SQL Server Data Mining. I am aware that one particular Amazon reviewer "Lew" was seriously disappointed both at the cost of the book, and more specifically that it did not address his predetermined favorite program R.
People who are professionals in this area need to consider all software available, both open source and commercial, and not have a predetermined "free is best" philosophy. I have expertise both with SAS and Microsoft Business Intelligence, and as a consulting professional, I choose an obligation to my clients to be aware of various technologies. XLMiner is the choice for this book's authors, and though the book examples came from this technology, I believe the introductions and statistical explanations were vendor-neutral.
I also responded to Amazon reviewer "Jessica Jean" about the need for professional guidance. I am sad for her that in her online class she does not have sufficient professional leadership to guide her into better mathematics. People who read only one vendor's documentation whether it be from Microsoft or SAS or R fans will only get only one set of perspectives logically tailored to that technology's implementation.
Serious professionals seek good guidance across technologies and also seek people like myself who provide both formal training and onsite guidance. There are many books beyond just this one resource, and there are communities and conferences for people interested in building one another. Data mining is an area of active research and active practice development, and no one book or article can encompass the newest and best applications either from the technology perspective or from the machine learning algorithm direction.
Given the emerging nature of data mining, I have therefore provided many links on my data mining website find the site in a search engine. This textbook is a good introduction to data mining, and provides comprehensive information without being a several-volume tome or being pages. Especially business analysts would find the book practical. Computer science majors sometimes take the topic at a more aggressive pace, delving either deep on the statistics or the algorithms implementation or both.
I believe those directions are important for people expecting "machine learning". By contrast, this book focuses on having practical datasets and applying them for specific problems.
People can access the datasets from the authors' website and use not only XLMiner but any other data mining technology which may already be licensed and onsite. If I had a critique really a request for more it would have been great to see this book in color like Hastie. Shmueli agrees with me and she said the authors worked hard on the graphical design of the content: I believe their effort shows.
She also mentioned that the instructor slides are in color, and I applaud their choice to include color. In practice, we will be seeing more people experimenting with interactive visual analytics in color. Pretty shallow presentation of very basic statistical algorithms. Algorithms presented without any explanation of mathematical backgrounds and assumptions when they can be used. Almost no formulas in text, just data and plots.
These algorithms are standard statistical algorithms; it is not clear from the text whether "data mining" and "statistics" is the same or not. What is worse, there is Excel library that must be used with the book. All examples are in the context of this library. Or spend few thousand bucks for full license, what taking into account the "sophistication" of the library would be wasting of money. Take R language that is free and infinitely more powerful.
Warning if you purchase used book: each copy of the book has unique license ID that must be used to download the software. Once downloaded, this software cannot be downoaded second time. This means that it is not possible to download software if you purchased used book. Data mining is the extraction of useful information from large amounts of data. Perhaps the best example of this is Amazon. If you go to Amazon to look at a book, you'll find such tidbits of information as a section on the page headlined 'Customers who bought this item also bought' and another 'What do customers ultimately buy after viewing this item?
Then they went on to take these other actions. Among all the data that Amazon has collected they mine their database and pull out information to fill in these blocks. This book, intended for MBA level students gives an excellent introduction to data mining. It further includes access to an Excel add-in called XLMiner that is specifically set up to allow the student to use Excel to learn how data mining is done.
The one thing I would ask the authors to do in their next edition is to provide a brief review of the commercially available data mining software products that are available. If not all of the software, perhaps just the top half dozen or so.
In real life we aren't going to use Excel for data mining, our data resides in a database somewhere. See all reviews from the United States. Top international reviews. Excellent to give you the fundamentals of predictive analytics. Thank you for your feedback. Sorry, we failed to record your vote. Please try again. Good book in good condition. One person found this helpful. Good Book. But where do you find the example data sets this book is referring Excel miner is a disappointment!
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