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Product details
File Size: 50980 KB
Print Length: 480 pages
Simultaneous Device Usage: Up to 5 simultaneous devices, per publisher limits
Publisher: Pearson FT Press; 1 edition (May 2, 2015)
Publication Date: May 2, 2015
Sold by: Amazon Digital Services LLC
Language: English
ASIN: B00XANZZ4A
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Amazon Best Sellers Rank:
#595,773 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
The organization of the book is confusing. You can't use the table of contents to decide what to read. After reading the first 4 chapters, I'm extremely disappointed.
Very good book, well written, and the best pas, as with all of Miller's books that I have purchased, is that it comes with real code examples in both Python and R. Great way to get up and running.
good book, but....no data sets to work with. Seems critical for a source code heavy book (ie almost every chapter has pages of code). We would prefer not to scan, then try to run the code ourselves. Read the appendix first at that seems to be where the theory is then go back to the chapters for practical work. Borrowed this book from the library....its really expensive otherwiseupdate: OK --kept reading, and paying overdue fines at the library, so I bought the book. Really worth the read if you're serious about focusing on marketing data science. Great starting read for technical marketers who want to do something in this field.Glad the code samples are now available. Will have to test and see. One thing I noticed about the content. Every time something got interesting Prof. Miller would quote a reference for further reading (ie. details). That's sort of OK, but leaves me wanting and having to go dig elsewhere. Suggestion: one more paragraph for such situations would put my curiosity at rest. A lot of content around product development, positioning, recommending, but a little light on broader examples - It might be helpful to describe a broader range of techniques (ie. list them), then drill down on one or two. It just seems too narrow, like drinking from a straw when really a funnel is needed with the huge alternatives. Enjoyed the book (looks like a text book but reads like a novel - that's a good thing)
If you want to have just one book on Marketing Data Science, this is the one.
Updated my rating from 2 to 5 stars as the code has become available on FTPress. I received an email from the publisher last week. Not sure why it took over 6 months for them to post this.
Good book, very well explained examples (the R and the Python codes are very well written) but if you have read other books from Prof. Miller, you would be able to remember some exacts paragraphs across some books.
Now that the data is available I will go through this book and do a proper review. But in general I do not like this book as much as this one R for Marketing Research and Analytics (Use R!)
There is a lot of good information in this book, but I find it poorly organized and presented.It's hard to figure out the best audience for this book, and I think when you imagine it as a textbook in a specific degree program, where the instructor would know exactly the prerequisite classes taken by the students coming into the class, then it starts to make sense. Ideas toward the front of the book are not properly introduced and code is thrown at the reader without much in the way of explanation. In the context of a class, the instructor could smooth out these rough edges and likely deliver a coherent educational experience. But as a standalone learning experience, this is rough going.(I'm sure it's relevant what my own background is, which is completely comfortable with predictive analytics and programming, but ignorant of the specific way that "marketing" folks look at the world.)I find the book very strangely organized in that Appendix A, "Data Science Methods", is, well, relegated to an appendix, rather than being a central part of the flow of the book. It's a really good chapter!There is some tremendously useful code presented in this book, but again, the way that it's presented will make it of limited utility to those who do not have a guide in the form of a professor teaching a class or who do not have sufficient background to ramp up to the ideas presented.I am also not fond of the production qualities of the book, e.g:1. The paper is overly thin, so you can see the text on the other side of the page, which just makes reading harder to my mind.2. The code is presented with this horrible gray background. Black text on a gray background (I dunno, maybe 30% gray) is not good contrast. So the code ends up being unnecessarily difficult to read.3. That this was created using LaTeX is obvious to me. Not because the typesetting is problematic, but because of the front matter, with the separate listings of Figures, Tables, and Exhibits. LaTeX makes producing these listings easy, but just because you can produce them doesn't mean you should. They feel anachronistic and strange.The fact that all the code and underlying data is available on the web is amazing, so for some folks, the difficult presentation will be worth slogging through to see the examples. But I fear that many would-be readers will just give up because the path is so rough.
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