- Paperback: 414 pages
- Publisher: O'Reilly Media; 1 edition (August 19, 2013)
- Language: English
- ISBN-10: 1449361323
- ISBN-13: 978-1449361327
- Product Dimensions: 7 x 0.9 x 9.2 inches
- Shipping Weight: 1.7 pounds (View shipping rates and policies)
- Average Customer Review: 183 customer reviews
- Amazon Best Sellers Rank: #3,903 in Books (See Top 100 in Books)
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Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking 1st Edition
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Top customer reviews
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What I mean might become clearer if I point out what this book is *not*:
- It is *not* a computer science textbook with a focus on theoretical derivations and algorithms.
- It is *not* a "cookbook" that provides "step-by-step" guidance with little to no explanation of what one is doing.
- It is *not* your standard "management" title on the cool tech du jour available at airport stands and meant to be read in one sitting (buzzwords, hype and overly enthusiastic statements making up for the dearth of actual content).
Instead, it is close to being the perfect guide for the intelligent reader who -- regardless of whether s/he has a tech background -- has a sincere desire to learn how the tools and principles of data science can be used to extract meaningful information from huge datasets. Highly recommended.
Rather than reading this you're probably better off reading a book about how business might be impacted by machine learning and related things (The Second Machine Age or Average is Over). Alternatively, if you want to know more about data science / data mining (now fairly deprecated term this book uses) or machine learning you'd be better off picking up Hastie's or Mitchell's book or taking Andrew Ng's course on Coursera.
I've this far only read two chapters. My pattern-recognition ;) this far however, with an assessment that this will be applicable to the rest of the book is two-fold:
1) Too verbose!
Too much stuff on explaining the structure and purpose of the book. Could've been said way more succinctly, and therefore more clearly. The effect is that I start skimming.
2) Not 'sharp' enough.
The best non-fiction written for non-expert manages to reduce the complex into explaining the essence. Not making it simpler, and reducing crucial comprehension. But reducing the complex into its crucial essence.
When going over different types of tasks; classification, regression, similarity matching, clustering, co-occurence grouping - the way they are described, there is essentially no difference between i.e. clustering, similarity matching and clustering; they're all classifications - yes, there is a difference between regression.
In order for this to be truly helpful even for an absolute layman as myself, it needs to add enough crucial, essential distinctions to make the categories mutually exclusive. I can think about it, I can look it up. The book would however been better if the information was more 'sharply' communicated.
So why 4-star?
Because it is a beautiful balance for the amateur. Explaining basic concepts instead of trendy-applications.
For future versions though, correcting for verbosity and greater specificity (essence) will make it a true winner.
Provost and Fawcett is THE text if you want to learn advanced statistical methods in business-related problem-solving contexts separate from any specific programming language like R. It’s also the right choice if you want to understand data science from a strategic perspective and its process characteristics. Provost and Fawcett is extremely useful for anyone who is trying to get up to speed and demonstrate knowledge in business analytics or data science in relatively short manner. This text is extremely well written—the authors use non-technical language for the most part—and it’s interesting!