- 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: 184 customer reviews
- Amazon Best Sellers Rank: #8,325 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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|Data Science for Business||Data Science from Scratch||Doing Data Science||R for Data Science||Data Science at the Command Line||Python Data Science Handbook|
|What You Need to Know about Data Mining and Data-Analytic Thinking||First Principles with Python||Straight Talk from the Frontline||Visualize, Model, Transform, Tidy, and Import Data||Facing the Future with Time-Tested Tools||Tools and Techniques for Developers|
What you need to know about data mining and data-analytic thinking
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.
In the beginning we are shown the motivations for Data Science and what fields they apply to. Some examples include movie recommendations, credit card charges, telecom churn rate, and automated analysis of stock market news. The book avoids going into the highly technical parts of creating the system but gives you links for where to go.
They do not really reveal the whole Data Science stack. For example Hadoop was mentioned as an implementation of MapReduce but they said going into Hadoop configuration would be too detailed for this type of book. I tended to agree, and even being a progammer myself, I thought they made the right choice to leave that out.
Where the book shines is in the explanations. I am very familiar with expected value calculations and there was a chapter on this. It was a much better high level discussion than I have seen elsewhere, and they mentioned possible pitfalls of the expected value framework.
I liked that the emphasis was on deciding what problem to solve in Data Science. The title of the book is appropriate as it is not just about analyzing data, but figuring out the business case. If you are new to Data Science or looking to get a high level overview this book is an great place to start.
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!
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.