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Practical Data Science with R 1st Edition

4.3 out of 5 stars 29 customer reviews
ISBN-13: 978-1617291562
ISBN-10: 1617291560
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Editorial Reviews

About the Author

Nina Zumel co-founded Win-Vector, a data science consulting firm in San Francisco. She holds a PH.D. in robotics from Carnegie Mellon and was a content developer for EMC's Data Science and Big Data Analytics Training Course. Nina also contributes to the Win-Vector Blog, which covers topics in statistics, probability, computer science, mathematics and optimization.

John Mount co-founded Win-Vector, a data science consulting firm in San Francisco. He has a Ph.D. in computer science from Carnegie Mellon and over 15 years of applied experience in biotech research, online advertising, price optimization and finance. He contributes to the Win-Vector Blog, which covers topics in statistics, probability, computer science, mathematics and optimization.

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Product Details

  • Paperback: 389 pages
  • Publisher: Manning; 1st edition (April 13, 2014)
  • Language: English
  • ISBN-10: 1617291560
  • ISBN-13: 978-1617291562
  • Product Dimensions: 7.3 x 1 x 9.1 inches
  • Shipping Weight: 1.6 pounds (View shipping rates and policies)
  • Average Customer Review: 4.3 out of 5 stars  See all reviews (29 customer reviews)
  • Amazon Best Sellers Rank: #60,033 in Books (See Top 100 in Books)

Customer Reviews

Top Customer Reviews

Format: Paperback
A problem with the other reviews is that they consider the book in isolation, as if no alternatives were available. "Practical data science" is not the only machine-learning-lite book on the market: Manning itself had published Harrington's Python-based "Machine learning in action", Packt offers "Machine learning with R" by Lantz, O'Reilly boasts "Doing data science" by Schutt and O'Neil, and, finally, Springer has "Introduction to statistical learning" by James, Witten, Hastie and Tibshirani. I have seen and reviewed all except Harrington's; for the purposes of this review, I'll ultra-briefly describe each contender ("Machine learning with R" - thin, average-quality, superficial, but effective at what it sets out to achieve; "Doing data science" - a mash-up of a textbook and a magazine article about kewl data scientists; below-average quality, but a lot of pop appeal; "Introduction to statistical learning" - high-quality, accessible and visually appealing textbook with R illustrations) and get to "Practical data science" - which, to me, comes across as a better-organized, earnest version of "Doing data science". The book's forte is its effort to go beyond a catalogue of R-illustrated machine-learning methods - and you have to have seen similar books to know how standard this repertoire is - and discuss practical skills useful to a budding "data scientist", from version control to presenting. I appreciate this effort, but feel that this content was not sufficiently substantial or polished to develop into a "unique selling proposition" of the kind that each of its competitors has - hence the title of my review.
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Format: Paperback
tl;dr: A well rounded, occasionally high-level introductory text that will leave you feeling prepared to participate in the Data Science conversation at work, from earliest planning to presentation and maintenance.

Details:

Was excited to see this book coming to publication. I'm a fan of practical, non-academic approaches to subjects and prefer working from concrete examples to abstract principles (rather than the other way around). I think this is both the most difficult and most needed type of resources that can be put into print. This book handles the task ok; it falls a bit short on practical, concrete, use cases as it alternates between working with hands on datasets and shotgun coverage of principles and techniques at a higher level. I'd have much preferred sticking with single data-sets for longer (say, a couple chapters per data set), but didn't feel cheated out of hands on work.

Pros:
- Easy access to the datasets via Github; good documentation on where to find others
- Key Takeaways provided at end of chapter are good summaries of overall information provided.
- A good focus on not just data analysis, but the process as a whole; very Agile like, practical, and non-dogmatic.
- Battle tested advice: You can tell some of the advice comes from hard-fought battles - ex: Why not use the sample() function instead of manually creating a sample column? Because with a sample column, you can repeatably sample the same data (e.g. all columns < 2) for repeatable output and for regression testing (avoiding introducing bugs).
- Builds your analyst vocabulary, increasing your all-important google-fu skills. Not knowing what to Google is, imho, the single hardest problem when learning a new set of problems / api's.
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Format: Paperback
I've had to hire recent graduates with degrees in machine learning, operations research and even "data science." One of the problems with such people: they don't know anything practical. They probably know the basics of regression and some classification routines, as learned in their coursework. They've probably worked on one or many data science like problems, using machine learning techniques or regression or what not. Many of them have never done a SQL query, or done the dirty business of data cleaning which takes up most of the data scientist's time. They'll always have gaps in their education; maybe they wrote a dissertation on an application of trees or deep learning, and have never used any of the other myriad tools available to the data scientist. None of them have ever done data science for money, and so none of them know about practical things like git or what the process looks like in an industrial setting. It is for these people that this book appears to be written. In an ideal world, all larval data scientists would be taught a course based on this book, or at least go through it themselves. It is also useful to experienced practitioners, as it covers many things, and can be a good practical reference to keep around. The book is ordered as a data science project would be ordered, from start to finish; so, as you proceed down an engagement, reviewing the chapters in order will be helpful.

Ch1 describes the job of the data scientist, the workflow, and the characters you run into on a project.
Ch2 outlines some of the tools used to get at the data, including the authors tool, "SQL Screwdriver.
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