- Paperback: 389 pages
- Publisher: Manning; 1st edition (April 13, 2014)
- Language: English
- ISBN-10: 1617291560
- ISBN-13: 978-1617291562
- Product Dimensions: 7.5 x 1 x 9.5 inches
- Shipping Weight: 1.6 pounds (View shipping rates and policies)
- Average Customer Review: 35 customer reviews
- Amazon Best Sellers Rank: #104,189 in Books (See Top 100 in Books)
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Practical Data Science with R 1st Edition
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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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Top customer reviews
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The "Practical Data Science with R" book is the very practical guide book. It immediately sheds light on how to tackle industry data science problems. It equips us with a toolbox of very useful and well-established methodologies to solve problems. The authors help us get familiar with the forefronts of data science development in the current data science industry.
This book was written by authors providing data science consulting services to technology companies and start-ups. It is a must-read for anybody who plans to transit from school to industry.
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.
- 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.
- Good use of Appendices for introducing R syntax / installation, rather then stuffing it into one of the early chapters.
- Doesn't stick with data sets long enough. I went to the trouble of setting up a true database to use the first dataset (chapter 2); only to move on to a different data set in the very next chapter (book did eventually return to the data set).
- Feels a bit back and forth at times on whether it wants to be a truly pragmatic, focused work or a principles driven, broadly scoped book (thinking of chapters 5-7 here). Not necessarily a knock depending on what your looking for.
I've ready a few books on getting started in data analysis, R, statistics, etc. This book is solid enough that were I to choose among them, I'd recommend it first. I think if the book focused down on using data-sets for longer stretches, allowing you to learn the data well and apply multiple types of analyses on top of it (especially earlier on), it would be a bit more engaging.
Lastly, its has good coverage of R principles but (per its scope) doesn't get into the nitty gritty. I'd recommend "The Art of R Programming" for that, which would be a good companion to this book (e.g. covers R but not Data Analysis). I've heard R in Action is good as well, though haven't read it. Caveat emptor.
Disclaimer: I received a e-copy of the book from Manning for review.