Programming Books C Java PHP Python Learn more Browse Programming Books
Qty:1
  • List Price: $49.99
  • Save: $5.06 (10%)
In Stock.
Ships from and sold by Amazon.com.
Gift-wrap available.
Practical Data Science wi... has been added to your Cart
+ $3.99 shipping
Used: Good | Details
Sold by -Daily Deals-
Condition: Used: Good
Comment: This Book is in Good Condition. Used Copy With Light Amount of Wear. 100% Guaranteed.
Access codes and supplements are not guaranteed with used items.
Sell yours for a Gift Card
We'll buy it for $15.49
Learn More
Trade in now
Have one to sell? Sell on Amazon
Flip to back Flip to front
Listen Playing... Paused   You're listening to a sample of the Audible audio edition.
Learn more
See all 2 images

Practical Data Science with R Paperback – April 13, 2014

ISBN-13: 978-1617291562 ISBN-10: 1617291560 Edition: 1st

Buy New
Price: $44.93
36 New from $27.04 14 Used from $28.47
Amazon Price New from Used from
Paperback
"Please retry"
$44.93
$27.04 $28.48
Free Two-Day Shipping for College Students with Amazon Student Free%20Two-Day%20Shipping%20for%20College%20Students%20with%20Amazon%20Student


Hero Quick Promo
Save up to 90% on Textbooks
Rent textbooks, buy textbooks, or get up to 80% back when you sell us your books. Shop Now
$44.93 FREE Shipping. In Stock. Ships from and sold by Amazon.com. Gift-wrap available.

Frequently Bought Together

Practical Data Science with R + R for Everyone: Advanced Analytics and Graphics (Addison-Wesley Data & Analytics Series) + The Art of R Programming: A Tour of Statistical Software Design
Price for all three: $98.73

Buy the selected items together

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.

NO_CONTENT_IN_FEATURE

Shop the new tech.book(store)
New! Introducing the tech.book(store), a hub for Software Developers and Architects, Networking Administrators, TPMs, and other technology professionals to find highly-rated and highly-relevant career resources. Shop books on programming and big data, or read this week's blog posts by authors and thought-leaders in the tech industry. > Shop now

Product Details

  • Paperback: 416 pages
  • Publisher: Manning Publications; 1 edition (April 13, 2014)
  • Language: English
  • ISBN-10: 1617291560
  • ISBN-13: 978-1617291562
  • Product Dimensions: 9.2 x 7.4 x 0.8 inches
  • Shipping Weight: 1.6 pounds (View shipping rates and policies)
  • Average Customer Review: 4.4 out of 5 stars  See all reviews (22 customer reviews)
  • Amazon Best Sellers Rank: #33,912 in Books (See Top 100 in Books)
  •  Would you like to update product info, give feedback on images, or tell us about a lower price?


More About the Authors

Discover books, learn about writers, read author blogs, and more.

Customer Reviews

4.4 out of 5 stars
5 star
50%
4 star
41%
3 star
9%
2 star
0%
1 star
0%
See all 22 customer reviews
Thankfully, this book is a welcome bridge.
David M. Steier
Overall, Practical Data Science with R is a great book, with nice toolset of tips, examples and methods for any data analyst (and scientists) in general.
Paulo Nuin
I highly recommend this book to beginner and intermediate practitioners.
Giovanni Seni

Most Helpful Customer Reviews

52 of 59 people found the following review helpful By Dimitri Shvorob on April 20, 2014
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.
6 Comments Was this review helpful to you? Yes No Sending feedback...
Thank you for your feedback. If this review is inappropriate, please let us know.
Sorry, we failed to record your vote. Please try again
23 of 24 people found the following review helpful By Christopher G. Loverich on April 24, 2014
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.
Read more ›
5 Comments Was this review helpful to you? Yes No Sending feedback...
Thank you for your feedback. If this review is inappropriate, please let us know.
Sorry, we failed to record your vote. Please try again
31 of 35 people found the following review helpful By Scott C. Locklin VINE VOICE on May 11, 2014
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.
Read more ›
12 Comments Was this review helpful to you? Yes No Sending feedback...
Thank you for your feedback. If this review is inappropriate, please let us know.
Sorry, we failed to record your vote. Please try again

Most Recent Customer Reviews

Set up an Amazon Giveaway

Amazon Giveaway allows you to run promotional giveaways in order to create buzz, reward your audience, and attract new followers and customers. Learn more
Practical Data Science with R
This item: Practical Data Science with R
Price: $49.99 $44.93
Ships from and sold by Amazon.com