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Russell Jurney
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Agile Data Science: Building Data Analytics Applications with Hadoop ペーパーバック – 2013/11/5

英語版 Russell Jurney (著)
5つ星のうち3.2 13
すべての形式と版を表示

Mining big data requires a deep investment in people and time. How can you be sure you’re building the right models? With this hands-on book, you’ll learn a flexible toolset and methodology for building effective analytics applications with Hadoop.

Using lightweight tools such as Python, Apache Pig, and the D3.js library, your team will create an agile environment for exploring data, starting with an example application to mine your own email inboxes. You’ll learn an iterative approach that enables you to quickly change the kind of analysis you’re doing, depending on what the data is telling you. All example code in this book is available as working Heroku apps.

  • Create analytics applications by using the agile big data development methodology
  • Build value from your data in a series of agile sprints, using the data-value stack
  • Gain insight by using several data structures to extract multiple features from a single dataset
  • Visualize data with charts, and expose different aspects through interactive reports
  • Use historical data to predict the future, and translate predictions into action
  • Get feedback from users after each sprint to keep your project on track
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  1. 本の長さ
    178ページ
  2. 言語
    英語
  3. 出版社
    O'Reilly Media
  4. 発売日
    2013/11/5
  5. 寸法
    17.78 x 0.97 x 23.34 cm
  6. ISBN-10
    1449326269
  7. ISBN-13
    978-1449326265
  8. すべての詳細を表示
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商品の説明

著者について

Russell Jurney cut his data teeth in casino gaming, building web apps to analyze the performance of slot machines in the US and Mexico. After dabbling in entrepreneurship, interactive media and journalism, he moved to silicon valley to build analytics applications at scale at Ning and LinkedIn. He lives on the ocean in Pacifica, California with his wife Kate and two fuzzy dogs.

登録情報

  • 出版社 ‏ : ‎ O'Reilly Media; 第1版 (2013/11/5)
  • 発売日 ‏ : ‎ 2013/11/5
  • 言語 ‏ : ‎ 英語
  • ペーパーバック ‏ : ‎ 178ページ
  • ISBN-10 ‏ : ‎ 1449326269
  • ISBN-13 ‏ : ‎ 978-1449326265
  • 寸法 ‏ : ‎ 17.78 x 0.97 x 23.34 cm
  • カスタマーレビュー:
    5つ星のうち3.2 13

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Kez
5つ星のうち2.0 erm short and expensive
2014年3月8日に英国でレビュー済み
Amazonで購入
Please note, this is a practical reference, little on the bigger picture of big data. The 178 pages listed includes the index and probably 3/4 of 158 pages of real book are code listings and screen shots so being generous that's 40 pages of proper scribblings. I suspect the author wrote this within a day or two. I have read the entire book in one sitting, just a couple of hours.

My reason for the poor review is not reflective of the content, just the disappointment in that it's only really a booklet and that you're paying 50p per page.
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Kev
5つ星のうち2.0 Frustrating but beneficial.
2014年10月24日にアメリカ合衆国でレビュー済み
Amazonで購入
This book is terribly frustrating. I have a lot of experience in statistical analysis, machine learning, and programming and picked up this book to learn how to apply these skills with "Agile Data Science." This mostly means implementing these skills with the tools of AWS, Hadoop, and hosting data products on scalable platforms.

The book does a great job of summarizing the agility needed and the tools used, but the code to implement these tools is lacking. I expected the code in the book to be outdated even though the book was only published a year ago. There is a github repository for the code but it is incomplete.

One benefit of the code not working or the instructions being vague is having to debug it yourself or search for solutions. This is a great learning tool. I don't think this is a benefit the author would like and should put some more time in the second iteration of making the instructions clearer. Since the book has only 164 pages there is significant room for growth.
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Dorgival Guedes
5つ星のうち4.0 A very interesting introduction to the area, with a complete use case scenario.
2015年3月29日にアメリカ合衆国でレビュー済み
Amazonで購入
I found this short book interesting to read and it seemed to me a good way to introduce someone to hadoop and data analysis using it. I have handed it to a few students working with me in research projects and they seem to like it as a quick way to get a general view of the field, with the description of a complete application to a clearly defined problem.
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chad
5つ星のうち5.0 Absolute required reading for all new data scientists
2014年1月3日にアメリカ合衆国でレビュー済み
Amazonで購入
I was once told by a chief data scientist that they would rather teach a mathematician programming than a programmer math (to be a data scientist). After being a data scientist for some time now I would have to respectfully disagree. 85% of data science is plumbing and I wouldn't hire a physicist to be a plumber. Indeed modern data scientists really do need to be full-stack developers trapped in an academic's body.
Jurney nails it! He offers tools and methodologies adapted to common data science workflows and their associated pitfalls wherein we spend 85% of our time plumbing and 15% of our time integrating some off-the-shelf algorithm to find deep insight.
So, for new data scientists or 3rd-4th year grad students who have balanced their Twitter API hack with NSF grant deadlines, this is ABSOLUTELY REQUIRED READING.
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SF
5つ星のうち1.0 He sent this with NON-WORKING CODE
2014年6月9日にアメリカ合衆国でレビュー済み
Amazonで購入
The story is nice, but the code that forms the basis of the entire project behind the book DOESN'T COMPILE. The author has - as of today (June 9, 2014) - removed all of the github references to the project.

I"m half way through the book, have been practicing Agile development techniques for several years, and I am not quite sure what in particular makes this book about Data Science 'Agile' based.

One thing that he does nicely is explain the Pig code he uses, but I can't use those programs because the Python programs that gather the data that feed Pig will not compile, even after I de-bugged his code for several hours. (Example: the author made reference to an RFC inline in the Python code that would have NEVER compiled. NEVER. Line 11 gmail.py from call to email utilitiies)
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