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Agile Data Science 2.0: Building Full-Stack Data Analytics Applications with Spark

3.68  ·  Rating details ·  28 ratings  ·  6 reviews
Data science teams looking to turn research into useful analytics applications require not only the right tools, but also the right approach if they're to succeed. With the revised second edition of this hands-on guide, up-and-coming data scientists will learn how to use the Agile Data Science development methodology to build data applications with Python, Apache Spark, Ka ...more
Paperback, 352 pages
Published June 23rd 2017 by O'Reilly Media
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3.68  · 
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 ·  28 ratings  ·  6 reviews


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Netoasis
May 08, 2018 rated it it was ok
It's a good book for programmer or data science beginner level to know the data science concept and popular tool-set in the industry.
However the book is not up-to-date to keep with the latest software version. Thus some of the codes in the book are not working as expected(Too bad..). So it means the quality of the book is under the standard.
For example, it's using pyElasticsearch package however it's only support ElasticSearch (<2.0) version.
It's an ok book and needs more polished in my opin
...more
Kristian Edlund
May 19, 2018 rated it it was ok
I really wanted to love this book. The concept of walking through a quite elaborate example is excellent. I think it shows many of the pit falls and iterations you need to do from start to finished data product.

However, as one of the other reviews also points out, the code example in there are unfinished and the instructions are hard to follow. For me it started in chapter 4, where you are asked to run the first piece of code to convert data. However, I wasn't sure where to run it and it took m
...more
Rebecca Bilbro
Mar 07, 2018 rated it really liked it
Favorite quotes:
- "In data science, by contrast to software engineering, code shouldn't always be good; it should be eventually good."
- "In Agile Data Science, we value generalists over specialists...Examples of good Agile Data Science team members include: Designers who deliver working CSS; Web developers who build entire applications and understand the user interface and user experience; Data scientists capable of both research and building web services and applications; Researchers who check
...more
Alex Galea
Apr 09, 2019 rated it really liked it
This book describes Russell's perspectives on good data science workflow using an agile methodology. He walks through a project about airline flight data in great detail and shows off some really neat tricks for building web apps and doing predictive analytics at scale. I would describe the material at intermediate level, where the reader should already be familiar with the data science ecosystem.

I loved chapter 2, which introduces the technology stack. It's awesome to see minimal working snippe
...more
Jose Manuel
Dec 20, 2017 rated it it was amazing
impresionante. Pese a ser R mi opción principal y este libro usar Python, su enfoque , centrándose en la parte "científica" de la labor del Data Scientist es claramente acertada. Los primeros capítulos describen mi día a día de forma tan acertada que me ha llegado a emocionar. Su enfoque de mantener las cosas tan sencillas y escalables como sea posible centrándonos en las personas más que en los procesos, liberando resultados de manera rápida y continuada a lo largo del proceso, son consejos que ...more
Vaidas
Aug 24, 2018 rated it it was amazing
Interesting ideas and quite detail explanation of implementation.
I read this mainly for the description of the process and for hints how one might actually go about implementing all the steps. Book is clear on these points and therefore 5 stars. If one actually went on and ran the code probably something might not really work - but that's software :)
I am actually into these ideas and will do my best to get DS process at my current employer as close as is practical to this.
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