Agile Data Science: Building Data Analytics Applications with Hadoop by Russell JurneyMy rating: 4 of 5 stars
One of the problems with data science is that any description of what is encountered takes on the appearance of a mythical unicorn, noone person could possibly have all of the skills required. And it gets worse when you add to the standard set of statistics, domain knowledge, and programming the ability to deploy the application into a high speed environment. This book is not going to make a data scientist an expert in running a data center, but it is useful to give someone who has the rest of the skills an understanding of the environment their work will be deployed into.
One of the conflicts between the data scientist/analyst and information technology groups is that while the data scientist gives the data owned by the organization its value, IT is charged with storing the data and providing the access. And in a high velocity, high volume environment of big data, not understanding how the architecture works can lead to the data scientist creating valid solutions that cannot be applied in the actual day to day working environment. That is where this book comes in. The book has associated virtual machines in software repository so that the data scientist who does not know anything about infrastructure and the software stack that the data and the analysis rides on can see how everything fits together.
The book title is misleading. This is not a book about data analytics. This is a book for data analysts so they know how their analytical application is deployed and applied to day-to-day use in enterprise environments. For that reason it is useful.
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Disclaimer: I received a free electronic copy of Agile Data Science as part of the Oreilly Press Blogger program.
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