Reviews

On Jun 3 Marco Rapella wrote:
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Rating: 4.0

On May 5 Kiatikun Luangkesorn wrote:
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. Full Review  >

Rating: 4.0

On Apr 5 Sumit Bisht wrote:
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Rating: 3.0

On Feb 5 Carsten Jorgensen wrote: Chapter 3 alone is worth the price of the entire book.
The main value of this book is definitely chapter 3 where Jurney show us how to go from zero to a working data science application. The application is literally build from ground up starting with data collection over storing data to build a web front-end. Full Review  >

Rating: 4.0

Reviews

On Apr 15 Daniel Molnar wrote: You could say I'm a sucker for bashing, but hell no, was I just curious.
Agile is something of an overused buzzword now and it seems to be the case now. Agile here equals with just installing a dozen frameworks after each other and skim another programming or markup language in a row spiced with some algos and stats. You gonna be exposed to SQL, NoSQL, Python, Avro, Pig, Flask, Bootstrap, Jinja, D3.js, nvd3.js, MongoDB, ElasticSearch, GitHub, dotCloud, S3, Elastic Mapreduce, Google Analytics, Mortar Data, ElasticSearch, Wonderdog, TF-IDF and Naive Bayes, all in 178 pages. Thinking positively this is the quickest possible intro for a workflow with all typical phases, although weapons of choice seem arbitrary, never a why, never a pro or a con nor any alternatives. If you're lazy to understand the landscape and want a pre-cooked menu that you can cook, but can't change the recipes, go for it. Full Review  >

Rating: 2.0