Agile Data Science: Building Data Analytics Applications with Hadoop Author: Russell Jurney | Language: English | ISBN:
B00IG2UU9U | Format: PDF
Agile Data Science: Building Data Analytics Applications with Hadoop Description
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
- File Size: 10125 KB
- Print Length: 178 pages
- Publisher: O'Reilly Media; 1 edition (October 15, 2013)
- Sold by: Amazon Digital Services, Inc.
- Language: English
- ASIN: B00IG2UU9U
- Text-to-Speech: Enabled
X-Ray:
- Lending: Not Enabled
- Amazon Best Sellers Rank: #175,839 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
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Book review - Agile Data Science by Russell Jurney, O'Reilly Media
The subtitle "Building Data Analytics Applications with Hadoop" of this book says more about the book than the actual title "Agile Data Science". However the subtitle will probably fool most people. Before reading this book I believed that Hadoop with the the distributed file-system HDFS. If you are looking for a book about building applications on the of HDFS then this book IS NOT for you. It turns out that Hadoop is much more than just HDFS.
Do not buy this book for learning about agile software development methodologies. There are some rather strange comments about personal and private space requirement for creative workers as well as mentioning of "Easy access to large-format printing is a requirement for the agile environment." The discussion about agile methods for working with data science is interesting. The basic question is if it is possible to bridge agile methods and data science since science in it's nature does not consists of a predefined set of tasks. It seems to me that the tools and software used in chapter 3 are called agile an hence is the process agile. In part II of the book the application build is chapter 3 is refined in a number of steps that the author calls iterative. But again, that does not make the process agile. I am not saying that the author is wrong but the point about the agile method and how process and tools interact to make the development agile is not entirely clear to me.
This is NOT a book about the inner workings of Hadoop. Please refer to "Hadoop: The Definitive Guide" by Tom White for O'Reilly Media for a thorough introduction to Hadoop.
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