October 2013
Beginner to intermediate
175 pages
3h 53m
English
Content preview from Agile Data ScienceBecome an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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Chapter 1. Theory
We are uncovering better ways of developing software by doing it and helping others do it. Through this work we have come to value:
Individuals and interactions over processes and tools Working software over comprehensive documentation Customer collaboration over contract negotiation Responding to change over following a plan That is, while there is value in the items on the right, we value the items on the left more.
—The Agile Manifesto
Agile Big Data
Agile Big Data is a development methodology that copes with the unpredictable realities of creating analytics applications from data at scale. It is a guide for operating the Hadoop data refinery to harness the power of big data.
Warehouse-scale computing has given us enormous storage and compute resources to solve new kinds of problems involving storing and processing unprecedented amounts of data. There is great interest in bringing new tools to bear on formerly intractable problems, to derive entirely new products from raw data, to refine raw data into profitable insight, and to productize and productionize insight in new kinds of analytics applications. These tools are processor cores and disk spindles, paired with visualization, statistics, and machine learning. This is data science.
At the same time, during the last 20 years, the World Wide Web has emerged as the dominant medium for information exchange. During this time, software engineering has been transformed by the “agile” revolution in how applications are conceived, ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.