Big Data Analysis in the Clouds
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Big Data Analysis in the Clouds
Scientific, economic, social, political and cultural issues
Curated by Pierre Levy
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38 Seminal Articles Every Data Scientist Should Read

38 Seminal Articles Every Data Scientist Should Read | Big Data Analysis in the Clouds | Scoop.it
Here is selection containing both external and internal papers, focusing on various technical aspects of data science and big data. Feel free to add your favor…
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Big Data Now: Current Perspectives from O'Reilly Radar

Big Data Now: Current Perspectives from O'Reilly Radar | Big Data Analysis in the Clouds | Scoop.it
This collection represents the full spectrum of data-related content we’ve published on O’Reilly Radar over the last year. Mike Loukides kicked things off in June 2010 with “What is data science?
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Jacek Bugajski's curator insight, April 16, 2013 11:53 PM

O'Reilly i BigData 

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Free e-book on Data Science with R

Free e-book on Data Science with R | Big Data Analysis in the Clouds | Scoop.it
A new book by Jeffrey Stanton from Syracuse Iniversity School of Information Studies, An Introduction to Data Science, is now available for free download.
Pierre Levy's insight:

Throughout the book, you'll find many examples of data science applications implemented in the R language. For R beginners a Getting Started with R chapter is included, but it does get into some fairly in-depth topics including sentiment analysis of Twitter data, working with data in Hadoop via RHadoop, and creating information maps. R code is sprinkled liberally for your own use, and available to download (also under an open-source license) from GitHub.

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Carla Gentry CSPO's curator insight, March 18, 2013 1:02 PM

Good for the newbies :)

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The Fourth Paradigm: Data-Intensive Scientific Discovery - Microsoft Research

The Fourth Paradigm: Data-Intensive Scientific Discovery - Microsoft Research | Big Data Analysis in the Clouds | Scoop.it
/collaboration/fourthparadigm
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Mega collection of data science books and terminology

Mega collection of data science books and terminology | Big Data Analysis in the Clouds | Scoop.it
More than a thousand keywords with detailed explanations, and hundreds of machine learning / data science books categorized by programming language used to ill…
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Rob Thomas, author, ‘Big Data Revolution’

Rob Thomas, author, ‘Big Data Revolution’ | Big Data Analysis in the Clouds | Scoop.it
“Competing and innovating have changed forever” – Rob Thomas, author, ‘Big Data Revolution’
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[Book] The Field Guide to Data Science

[Book] The Field Guide to Data Science | Big Data Analysis in the Clouds | Scoop.it
Data Science is the competitive advantage of the future for organizations interested in turning their data into a product through analytics. Industries from he…
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luiy's curator insight, December 10, 2013 6:38 PM

Booz Allen Hamilton created The Field Guide to Data Science to help organizations of all types and missions understand how to make use of data as a resource. The text spells out what Data Science is and why it matters to organizations as well as how to create Data Science teams. Along the way, our team of experts provides field-tested approaches, personal tips and tricks, and real-life case studies. Senior leaders will walk away with a deeper understanding of the concepts at the heart of Data Science. Practitioners will add to their toolboxes.

In The Field Guide to Data Science, our Booz Allen experts provide their

insights in the following areas:

 

Start Here for the Basics provides an introduction to Data Science, including what makes Data Science unique from other analysis approaches. We will help you understand Data Science maturity within an organization and how to create a robust Data Science capability. Take Off the Training Wheels is the practitioners guide to Data Science. We share our established processes, including our approach to decomposing complex Data Science problems, the Fractal Analytic Model. We conclude with the Guide to Analytic Selection to help you select the right analytic techniques to conquer your toughest challenges. Life in the Trenches gives a first hand account of life as a Data Scientist. We share insights on a variety of Data Science topics through illustrative case studies. We provide tips and tricks from our own experiences on these real-life analytic challenges. Putting it All Together highlights our successes creating Data Science solutions for our clients. It follows several projects from data to insights and see the impact Data Science can have on your organization.

 

http://www.boozallen.com/media/file/The-Field-Guide-to-Data-Science.pdf

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The promise and perils of a datafied world

The promise and perils of a datafied world | Big Data Analysis in the Clouds | Scoop.it
Pierre Levy's insight:

First, then, a definition. Big data describes the idea that everything can be digitised and "datafied" – thanks to cheaper storage, faster processing and better algorithms. And that really means everything, from your current location or liking for strawberry pop tarts, to your propensity for misspelling and degree of personal compassion. And not just your data: everyone's data.

This changes science, say the authors, by ridding us of biased random samples and the need to massage the resulting data to make it sufficiently representative of a larger population. Could biased sample sets be at the root of many failed attempts to replicate experiments?

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Big Data: A Revolution That Will Transform How We Live, Work, and Think

<em>Big Data:</em> A Revolution That Will Transform How We Live, Work, and Think | Big Data Analysis in the Clouds | Scoop.it
Big Data is a new book from Viktor Mayer-Schonberger, a respected Internet governance theorist; and Kenneth Cukier, a long-time technology journalist who's been on the Economist for many years.
Pierre Levy's insight:

A simple way of thinking of big data versus "small data": Small data relies on statistical sampling, and emphasises the reliability and accuracy of each measurement. With big data, you sample the entire pool of activities -- all the books sold, all the operations performed -- and worry less about inaccuracies and anomalies in individual measurements, because these are drowned out by the huge numbers of observations performed.

See here another review : http://www.niemanlab.org/2013/03/were-going-to-tell-people-how-to-interview-databases-the-rise-of-data-big-and-small-in-journalism/

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