Financial crises result from a catastrophic combination of actions. Vast stock market datasets offer us a window into some of the actions that have led to these crises.

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January 27 2015

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December 26 2012

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Scoops

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Big because of the volume, velocity and variety of the data that are processed in the cloud. Economic, social and cultural effects.

Curated by
Pierre Levy

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A practitioner of data science is called a data scientist. The term was coined by DJ Patil and Jeff Hammerbacher. Good data scientists are able to apply their skills to achieve a broad spectrum of end results. Some of these include the ability to find and interpret rich data sources, manage large amounts of data despite hardware, software and bandwidth constraints, merge data sources together, ensure consistency of data-sets, create visualizations to aid in understanding data and building rich tools that enable others to work effectively. The skill-sets and competencies that data scientists employ vary widely. Data scientists are an integral part of competitive intelligence, a newly emerging field that encompasses a number of activities, such as data mining and analysis, that can help businesses gain a competitive edge. |
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The Wikimedia Foundation’s first major new project in 7 years is now feeding the biggest project in that stable, Wikipedia itself. But anyone can take structured data from Wikidata, due to its open license. subprojeto Wikidata não só abastece a Wikipédia como é embrião da Web semântica
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A Formally, Bayesian networks are directed acyclic graphs whose nodes represent random variables in the Bayesian sense: they may be observable quantities, latent variables, unknown parameters or hypotheses. Edges represent conditional dependencies; nodes which are not connected represent variables which are conditionally independent of each other. Each node is associated with a probability function that takes as input a particular set of values for the node's parent variables and gives the probability of the variable represented by the node. For example, if the parents are Boolean variables then the probability function could be represented by a table of entries, one entry for each of the possible combinations of its parents being true or false. Similar ideas may be applied to undirected, and possibly cyclic, graphs; such are called Markov networks. Efficient algorithms exist that perform inference and learning in Bayesian networks. Bayesian networks that model sequences of variables ( |

Un ejemplo de aplicación de análisis de grandes datos: Este artículo presenta evidencias de como es posible predecir el movimiento de la bolsa a través del análisis de la data de visitas a determinadas páginas financieras de Wikipedia.