bianka: big data*

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  1. Sobre el milagro finlandés y otros abusos estadísticos en educación. «Finland is famously quoted by the right as a high performing PISA country. Yet, it is a small, homogeneous country with no streaming, high levels of vocational education, no substantial class divisions and no private schools. Facts curiously ignored by PISA supporters.»
    Tags: , por bianka (2013-11-10)
  2. Y es que esto es lo que hay. Soportes. Y claro, en medio de esto Pinterest (un sitio del que en España no usa ni cristo, por cierto) recibe una valoración que equivale a un 15% del valor de todo el grupo industrial FIAT (que fabrica y vende 2 millones de automóviles al año).
    Tags: , por bianka (2013-02-21)
  3. Por qué hay un acuerdo tácito en el sector de los datos sobre que todos son »científicos de datos». + el origen «libre» y la importancia de la identidad y la comunidad en el big data¿?
    Tags: , , por bianka (2012-03-10)
  4. Sobre los modelos de negocios detrás de los datos.

    When the expressivity of a technology domain lags the creativity of the strategic thinking, strategy gets structurally constrained by technology. When technology leads creativity, the canvas expands, and those who notice the newly-breakable constraints are able to cause disruption.
    Tags: por bianka (2012-03-10)
  5. Big data is data that exceeds the processing capacity of conventional database systems. The data is too big, moves too fast, or doesn't fit the strictures of your database architectures. To gain value from this data, you must choose an alternative way to process it.

    The value of big data to an organization falls into two categories: analytical use, and enabling new products.

    Input data to big data systems could be chatter from social networks, web server logs, traffic flow sensors, satellite imagery, broadcast audio streams, banking transactions, MP3s of rock music, the content of web pages, scans of government documents, GPS trails, telemetry from automobiles, financial market data, the list goes on.

    To clarify matters, the three Vs of volume, velocity and variety are commonly used to characterize different aspects of big data.
    Tags: por bianka (2012-01-23)

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