Le Big data marque le début d'une t

Le Big data marque le début d'une transformation majeure, qui va affecter de façon profonde l’ensemble des secteurs (de l’e-commerce à la recherche scientifique en passant par la finance et la santé).
L’exploitation de ces immenses masses de données nécessite des techniques mathématiques sophistiquées visant à extraire l’information pertinente. L'ensemble de ces méthodes forme le socle de la « science des données » (ou data science). Ce passage des données aux connaissances est porteur de nombreux défis qui requièrent une approche interdisciplinaire. La « science des données » s’appuie fortement sur le traitement statistique de l’information (statistiques mathématiques, statistiques numériques, apprentissage statistique ou machine learning). De l’analyse de données exploratoires aux techniques les plus sophistiquées d’inférence (modèles graphiques hiérarchiques) et de classification ou de régression (deep learning, machine à vecteurs de support), une vaste palette de méthodes de statistiques mathématiques et numériques et d’apprentissage est mobilisée. Ces méthodes, pour pouvoir être développées à l’échelle de masses de données requièrent la maitrise des mécanismes de distribution des données et des calculs à très grande échelle. Les mathématiques appliquées (analyse fonctionnelle, analyse numérique, optimisation convexe et non convexe) ont également un rôle essentiel à .jouer.

Résultats (Anglais) 1:

The Big data marks the beginning of a major transformation, which will deeply affect all the sectors (of e-commerce for scientific research through the finance and health).The exploitation of these huge masses of data requires mathematical sophisticated techniques to extract the relevant information. All of these methods form the base of the "data science" (or data science). This passage from data to knowledge carries many challenges that require an interdisciplinary approach. The "data science" relies heavily on the statistical information processing (mathematical statistics, numerical statistics, statistical learning or machine learning). Analysis of exploratory data to the most sophisticated techniques of inference (hierarchical graphic models) and classification or regression (deep learning, support vector machine), a wide range of mathematical and numerical statistics and learning methods is mobilized. These methods, in order to be developed across masses of data require the mastery of the mechanisms of distribution of data and calculations on a very large scale. Applied mathematics (functional analysis, numerical analysis, convex and Nonconvex optimization) also have a crucial role in .jouer.

Résultats (Anglais) 2:

The Big data is the beginning of a major transformation that will profoundly affect all sectors (from e-commerce to scientific research through finance and health).
The exploitation of these immense datasets requires sophisticated mathematical techniques to extract relevant information. All of these methods forms the basis of "science data" (data or science). This passage data to knowledge carries many challenges that require an interdisciplinary approach. The "data science" relies heavily on the statistical processing of information (mathematical statistics, numerical statistics, statistical learning or machine learning). From the exploratory data analysis to sophisticated inference techniques (hierarchical graphical models) and classification or regression (deep learning, support vector machine), a wide range of mathematical and numerical statistical methods and Learning is mobilized. These methods, to be developed across datasets require the mastery of data distribution mechanisms and calculations on a massive scale. Applied mathematics (functional analysis, numerical analysis, optimization and convex non-convex) also have a vital role to .jouer.

Résultats (Anglais) 3:

The Big data marks the beginning of a major transformation, which will affect so deep all the sectors (from e-commerce to the scientific research in passing by the finance and health) .
the exploitation of these immense masses of data requires mathematical techniques sophisticated efforts to extract the relevant information.The whole of these methods form the base of the "science of data" (or data science). This passage from data to knowledge is the bearer of many challenges that require an interdisciplinary approach. The "science of data" relies heavily on the statistical processing of information (statistics mathematics, numeric statistics,Statistical learning or machine learning). From the analysis of exploratory data the most advanced techniques of inference (graphics models hierarchical) and of classification or regression (deep learning, machine to vectors of support), a wide range of methods of mathematical statistics and digital and learning is mobilized. These methods,To be able to be developed at the scale of masses of data require the control of mechanisms of distribution of data and calculations to very large scale. The applied mathematics (functional analysis, numerical analysis, optimization convex and non-convex) also have an essential role to .play.