BigDataFr recommande : Data Scientist : le métier le plus attrayant du XXIe siècle ? ‘Il y a fort à parier, en effet, que les Data Analysts ou encore Data Scientists vont acquérir une certaine popularité et se faire approcher par des entreprises qui commencent à prendre conscience que l’analyse de données à grande échelle, […]
Documentation
[Forbes] BigDataFr recommends: Capitalize On Social Media With Big Data Analytics
BigDataFr recommends: Capitalize On Social Media With Big Data Analytics ‘Social media promises to accelerate innovation, drive cost savings and strengthen brands through mass collaboration. Companies across every industry are using it to hype new products and services, and also monitor what people are saying about their brand. And yet, most struggle to measure the […]
[LesEchos] BigDataFr recommande : Comment traiter les données à l’âge de l’Internet des objets ?
BigDataFr recommande : Comment traiter les données à l’âge de l’Internet des objets ? ‘ Les utilisations concrètes ne manquent pas, depuis l’industrie manufacturière où les données collectées sur des machines défectueuses sont exploitées pour évaluer de futurs dysfonctionnements potentiels jusqu’aux projets agricoles où le matériel agricole connecté permet de cerner un microclimat dans un […]
[Forbes] BigDataFr recommends: Roundup of Analytics, Big Data & Business Intelligence Forecasts
BigDataFr recommends: Roundup Of Analytics, Big Data & Business Intelligence Forecasts And Market Estimates, 2015 ‘Salesforce (NYSE:CRM) estimates adding analytics and Business Intelligence (BI) applications will increase their Total Addressable Market (TAM) by $13B in FY2014. 89% of business leaders believe Big Data will revolutionize business operations in the same way the Internet did. 83% […]
[ERIC – Institut of Education Sciences] BigDataFr recommends: Research on Implementing Big Data Technology, People, & Processes #datascientist #machinelearning
BigDataFr recommends: Research on Implementing Big Data: Technology, People, & Processes ‘Abstract: When many people hear the term “big data”, they primarily think of a technology tool for the collection and reporting of data of high variety, volume, and velocity. However, the complexity of big data is not only the technology, but the supporting processes, […]
[Maddyness] BigDataFr recommande: Logmatic annonce une levée de fonds de 900 000 euros auprès d’ISAI Seed Club
BigDataFr recommande : Logmatic annonce une levée de fonds de 900 000 euros auprès d’ISAI Seed Club « C’est en intégrant ses propres logs au cours de l’été 2014 dans l’algorithme de Focusmatic (solution logicielle initialement développée par l’équipe) que les 3 fondateurs ont réalisé le potentiel de cet outil. En effet, Logmatic permet à tout […]
[The DataCamp Blog] BigDataFr recommends: Choosing R or Python for data analysis? An infographic
BigDataFr recommends: Choosing R or Python for data analysis? An infographic ‘I think you’ll agree with me if I say: It’s HARD to know whether to use Python or R for data analysis. And this is especially true if you’re a newbie data analyst looking for the right language to start with. It turns out […]
[arxiv] BigDataFr recommends: Computing on Masked Data to improve the Security of Big Data
BigDataFr recommends : Computing on Masked Data to improve the Security of Big Data Organizations that make use of large quantities of information require the ability to store and process data from central locations so that the product can be shared or distributed across a heterogeneous group of users. However, recent events underscore the need […]
[arXiv] BigDataFr recommends: Network Filtering for Big Data
BigDataFr recommends: Network Filtering for Big Data: Triangulated Maximally Filtered Graph ‘We propose a network-filtering method, the Triangulated Maximally Filtered Graph (TMFG), that provides an approximate solution to the Weighted Maximal Planar Graph problem. The underlying idea of TMFG consists in building a triangulation that maximizes a score function associated with the amount of information […]
[arXiv] BigDataFr recommends: Two models at work on Big Data application frameworks
BigDataFr recommends: Actors vs Shared Memory: two models at work on Big Data application frameworks ‘This work aims at analyzing how two different concurrency models, namely the shared memory model and the actor model, can influence the development of applications that manage huge masses of data, distinctive of Big Data applications. The paper compares the […]