Statistique et Big Data Analytics; Volumétrie, L'Attaque des Clones
This is an incremental guide for statisticians needing to transition skills to handle large-scale data analytics.
The paper addresses the challenge of scaling traditional statistical methods to big data volumes, focusing on adapting unsupervised and supervised learning algorithms to Hadoop's Map-Reduce framework.
This article assumes acquired the skills and expertise of a statistician in unsupervised (NMF, k-means, SVD) and supervised learning (regression, CART, random forest). What skills and knowledge do a statistician must acquire to reach the "Volume" scale of big data? After a quick overview of the different strategies available and especially of those imposed by Hadoop, the algorithms of some available learning methods are outlined in order to understand how they are adapted to the strong stresses of the Map-Reduce functionalities