The department is unable to provide any admissions counseling, such as advising or assessment, for applicants or prospective applicants and therefore does not respond to such requests. Our M.S. admissions and program pages address all the relevant information, so please take the time to read them thoroughly. You might also consult the course catalog (which will be slightly modified next year). Take also a look at the minimal prerequisites.
DataScience and BigData in Paris Saclay
DataScience and BigData are key enabling technologies… Data science is based on a set of techniques and theories from many fields within the broad areas of mathematics, statistics, information science, and computer science, including machine learning,, signal and image processing, statistical learning, data mining, but also database, data engineering, visualization, predictive analytics, uncertainty modeling, data warehousing, data compression, computer programming and high performance computing. Of particular relevance are algorithms and methods that can be scaled to Big Data are of particular interest in data science. Data science utilizes data preparation, statistics, predictive modeling and machine learning to investigate problems. Applications are numerous: marketing, fraud detection, risk management, marketing analytics, public policy, etc. Data science has now a sginificant impact both in academic and applied research; it has recently proposed disruptive ideas in man-machine interface (machine translation, speech recognition), web-related technologies (search engines, ad-management, recommender systems), digital economy (marketing, fraud detection, churning), but also health care, social sciences and the humanities. It heavily influences economics, business and finance.
Regular week P3
- Monday, 10h00-12h00, 14h-17h00, Systems for Big Data Analytics (Y. Diao, Ecole Polytechnique, location: Ecole Polytechnique)
- Tuesday, 11h00-13h00, Apprentissage par aggregation (P. Alquier, ENSAE, location: ENSAE)
- Tuesday 14h00-16h00, 16h00-18h00 (lab) Simulation-based Learning (G. Fort, Telecom Paris-Tech, E. Moulines, Ecole Polytechnique, location: ENS Cachan, joint MVA)
- Tuesday 14h00-16h00, Introduction mathématiques au compressed sensing (G. Lecué, ENSAE, location: ENSAE)
- Wednesday 13h00-16h00, Kernel methods for Machine Learning (J. Mairal, INRIA, location ENS Cachan)
- Thursday 14h00-18h00, Advanced Learning for text and graphs (M. Vazirgiannis, Ecole Polytechnique, location: Ecole Polytechnique)
- Wednesday 9h30-12h30, Sequential Learning and Sequential Optimization (G. Stoltz, HEC, location: Univ. Paris-Sud, joint filière Mathématiques de l’Aléatoire et Optimisation).