The Data Science course is designed for students with M1 level knowledge in applied mathematics or computer science with prerequisites in statistics or machine learning. It is a high-level scientific course balanced between theory and implementation.
The training objective is both the pursuit of a PhD and the beginning of a career as a data scientist.
- The required programming skills are
- The required competencies in applied mathematics are
The major players in the economic world today are becoming increasingly aware of the potential of their data and are looking for ways to exploit and make the most of this useful information. To help them in this task, datascientists (literally data scientists) are the people in charge of retrieving, storing, organizing and processing this mass of information in order to derive value from it.
The datascientist is a new kind of profit, resulting from the convergence of statistics and computer science. Giving a precise definition of what the word datascientist covers remains a challenge. What certainly best characterizes him is the variety of skills he has to master. It is a hybrid profile, which must have a solid background in mathematics, statistics but also master the computer tools or infrastructure necessary for data management and processing. He or she must have curiosity and a thirst for understanding the business of the sector in which he or she works. The objective of this master's degree is to prepare you to become the data scientists of tomorrow in both the academic and industrial worlds. A large number of our students also choose to do a PhD.
Knowledge extraction methods, in order to be developed on the scale of masses of data, require mastery of the mechanisms of parallelisation and distribution of calculations, methods of access and queries to distributed databases on a very large scale and in real time. The large scale influences the very design of knowledge extraction and statistical inference algorithms, leading to the use of new tools from different branches of mathematics (functional analysis, numerical analysis, convex and non-convex optimization) which must be understood.
This course combines theoretical and methodological courses with "real-life" projects involving all aspects of data science, from acquisition to exploitation and analysis. A significant part of the course will be validated in the form of projects. One of the original features of this course is the use of innovative pedagogies based on project-based learning and participation in data science competitions (kaggle). The candidate is free to choose a work placement offered by one of the Master's teachers, a work placement in a company offered as part of the "internship grant", or a work placement from a different origin that has been approved by a Master's teacher. The internship must present a real scientific challenge and the application development of one of the themes developed in the master's programme. The duration is a minimum of 4 months, starting in April.
The skills in the field of statistical learning and Big Data processing that students following this course will acquire are sought after both in start-ups (many of which have projects based on knowledge extraction, recommendation and targeting methods) and in large companies (all fields of activity are impacted). These new "datascientist" professions are multifaceted, ranging from the implementation of new generations of decision-making information systems to the development of completely new applications (around e-commerce, recommendation, mining of social networks, etc.).
The need for PhD students is also important in this field of breakthrough innovations. Thesis proposals are numerous in public research (University, CNRS, INRIA, CEA, CNES, INRA, INSERM, LETI, etc.) and in major industrial research laboratories (Aerospace, Alcatel, Sagem, General Electric, Matra, Philips, Siemens, Thales, EDF, etc.).
To apply: online application on the website https://www.ip-paris.fr/master-2-data-science/
Opening of admissions: 20 December 2019 / Closing of admissions: 28 February (session 1) - 30 April (session 2) - 30 June (session 3)