M2 - Statistics and Data Science (SSD) - MIND

This M2 program is aimed at students with an M1 in Statistics and Data Science (SSD) or any other M1 in mathematics or equivalent, with a strong specialization in probability and statistics.

This M2 course is divided into two more specialized sub-courses, some of which are shared.

- The first of these specializations is Biostatistics, which focuses on the analysis and modeling of living data.

- The second is Information and Decision Management (MIND), specializing in the analysis and modeling of economic data, as well as the management of associated decisions and risks.

• The ambition of the SSD-Biostat pathway is to meet the expectations of M1 SSD students who are attracted by the modeling of data from the living world or the environment. The statistical aspects covered in this course range from life modeling to the most theoretical issues in statistics and stochastic modeling. Numerical aspects are extremely present in this pathway, and require a pronounced taste for computer programming.

The SSD-Biostat pathway is a demanding course because it focuses on concepts rather than techniques. Indeed, in the field of data in the broadest sense, digital technologies, with the advent of artificial intelligence, are evolving fast and becoming obsolete even faster. Future statistical engineers or researchers who have to deal with data will be able to train in new technologies throughout their working lives, all the more so if they have had solid initial conceptual training. The added value of training is precisely to provide a theoretical understanding of the statistical concepts underlying automatic algorithms. Graduates must also be able to keep abreast of the latest technological developments.

In the second year, the SSD-Biostat pathway remains partly shared with the Information and Decision Management pathway (SSD-MIND). However, the SSD-BIOSTAT pathway has its own specialization courses (two per M2 semester) in life and environmental data science, with an emphasis on introduction to research.

• The ambition of the SSD-MIND course is to meet the expectations of M1 SSD students who are attracted by the application of data science in business. Given the wide diversity of companies and their issues, this M2 program trains students in generalist, "all-terrain" data science. In addition, it provides training that is more specific to the corporate context and its economic and managerial issues (economic information, financial risk management, customers, corporate strategy, etc.).

The SSD - MIND program is a mathematical engineering course, with a focus on methodology and perfect mastery of statistical concepts and models. Graduates of this course will be able to deal with all types of data and problems, and design a complete and often original methodology to meet these problems, starting with the management and organization of data, continuing with its exploration and targeted reduction, then with the modeling of phenomena of interest, and finally synthesizing the information extracted for decision-making purposes. He or she must also be able to pass on to the business the knowledge synthesized from the information extracted from the data. Each new set of data and each question asked about it is often a new problem, and the application of a standard method to this data is therefore unsuitable. On the contrary, the aim is to write a mathematical model adapted to these data (in the sense that it expresses their complexity satisfactorily) and make it assimilable to a standard estimation method, or to design and program a more specific method. The emphasis placed by this course on conceptual and mathematical mastery of tools ensures that graduates are highly adaptable and self-training, as required by the rapid evolution of data science.

The SSD-MIND pathway remains partly shared in the second year with the biostatistics pathway (SSD-Biostat), more specialized in the analysis and modeling of data from the living world or the environment. The SSD-MIND pathway is a double degree program in partnership with IAE (which teaches economics and management).