Training structure
Faculty of Science
Presentation
Fueled by increasingly powerful means of collection, statistical data (aka data) is growing exponentially, and few areas escape measurement that is becoming more extensive every day. But if the collection of data is one thing, its analysis is another. This is made difficult by two main phenomena: the size of the data and the complexity of the measured phenomena. Contemporary statistics seeks to solve these two problems. It is thus led to evolve very rapidly, by preserving the best of past tools, which it adapts to massive and large-scale data, and by proposing at the same time more and more refined modeling methods that respect the complexity of the phenomena. Classical statistics has thus evolved towards a more computational "data science", which integrates automatic learning and diagnostic techniques halfway between statistics and artificial intelligence.
The Statistics and Data Science program provides training in all contemporary statistical analysis and modeling methodologies. While it leads to the profession of "data scientist", it integrates the aspects of methodological design - thanks to the mastery of the underlying mathematics and their computer programming - as well as the rigorous application of methods and models to data of various types and domains.
This course is split in the second year into two more specialized sub-courses, the teaching of which remains partially shared. The first of these specializations is Biostatistics, which focuses on the analysis and modeling of life data. The second is Information and Decision Management (MIND), which specializes in the analysis and modeling of economic data as well as the management of decisions and associated risks.
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Objectives
The first year of the program provides students with the conceptual, mathematical and practical foundations of the main branches of statistical methodology: statistical information and decision making, probabilistic modeling and statistical inference, and multidimensional linear exploration and modeling. The program also includes two optional introductory courses in economics and life data.
At the end of this year, the student will have a solid foundation on which to develop a specialization in the second year. They will also have a clear idea of which specialization will suit them best: Biostatistics or MIND.
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Know-how and skills
For each of the major families of techniques and models, the student will master the ideas, the mathematical formalism to the point of being able to adapt and program it, and of course the correct use, i.e. critical, conscious and respectful of the limits of validity.
The student will also begin to be able to build a data processing chain consisting of the following steps, in order: definition of a problem, management and pre-processing of the data, design and implementation of the analytical chain: exploratory analysis, modeling, estimation and choice of models, analysis of results, and finally, writing a clear, rigorous and pedagogical report of the entire work.
The student will also have learned, through various projects and homework assignments, to correctly and efficiently program statistical calculations required by original problems.
Finally, he or she will have learned to present and defend his or her work in front of an audience in about twenty minutes.
Program
A tutored project in the second semester of M1.
Stochastic processes
Information system and databases
4 creditsAnalysis of multi-dimensional data
5 creditsOptimization
5 creditsSoftware development
4 creditsInferential statistics
Information and decision theories
2 credits
Stochastic control
2 creditsTime series
4 creditsEstimation and non-parametric tests
4 creditsLinear model
5 creditsProject
5 creditsEnglish
2 creditsCHOICE2
2 creditsYour choice: 1 of 4
Epidemiology tools
2 creditsMicroeconomics
2 creditsBioinformatics Learning Lab
2 creditsBiological information
2 credits
CHOICES1
4 creditsYour choice: 1 of 2
Alignment and Phylogeny
4 creditsGeneral economy
4 credits
Programming R
2 credits
Admission
Conditions of access
How to register
Applications are made on the following platforms:
- French & European students: follow the "My Master" procedure from the website: https: //www.monmaster.gouv.fr/
- International students from outside the EU: follow the "Studies in France" procedure: https: //pastel.diplomatie.gouv.fr/etudesenfrance/dyn/public/authentification/login.html
Target audience
Students with a degree in general mathematics.
Necessary pre-requisites
Have a good level in analysis, linear and bilinear algebra, geometry, elementary statistics and probability.
Recommended prerequisites
Have a good level in analysis, linear and bilinear algebra, geometry, elementary statistics and probability.
And then
Further studies
M2 in statistics in the broadest sense, preparation for the agrégation in mathematics, and possible doctorate afterwards.
Professional integration
Professions: statistician, biostatistician, data scientist, data analysis, all at the engineering level.
All sectors of activity: industry, research and development, health, agronomy, banking and insurance, trade, etc.