M1 - Statistics and Data Science (SSD)

  • Training structure

    Faculty of Science

Presentation

Fueled by increasingly powerful means of data collection, statistical data (aka data ) is growing exponentially, and few areas escape ever more extensive measurement. But while collecting data is one thing, analyzing it is quite another. The latter is made difficult by two main phenomena: the size of the data and the complexity of the phenomena being measured. Contemporary statistics aims to solve both these problems. As a result, it is evolving very rapidly, retaining the best of past tools and adapting them to massive, large-scale data, while at the same time proposing increasingly refined modelling approaches that respect the complexity of the phenomena involved. Classical statistics has thus evolved towards a more computational "data science", incorporating 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 a career as a "data scientist", it also integrates aspects of methodological design - thanks to mastery of the underlying mathematics and computer programming - as well as the rigorous application of methods and models to data of various types and domains.

In the second year, this course is split 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 life 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.

 

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Objectives

The first year of the course provides students with the conceptual, mathematical and practical foundations of the main branches of statistical methodology: statistical information and decision-making, probabilistic modelling and statistical inference, and multidimensional linear exploration and modelling. The course also contains two opening courses to choose from among the introductory courses in the economy and life data.

At the end of this year, the student will have a solid foundation on which the specialization can be developed in the second year. He or she will also have an informed idea of which specialization will suit him or her 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 be able to 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, data management and pre-processing, design and implementation of the analytical chain: exploratory analysis, modeling, estimation and choice of models, analysis of the results, and finally, writing a clear, rigorous and educational report of all the work.

The student will also have learned, during various projects and domestic work, to correctly and efficiently program the statistical calculations required by original problems.

Finally, he or she will have learned to present and support his or her work in front of an audience in about twenty minutes.

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Program

A tutored project in the second semester of the M1.

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  • Stochastic processes

  • Information systems and databases

    4 credits
  • Multi-dimensional data analysis

    5 credits
  • Optimization

    5 credits
  • Software development

    4 credits
  • Inferential statistics

  • Information and decision theories

    2 credits
  • Stochastic control

    2 credits
  • Time series

    4 credits
  • Estimation and non-parametric tests

    4 credits
  • Linear model

    5 credits
  • Project

    5 credits
  • English

    2 credits
  • CHOIX2

    2 credits
    • Your choice: 1 of 4

      • Epidemiology tools

        2 credits
      • Microeconomics

        2 credits
      • Bioinformatics Learning Lab

        2 credits
      • Biological information

        2 credits
  • CHOIX1

    4 credits
    • Your choice: 1 of 2

      • Alignment and Phylogeny

        4 credits
      • General economics

        4 credits
  • R programming

    2 credits

Admission

Access conditions

How to register

Applications can be submitted on the following platforms: 

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Target audience

Etudiant.es hold a bachelor's degree in general mathematics.

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Necessary prerequisites

Have a good level in analysis, linear and bilinear algebra, geometry, elementary statistics and probability.

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Recommended prerequisites

Have a good level in analysis, linear and bilinear algebra, geometry, elementary statistics and probability.

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And then

Further studies

M2 in statistics in the broad sense, preparation for the agrégation in mathematics, and a doctorate possible afterwards.

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Professional integration

Professions: statisticien.ne, biostatisticien.ne, data-scientist, data analysis, all at engineering level.

All sectors of activity: industry, research and development, health, agronomy, banking and insurance, commerce, etc.

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