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Mathematics and programming training

Year : M1/M2
Semester : S1
ECTS : 0
Coordinator : Morgane Thomas-Chollier, Biology Department, ENS (MC ENS)
Hours : 30h

This training aims at providing the students the opportunity to update their knowledge in maths and informatics (programming), during the first week of the new term.

The first day includes an introduction to the computer working environment of the ENS, necessary for the newly-arrived students in M1 or M2.

For newly-arrived M1 students, this training (both maths and informatics) is a pre-requisite for the Statistics course.

This week will start on Monday 4th September 2017 9am.

Themes :

1. WORKING ENVIRONMENT :
The computer room is equipped with machines running under the Linux environment (Ubuntu). The students will learn how to login on the machines, become familiar with the graphical environment and also use the command line.
2. INFORMATICS :
We chose to make the students work with python, a simple and powerful programming language. For the start of new term, the aim would be to have understood the various types of data (especially lists and dictionaries), to be able to write a function (notion of local and global variables), be able to use modules, and understand how to read and to write in a file (module sys and os). The last session is devoted to the langage R.
3. MATHEMATICS :
This introductory course aims to provide students with basic mathematical tools necessary for most subsequent courses. The first half of the course will be an introduction to the study of dynamical systems, and will start with a review of linear algebra ; we will then study the resolution of systems of linear differential equations, before generalising some results to non-linear equations (e.g., study of the fixed points in the Lotka–Volterra equations). The second half of the couse will provide students with basic knowledge in probability and statistics—which is crucial to any biologist nowadays—and will cover the main theorems that give rise to common hypothesis tests broadly used the scientific community.
The course will be mostly self-contained ; however, few pre-requisites include basic concepts in linear algebra (matrices, eigenvectors, eigenvalues, diagonalisation), analysis (derivatives, differential equations), and probability (basic combinatorics, probability density function, central limit theorem).

Organization :

The teaching unit is organised over the first week of the semester, with informatics in the mornings, and maths classes in the afternoons. It is possible to follow only one training (maths or informatics).

Further reading

Linux command-line :
- Interactive MOOC (English) :https://www.codecademy.com/en/courses/learn-the-command-line/
- Other courses are listed on this page :http://freevideolectures.com/blog/2012/04/5-websites-learning-linux/

Python
- Python documentation : http://docs.python.org
- A good Python course, by a professor at Paris 7 : http://www.dsimb.inserm.fr/~fuchs/python/cours_python.pdf
- For those wishing to fo further : a free book in French : http://inforef.be/swi/python.htm
- Another book, in English :http://www.greenteapress.com/thinkpython/

R
- Various documentation in many languages on CRAN : https://cran.r-project.org/other-docs.html
- A resource in French :www.duclert.org/Aide-memoire-R/Le-langage/Introduction.php

Teaching team :

Lambert Moyon (ENS, Paris)
Jérémy Boussier (Institut Pasteur, Paris)
Swann Floc’hlay (ENS, Paris)