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Algorithms

2023-2024

FrECOLE DU NUMERIQUE ( EDN )

Code Cours :

2324-EDN-COMP-EN-4008


Niveau Année de formation Période Langue d'enseignement 
S1FrAnglais
Professeur(s) responsable(s)CHARLES YAACOUB
Intervenant(s)Pas d'autre intervenant

    Ce cours apparaît dans les formations suivantes :
  • Ecole Du Numérique (EDN) - Master 1 DATA Management In Biosciences - S1 - 5 ECTS

Pré requis

Basic knowledge in programming

Objectifs du cours

This course aims at providing the learners coming from multiple backgrounds with basic knowledge and skills in programming and algorithm design. By the end of this course, students will be able to:



- Apply basic programming concepts.


- Use the Python language for the development of computer programs.


Design and implement algorithms to solve practical cases.


Contenu du cours

Introduction to Programming


Algorithms and flowcharts


Python syntax


Basic datatypes


Character strings


Lists, tuples, and dictionaries


Scripts and functions/methods


Branch instructions ; conditional statements and loops


Python modules (including for example: datetime, math, etc…) and module creation


File processing


Matplotlib


Searching and sorting algorithms


Opening (i.e., open-ended topics to be further developed in later courses):


- Introduction to object-oriented programming


Introduction to data processing and data science



Modalités d'enseignement

Organisation du cours

In class face-to-face sessions including lectures, problem sessions, and guided lab activities. Off-class activities include problem-solving and projects.



A blend of directive and active learning: the former aims at transferring the knowledge from the teacher to the learner, and the latter aims to make the learner an actor of the knowledge that is constructed through experimentation, projects, and reflection.



Méthodes pédagogiques


    Évaluation

    Contrôle continu : coeff. 1


    Bibliographie

    • Guttag, John. Introduction to Computation and Programming Using Python: With Application to Understanding Data. MIT Press.


    Ressources internet



     
    * Informations non contractuelles et pouvant être soumises à modification
     
     
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