Foundations of Programming and Scientific Computing with Python

  • Credits: 3
  • Coefficient: 1
  • Volume Horaire: 45h (Cours: 15h, TD: 15h, TP: 15h)
  • Objectives: Teach foundational programming skills in Python and their application to scientific computing in mathematics and data science.
  • Prerequisite Knowledge: Basic computer literacy, Algorithmique et Structure de Données 1.
  • Content:
    • Chapter 1: Python Basics
      • Variables, data types, control structures
      • Functions, modules, libraries
    • Chapter 2: Scientific Computing Libraries
      • NumPy: arrays, linear algebra
      • SciPy: numerical methods
      • Matplotlib: plotting and visualization
    • Chapter 3: Numerical Methods
      • Solving linear systems
      • Root finding, numerical integration
    • Chapter 4: Applications
      • Simulating mathematical models
      • Data analysis and visualization
    • Note: Practical work focuses on implementing numerical algorithms in Python.
  • Evaluation: Exam (50%), Continuous Assessment (50%)
  • References:
    • J. VanderPlas, Python Data Science Handbook, O’Reilly, 2016
    • A. B. Downey, Think Python, O’Reilly, 2015
    • H. P. Langtangen, A Primer on Scientific Programming with Python, Springer, 2016