Scientific computing with Python

This course introduces first-year mathematics students to Python for scientific computing. 

Objectives:

• Master Python syntax.
• Discover scientific libraries (NumPy, Matplotlib).
• Apply programming to linear algebra, differential calculus, and basic probability/statistics.

Benefits: 

• Bridge theory-practice: Transform mathematical formulas into executable code.
• Visualize concepts: Plot functions, vector fields, statistical distributions.
• Algorithm implementation: Numerical methods, matrix operations, simulations.
• Future-proof skills: Essential for Master's/PhD research, data analysis careers.
• Immediate applications: Solve real math problems (systems of equations, derivatives.

Chapters:

1. Python Introduction (Installation, Jupyter).
2. Basic Syntax (Variables, Operations, Control Structures).
3. Functions & Modules.
4. Data Structures (Lists, Tuples, Dictionaries, Sets).
5. NumPy & Matplotlib Introduction.
6. Applications: Linear Algebra, Calculus, Probability​.