Scientific Computing in Python

A foundational overview of Python programming language, covering basic syntax, data types, functions, and classes. Students learn about Python's role in scientific computing and set up their development environment with essential tools like Anaconda and IDEs.

Introduction to crucial scientific computing libraries including NumPy, SciPy, and Matplotlib. Focus on data manipulation, array operations, and visualization techniques essential for scientific applications.

Techniques for fitting mathematical models to experimental data, including least squares methods, polynomial fitting, and regression analysis using Python's scientific libraries.

Implementation of numerical methods for finding roots of equations, including bisection method, Newton's method, and other iterative techniques for solving nonlinear equations.

Study of numerical differentiation techniques, Richardson extrapolation, and methods for computing partial derivatives. Includes practical applications in physics and engineering problems.

Numerical integration methods, including trapezoidal rule, Simpson's rule, and adaptive quadrature techniques for solving complex integrals.

Solving ordinary and partial differential equations numerically using Python. Covers Euler's method, Runge-Kutta methods, and their applications in physical systems.

Implementation of Fourier transforms and spectral analysis techniques. Includes practical applications in signal processing and data analysis.

Comprehensive coverage of statistical methods in Python, including probability concepts, descriptive statistics, inferential statistics, and correlation analysis. Focuses on practical applications in data analysis and scientific research.