Scientific Computing in Python 10 Sections20 LessonsLifetime
Curriculum
- 10 Sections
- 20 Lessons
- Lifetime
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- Module 1 - Introduction to Python2 ItemsA 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.
- Module 2 - Python Libraries2 ItemsIntroduction to crucial scientific computing libraries including NumPy, SciPy, and Matplotlib. Focus on data manipulation, array operations, and visualization techniques essential for scientific applications.
- Module 3 - Linear Algebra2 ItemsExploration of fundamental linear algebra concepts implemented in Python. Covers matrix operations, eigenvalues, linear systems, and numerical methods for solving complex linear equations.
- Module 4 - Data Fitting2 ItemsTechniques for fitting mathematical models to experimental data, including least squares methods, polynomial fitting, and regression analysis using Python's scientific libraries.
- Module 5 - Finding Roots2 ItemsImplementation of numerical methods for finding roots of equations, including bisection method, Newton's method, and other iterative techniques for solving nonlinear equations.
- Module 6 - Differentiation2 ItemsStudy of numerical differentiation techniques, Richardson extrapolation, and methods for computing partial derivatives. Includes practical applications in physics and engineering problems.
- Module 7 - Integration2 ItemsNumerical integration methods, including trapezoidal rule, Simpson's rule, and adaptive quadrature techniques for solving complex integrals.
- Module 8 - Differential Equations2 ItemsSolving ordinary and partial differential equations numerically using Python. Covers Euler's method, Runge-Kutta methods, and their applications in physical systems.
- Module 9 - Fourier and Transforms2 ItemsImplementation of Fourier transforms and spectral analysis techniques. Includes practical applications in signal processing and data analysis.
- Module 10 - Statistics2 ItemsComprehensive 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.
Live Session 1 – Zoom Meeting – 04/27/2025 (1:00 PM ET)
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