Numerical Methods with Python 3
- Home
- Numerical Methods
Métodos Numéricos
- Métodos Numéricos, PCF-UNAM
Temario.
- Why Python ?
Intro.key
- Installation: There are two recommended ways to install it:
1.- With Anaconda which contains a complete data science toolkit.
Anaconda.
2.- Plain python - which is much lighter -
Python.
- An starting guide to Python, and in general to data science:
The Python compendium on github:
GitHub.
- Bibliography:
1.- Mathematical Methods for Physics and Engineering. Riley, Hobson and Bence.
2.- Learning Python: Powerful OO Programming, Mark Lutz.
3.- Python for Data Analysis: Data Wrangling with Pandas, Numpy, and Ipython, Wes McKinney.
4.- Numerical Analysis, Richard L Burden, Faires.
5.- Numerical Methods for Engineers and Scientists, Joe D. Hoffman.
6.- Numerical Methods for Engineers, Steven Chapra.
Lectures
- Lecture 1:
Intro to the Notebook, along with H-W:
Jupyter.
- Lecture 2:
Still analytics, with symbolic python:
Sympy 1,
Sympy 2.
- Lecture 3:
Visualization, with Matplotlib, Seaborn and D3:
Matplotlib,
Matplotlib 2,
Seaborn and D3.
- Lecture 4:
Calculus review. Notas
- Lecture 5:
Numerical Analysis. Notas,
Notebook.
Approximations and Round-off Errors.
- Lecture 6:
Roots. Notas,
Notebook.
- Lecture 7:
Optimization. Notas,
Notebook.
- Lecture 8:
Interpolation and Fitting. Notas,
Interpolation,
Fitting.
- Examples:
Hosing prices and Titanic. Notas,
Notebook.
- Lecture 9:
Differentiation and Integration. Notas,
Notebook,
Notebook 2.
- Lecture 10:
Ordinary Differential Eqns. Notas,
Notebook.
- Lecture 11:
Linear Systems. Notas,
Notebook.
- Lecture 12:
Partial Differential Eqns. Notas,
Notebook.
- Codes:
Lorentz, Double pendulum,
Wave 2D, Spectral wave 2D,
Lax Wave , Wave 1D,
Heat.