Cosmopy

Cosmology with Python

Cosmology with Python, IA-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.

Lecture 0:
Intro to the Notebook, along with H-W: Jupyter.
Lecture 1:
The basics: Jupyter 1. The not so basics: Jupyter 2.
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:
Scientific python: Scipy 1, Scipy 2.
Lecture 5:
Optimizacion: Notas.
Algoritmos bioinspirados: Paper.
The Genetic Algorithm: Paper, Thesis, More biblio, GA in influenza.
Codes: Code 1, Code 2, Code 3a, Code 3b, Code 3c, Cosmo.
Genetic Programming: GP.
Lecture 6:
An introduction to Markov Chain Monte Carlo: Paper.
Cosmological Parameter Inference with Bayesian Statistics: Paper.
MCMC Nb: Notebook.
Lecture 7:
Artificial Neural Networks: Paper.

Repaso de Python, etc.. : Tarea 1b.
Tarea 2: Resolver: 3a, 3b, 3c.
Numerical Calculations. : Tarea Numerica, GitHub. .
Files: file_f, file_g.
Analytical Calculations. :
HWs : Tarea a, Tarea b, Tarea c, Tarea d.
Cosmology. :
HWs : Tarea, Tarea b.