Métodos Matemáticos y Análisis de Datos con Python
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- Python
Python
- Lecture 0, Python
Temario.
Tareas (60%), Examenes (20%), Proyecto (20%).
Horario (?), HWs (name, well organized, comments).
- 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:
Notebook.
Contains HW1, HW2.
- Lecture 2:
Types and operations:
Notebook.
Contains HW3.
- Lecture 3:
Strings and Lists:
Notebook.
Contains HW4.
- Lecture 4:
Tuples, Sets, Dics:
Notebook.
Contains HW5.
- Lecture 5:
The not so basic:
Notebook.
Contains HW6 and HW7.
Data.
- Examen
- Lecture 6:
Files and Matplotlib:
Notebook.
- Lecture 7 and 8:
Symbolic python:
Sympy 1,
Sympy 2.
HWs :
Tarea 8,
Tarea 9,
Tarea 10,
Tarea 11.
- Lecture 9:
Numpy:
Numpy 1,
Numpy 2,
Numpy summary.
HWs:
Tarea Numerica,
GitHub. .
Files: file_f,
file_g.
- Lecture 10:
More plotting:
Notebook.
- Lecture 11:
Intro a Scipy:
Notebook.
Interpolation: Nbk.
Optimization: Nbk.
Roots: Nbk.
Fitting: Nbk.
- Lecture 12 & 13:
Stats, Integrals and ODEs:
Notebook.
Integrals: Nbk.
ODEs: Nbk.
RK4: Nbk.
PDEs: Nbk.
Matrix: Nbk.
Fourier: Nbk.
- Lecture 14:
Seaborn, Bokeh and D3:
Seaborn and D3.
- Lecture 15:
Tortuga: 1py,
2py.
Animation: Codes.
- Lecture 16:
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 17:
An introduction to Markov Chain Monte Carlo:
Paper.
Cosmological Parameter Inference with Bayesian Statistics:
Paper.
MCMC Nb:
Notebook,
datos.
- Lecture 18 & 19:
Scrapping the web: ntbk.
Yellow pages: ntbk,
Yahoo's Finance: ntbk.
- Lecture 20 & 21:
Pandas: ntbk.
Writing & Reading I and II: ntbk 1,
ntbk 2.
Baby names: ntbk.
Cycling in Montreal: ntbk.
- Lecture 22:
NLTK: ntbk.
- Lecture 23:
Regression & Classification: ntbk.
Clustering: ntbk.
- Lecture 24:
ANN: ntbk.
Tareas
- Analytical Calculations. :
.