Learning by Doing

All the resources below were accessed on 18 August, 2015.

Python Scientific Installation

If you want to install Python, which you would need to run many of the resources in this section, and the IPython Notebook from Evan Bianco’s article, I recommend using the Anaconda free Python distribution, which can be used for both noncommercial and commercial purposes, and redistributed: http://continuum.io/downloads, and includes all the main scientific computing libraries you will need (Numpy, Scipy, Pandas, Matplotlib, etc). Once Anaconda is installed, you can start IPython Notebook from its launcher, and open an existing notebook or create a new one, or start IPython Notebook natively in your browser. In the latter case, say you have created a folder called

C:\PythonWork\Notebooks

you would open a Command Prompt terminal, and at the prompt C:\> you’d type:

>>> cd PythonWork\Notebooks
>>> IPython notebook

which will automatically start IPython Notebook on your browser.

Getting started with Python

Python Codecademy course:
https://www.codecademy.com/tracks/python

Diving into Python (Python from novice to pro), by Mark Pilgrim:
www.diveintopython.net/toc/index.html

Python Google course, complete with video lectures, notes, exercises and solutions, by Nick Parlante, Stanford University:
https://developers.google.com/edu/python/ and his collection of Python practice problems: http://codingbat.com/python

Learning more Python with games, challenges, contests, Hackathons

Invent Your Own Computer Games with Python, by Al Sweigart:
https://inventwithpython.com/chapters/

Hacking Secret Ciphers with Python, also by Al Sweigart:
https://inventwithpython.com/hacking/chapters/

The Python challenge:
www.pythonchallenge.com

Kaggle Python competitions:
https://www.dataquest.io/course/kaggle-competitions

Agile Geoscience 2015 Geophysics Hackathon in New Orleans (pre-SEG event):
http://www.agilegeoscience.com/events/2015/10/17/geophysics-hackathon-2015

Spacehack, a directory of ways to participate in space exploration:
http://spacehack.org/

Computer science courses (using Python)

Computer Science For All, Harvey Mudd College:
https://www.cs.hmc.edu/csforall/

with additional programming exercises:
https://www.cs.hmc.edu/twiki/bin/view/ModularCS1/CS5BlackMasterHWPage

How to Think Like a Computer Scientist: Interactive Edition:
http://interactivepython.org/runestone/static/thinkcspy/index.html

Python resources for scientific computing

Python Scientific Lecture Notes:
http://www.scipy-lectures.org/

Exploratory computing with Python, Delft University:
http://mbakker7.github.io/exploratory_computing_with_python/

Essentials of Machine learning algorithms, by Sunil Ray:
http://www.analyticsvidhya.com/blog/2015/08/common-machine-learning-algorithms/

Kaggle machine learning course:
https://www.dataquest.io/course/machine-learning

Implementing a Principal Component Analysis in Python step by step, by Sebastian Raschka:
http://sebastianraschka.com/Articles/2014_pca_step_by_step.html

Python resources for geoscience

Agile Geoscience’s several IPython Notebooks:
https://github.com/agile-geoscience/notebooks

SEG Geophysical tutorials:
http://wiki.seg.org/wiki/Geophysical_tutorials

Fatiando a Terra, a module for gravity, magnetics, seismic modeling:
http://www.fatiando.org/

Python for Geosciences:
https://github.com/koldunovn/python_for_geosciences

ObsPy, A Python Toolbox for seismology/seismological observatories:
http://docs.obspy.org/