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General reading for the class
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******
Python
******

If you are new to Python, we recommend starting off with the excellent
`Introduction to Python`_.

There are a good set of `resources
<http://www.introtopython.org/resources.html>`_ via the "Resources" link on
that site.  We especially like the Google course, if you like to watch videos
to go with your exercises.

Here are some good sites that help you practice writing Python:

* `CodingBat <http://codingbat.com/python>`_ by the same author as the Google
  Python course;
* `Code Academy Python track <http://www.codecademy.com/tracks/python>`_
* `SingPath <http://singpath.com>`_

**********
Background
**********

* `Nature article on Python in science
  <http://www.nature.com/news/programming-pick-up-python-1.16833>`_. This is a
  recent news article about the benefits of using Python for scientific
  programming.
* `Essay by Peter Norvig <http://norvig.com/21-days.html>`_.  `Peter Norvig
  <http://en.wikipedia.org/wiki/Peter_Norvig>`_ is director of research at
  Google.  The essay explains that, to learn programming, and therefore how to
  do computational science well, takes a long time.  We plan to teach you the
  steps so that you can continue learning computing for the rest of your
  career.
* Greg Wilson's article on scientific computing: Wilson, Greg, et al. `Best
  practices for scientific computing.
  <http://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1001745>`_
  PLoS biology 12.1 (2014): e1001745.  Greg Wilson and others set out the
  standard set of good habits that reduce errors and increase efficiency for
  scientific computing.
* Donoho, David L. 2010. An invitation to reproducible computational research.
  Biostatistics 11, 385–388.
  http://biostatistics.oxfordjournals.org/content/11/3/385.full . David Donoho
  is a professor in the Stanford statistics department who has long championed
  the need to make our work available to others in a form that lets them
  reproduce our results.

.. include:: links_names.inc