Basic Tutorial:

* Tutorial title: Introductory Scientific Computing with Python

* Intended audience (difficulty level, experience required):

  Beginning programmers who have experience with some programming
  language. A knowledge of elementary programming concepts is essential.
  Audience should know how to edit text files comfortably.  We strongly
  recommend that attendees go through at least 7 chapters of the online
  tutorial on Python: http://docs.python.org/tutorial and ideally
  complete it.  It should take no more than one afternoon.

  Prior experience with Scilab/Matlab/octave or similar tools would be
  useful but not necessary.

* Prerequisites: What experience must attendees have in order to fully
  benefit from this tutorial?

  Basic usage of a computer and elementary computer programming along
  with experience with some form of numerical computing via
  scilab/octave/matlab/mathematica.  You should go through the official
  Python tutorial (or be comfortable with the Python programming
  language).  You should definitely have the recommended packages
  installed and your computer setup for this tutorial.

* Promotional summary (max. 100 words).

  At the end of this tutorial, attendees should be able to use the basic
  tools and libraries for Python-based scientific computing.  We imagine
  that an undergraduate/graduate engineering/science student would be
  able to *start* doing most of their basic computing tasks using
  Python.

* Detailed tutorial outline

  The session is entirely hands-on.  We focus on common tasks and
  introduce the Python language in the context of these common tasks.
  Here is a rough outline of what we propose to cover:

   - Introduction and Preliminaries.
   - Introduction to IPython.
   - Creating basic plots with matplotlib.
   - NumPy array basics: 1D arrays.
   - Reading data files.
   - More on NumPy: multi-dimensional arrays, slicing, elementary image
     processing, random numbers.
   - Using SciPy for Linear Algebra, FFT's, root finding and integrating
     ODEs.

Session wise slide breakup:

1. intro.pdf
2. prelims.pdf
3. ipython_plotting.pdf
4. saving_scripts.pdf
5. lists_arrays.pdf
6. numpy.pdf
7. more_numpy.pdf
8. scipy.pdf
9. exercises.pdf
10. notebook.pdf
11. mlab.pdf


* Attendee Requirements:

  A laptop is definitely recommended since this will be a hands-on
  session.  You will need to have Python (2.7/3.x would work fine),
  IPython (0.10.x), Numpy (1.x), SciPy (>0.5.x), matplotlib (any recent
  version).

  On Linux, Windows and Mac OS X it is easiest to install these by installing
  the Enthought Canopy.