[Numpy-discussion] Tutorial topics for SciPy'09 Conference

Fernando Perez fperez.net at gmail.com
Tue Jun 2 01:21:03 EDT 2009

Hi all,

The time for the Scipy'09 conference is rapidly approaching, and we
would like to both announce the plan for tutorials and solicit
feedback from everyone on topics of interest.

Broadly speaking, the plan is something along the lines of  what we
had last year: one continuous 2-day tutorial  aimed at introductory
users, starting from the very basics, and in parallel a set of
'advanced' tutorials, consisting of a series of 2-hour sessions on
specific  topics.

We will request that the presenters for the advanced tutorials keep
the 'tutorial' word very much in mind, so that the sessions really
contain hands-on learning work and not simply a 2-hour long slide
presentation.  We will  thus require that all the tutorials will be
based on tools that the attendees can install at least 2 weeks in
advance on all  platforms (no "I released it last night" software).

With that in mind, we'd like feedback from all of you on possible
topics for the advanced tutorials.  We have space for 8 slots total,
and here are in no particular order some possible topics.  At this
point there are no guarantees yet that we can get presentations for
these, but we'd like to establish a first list of preferred topics to
try and secure the presentations as soon as possible.

This is simply a list of candiate topics that various people have
informally suggested so far:

- Mayavi/TVTK
- Advanced topics in matplotlib
- Statistics with Scipy
- The TimeSeries scikit
- Designing scientific interfaces with Traits
- Advanced numpy
- Sparse Linear Algebra with Scipy
- Structured and record arrays in numpy
- Cython
- Sage - general tutorial
- Sage - specific topics, suggestions welcome
- Using GPUs with PyCUDA
- Testing strategies for scientific codes
- Parallel processing and mpi4py
- Graph theory with Networkx
- Design patterns for efficient iterator-based scientific codes.
- Symbolic computing with sympy

We'd like to hear from any ideas on other possible topics of interest,
and we'll then run a doodle poll  to gather quantitative feedback with
the final list of candidates.

Many thanks,


More information about the NumPy-Discussion mailing list