We recently conducted a poll on Doodle, soliciting feedback on the preferred topics for the advanced track, which is meant to contain 2 days with 8 2-hour sessions focusing on one specific topic at a time. The table below shows the complete results, which I've only sorted for convenient viewing and anonymized (the raw Doodle output contains the names given by each person voting). If anyone would like the raw spreadsheet, just drop me a line.
The score was computed as #yes-#no (i.e., yes=+1, neutral=0, no=-1), from a total of 30 responses, and the results are in the table below, ranked from highest to lowest score. In my personal opinion, all the topics offered would have made for very good and interesting tutorials, but the point of asking for feedback is obviously to follow it to some degree, which we will now do.
I think it's worth noting --though not particularly surprising-- that the ranking roughly follows the generality of the tools: matplotlib and numpy are at the top, with finite elements and graph theory at the bottom. While I personally use NetworkX and love it, it's a specialized tool that for many probably offers no compelling reason to learn it, while pretty much every single numerical python user needs numpy and matplotlib. We are now in the process of contacting possible speakers for the top topics, and will communicate on the mailing list a final list of topics once we have confirmed speakers for all.
The score was computed as #yes-#no (i.e., yes=+1, neutral=0, no=-1), from a total of 30 responses, and the results are in the table below, ranked from highest to lowest score. In my personal opinion, all the topics offered would have made for very good and interesting tutorials, but the point of asking for feedback is obviously to follow it to some degree, which we will now do.
I think it's worth noting --though not particularly surprising-- that the ranking roughly follows the generality of the tools: matplotlib and numpy are at the top, with finite elements and graph theory at the bottom. While I personally use NetworkX and love it, it's a specialized tool that for many probably offers no compelling reason to learn it, while pretty much every single numerical python user needs numpy and matplotlib. We are now in the process of contacting possible speakers for the top topics, and will communicate on the mailing list a final list of topics once we have confirmed speakers for all.
| Yes | Neutral | No | Score | Rank | |
| Advanced topics in matplotlib use | 18 | 10 | 2 | 16 | 1 |
| Advanced numpy | 18 | 10 | 2 | 16 | 2 |
| Designing scientific interfaces with Traits | 15 | 11 | 4 | 11 | 3 |
| Mayavi/TVTK | 13 | 11 | 6 | 7 | 4 |
| Cython | 14 | 8 | 8 | 6 | 5 |
| Symbolic computing with sympy | 15 | 6 | 9 | 6 | 6 |
| Statistics with Scipy | 9 | 15 | 6 | 3 | 7 |
| Using GPUs with PyCUDA | 13 | 7 | 10 | 3 | 8 |
| Testing strategies for scientific codes | 11 | 11 | 8 | 3 | 9 |
| Parallel computing in Python and mpi4py | 12 | 8 | 10 | 2 | 10 |
| Sparse Linear Algebra with Scipy | 9 | 12 | 9 | 0 | 11 |
| Structured and record arrays in numpy | 8 | 14 | 8 | 0 | 12 |
| Design patterns for efficient iterator-based scientific codes | 9 | 7 | 14 | -5 | 13 |
| Sage | 8 | 6 | 16 | -8 | 14 |
| The TimeSeries scikit | 4 | 13 | 13 | -9 | 15 |
| Hermes: high order Finite Element Methods | 6 | 9 | 15 | -9 | 16 |
| Graph theory with NetworkX | 5 | 9 | 16 | -11 | 17 |
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