The Advanced Scientific Programming in Python (ASPP) summer school has had 10 extremely successful iterations in Europe. Now, thanks to the INCF, we will be holding its first iteration in Australia, to cater to the Asia Pacific region. (Note: the original ASPP will still take place in Europe next Northern summer; this is a fork of that school.)
- The workshop runs January 14-21 at the Melbourne Brain Centre, University of Melbourne, Australia
- Topics include: git, contributing to open source software with github, testing, debugging, profiling, advanced NumPy, Cython, data visualisation.
- Hands-on learning using pair programming
- Free to attend (but students are responsible for travel, accommodation, and meals)
- 30 student places, to be selected competitively
- Application deadline is Oct 31, 2017, 23:59 UTC.
- Website: https://melbournebioinformatics.org.au/aspp-asia-pacific
- Apply: https://melbournebioinformatics.org.au/aspp-asia-pacific/applications (make sure you read the FAQ on that page)
Scientists spend increasingly more time writing, maintaining, and debugging software. While techniques for doing this efficiently have evolved, only few scientists have been trained to use them. As a result, instead of doing their research, they spend far too much time writing deficient code and reinventing the wheel. In this course we will present a selection of advanced programming techniques and best practices that are standard in industry, but especially tailored to the needs of a programming scientist. Lectures are devised to be interactive and to give the students enough time to acquire direct hands-on experience with the materials. Students will work in pairs throughout the school and will team up to practice the newly learned skills in a real programming project — an entertaining computer game.
We use the Python programming language for the entire course. Python works as a simple programming language for beginners, but more importantly, it also works great in scientific simulations and data analysis. We show how clean language design, ease of extensibility, and the great wealth of open source libraries for scientific computing and data visualization are driving Python to becoming a standard tool for scientists.
Who is eligible?
This school is targeted at Master/PhD students and postdocs from all areas of science. Competence in Python or in another language such as Java, C/C++, MATLAB, or Mathematica is absolutely required. Basic knowledge of Python and of a version control system such as git, subversion, mercurial, or bazaar is assumed. Participants without any prior experience with Python and/or git should work through the proposed introductory material before the course.
We have strived to get a pool of students that is international and gender-balanced, and have succeeded, with gender parity in the last four schools.
If you have any questions, contact firstname.lastname@example.org