From 27th to 30th of August I participated at EuroScipy 2014, a European meeting about the use of the python programming language in sciences. Held in Cambridge, UK, EuroScipy brings together both developers of scientific tools and users from different scientific fields. Scientists have often to handle large amounts of data, and their formal education does not prepare them for that. This is especially true in remote sensing, as new sensor produce increasing amount of measurement data, a trend that is expected to continue in the following years.
In this context, the EuroScipy meeting was organized in two parts: a tutorial and a scientific one. In the tutorial track, experts and developers of scientific libraries provided hands-on training on core topics of scientific data analysis; some of the topics covered were machine learning, image processing and code optimization. The tutorials were structured around real-world problems that the scientist face in the day-to-day work.
During the scientific track, the speakers presented recent advances in core scientific libraries as well as their applications in wide range of scientific fields. The new developments presented included new data visualization libraries, machine learning algorithms and finite-element differential equation solvers. The application spanned many scientific fields such as biology, astrophysics and geosciences.
In total, EuroScipy 2014 was an useful and inspirational event. The hands-on training provided important programming skills, not covered by typical curricula, while the scientific presentations highlighted the benefits and important results that can be achieved through coordinated, open-source, development.