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+Scientists spend more and 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 which are standard in the 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 become a standard tool for the
+programming scientist.
+This school is targeted at Master or PhD students and Post-docs from all areas of science. Competence in Python or in another language such as Java,
+C/C++, MATLAB, or R 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|introductory material]] **before** the course.
+We are striving hard to get a pool of students which is international and gender-balanced: [[archives#stats|see]] how far we got in previous years!
+You can [[applications|apply online]].
+[[faculty|Faculty]], [[faculty#organizers|organizers]], [[students|students]].
+==== Date & Location =====
+**2–7 September, 2019**. [[https://goo.gl/maps/3EfZ3ttXAGs|Camerino]], {{:flags:it.png|Italy}} Italy
+==== Program =====
+  * Version control with git and how to contribute to open source projects with GitHub
+  * Tidy data analysis and visualization
+  * Testing and debugging scientific code
+  * Advanced NumPy
+  * Organizing, documenting, and distributing scientific code
+  * Advanced scientific Python: context managers and generators
+  * Writing parallel applications in Python
+  * Profiling and speeding up scientific code with Cython and numba
+  * Programming in teams
+Also see the [[schedule|detailed day-by-day schedule]] and information about [[location|venue and travel]].
+==== Sponsors ====
+We are able to hold this year's edition of the Python School thanks to the generous support of
+[[http://www.fondazionetim.it/|Fondazione TIM]]. Their contribution reflects their commitment
+to social enterprise and their mission to promote a culture of change and digital innovation,
+with particular focus on integration, communication and economic and social growth.
+==== Materials from previous years ====
+See the [[archives]].