Data Science with PythonYou want to learn Python for Data Science, but don’t find the time to visit synchronous courses regularly? Register for this free self-paced course and learn all you need to start with Python on your own schedule!
Andreas Blumauer | Ghislain Atemezing | Jakob Voß | Uma Balakrishnan | Stefan Peters | Haralabos Papatheodorou | Biswanath Dutta | Claudio Gnoli | Kristin Kolshus | Gerard Coen | Dan Michael O. Heggø | Ziyoung Park | So Young Yoon | Amadeu Pons | Andreas Ledl | Mihai Paunescu | Heather Hedden | Jeannine Beeken | Anja Gerber | Anne Schuchardt | Christian Rüter | David-Benjamin Rohrer | Erik Stubkjaer | Jarmo Saarikko | Monty Bitto | Ralph Hafner | Sabrina Gaab | Susanne Arndt | Volkan Çağdaş
Fast and Efficient Python Computing SchoolIn this school you will learn how Python code can be accelerated. A focus will be placed on numeric NumPy-like array computations. In addition, running these array computations on hardware accelerators, i.e., GPUs, will play a key role in this school.
The school is intended for young researchers - especially for PhD students - who regularly work with the scientific Python ecosystem. Requirements are good knowledge of the scientific Python ecosystem, basics of the C++ programming language are beneficial.
The school (see timetable) is split into five parts of which three are keynote lectures with hands-on tutorials. The other two comprise an opening talk and a coding group challenge for the participants.
A fee of 300€ will be charged for participation in the workshop.
