Hacking for Science Is Back!
By popular demand, we’re bringing the Hacking for Science course back to the D-MTEC PhD program in fall 2024. The course will open to guests from all departments as well as interested guests. We are currently updating the course website, stay tuned and subscribe to our newsletter in the meantime.
To learn more about the course, including an initial schedule, check our blog post on the h4sci project.
Feedback, Questions, Ideas?
Contact us.
Minna Heim
heim [ at ] kof.ethz.ch
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Hacking for Science, ETH PhD program
Hacking for Science is a highly interactive, online course providing non-computer science students valuable big picture guidance and teaching hands-on software development skills. The course aims to help researchers of all fields who embrace programming as part of their approach to working with data. Hacking for Science is part of D-MTEC offering for PhD students but has benefited from guests from academia as well as the public and private sector.
Hacking for Science ETH Course Catalog | Research Software Engineering - A Guide to the Open Source Ecosystem | 2022 KITE Award nomnination
Course Learning Personas
Lars Linux hacks on Linux, Docker, Kubernetes, Python R and Julia since he was 15 years old. Internet & social media native finds his solutions in stackoverflow and with the help of LLMs.
Pain points: he feels everyone throws mickey mouse problems at him and his collaborators can’t even use git properly.
Bruno Berufseinsteiger is a 20-something year old, who works in the private sector in an entry-level role at a SME company with a lot of freedom but few resources (i.e. he can/must teach himself new skills (programming, data analytics))
Pain points: course requirements and format must match his busy lifestyle and he needs to see immediate benefits for his work.
Quinn Quantitatives Ph.D. is a quantitative Ph.D. with some previous experience in coding, given her quantitative background she is not afraid of math and statistics. She is looking to improve her coding skills and learn how to learn to work with other non-quantitative researchers.
Pain points: if the course progresses too slowly
Peter Ph.D. is a Ph.D. student in the social sciences, he has some experience with coding but is not a computer scientist. He is looking to improve his coding to improve his data processing andcollaboration for his research
Pain points: if the course is not applied to research conditions