Coronavirus information for participants
The onsite course and conference programme at EMBL has been paused until the end of June 2020.
We aim to continue offering our advanced training for the scientific community however we safely can. While some events have been cancelled, many have been rescheduled for a later date and others will be delivered as virtual events.
Registration is open for onsite courses and conferences starting after 1 July and for the virtual events. All registration fees for any events which don’t take place due to the COVID-19 disruption are fully refundable.
More information for participants of events at EMBL Heidelberg can be found here.
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Programme / Instructions
Programme and further instructions can be found here.
Computation is an integral part of today's research as data has grown too large or too complex to be analysed by hand. An ever growing fraction of science is performed computationally and many wet-lab biologists spend part of their time on the computer. Many scientists struggle with this aspect of research as they have not been properly trained in the necessary set of skills. The result is that too much time is spent using inefficient tools when progress could be faster.
This is a course for researchers in the life sciences who are using computers for their analyses, even if not full time. The target student will know a little bit of command line/programmatic computer usage, but not consider themselves an expert. A target student will have written a for loop in some language before, but will not know what git is (or at least not be very comfortable with advanced git usage).
- Introduction to Python scripting
- Introduction to the Unix shell and usage of cluster resources
- Version control with git and Github
- Pipeline management with SnakeMake
- Scientific Python & working with biological data
- Literate programming with Jupyter notebooks
This course aims to teach basic software skills and best practices to researchers in biology who wish to analyse data. The goal is to enable them to be more productive and make their science better and more reproducible.