We hold regular Code & Data Clinic sessions using Zoom where researchers can stop by to get help with their own source code. Although a formal registration is not necessary, a short email notification with a brief explanation of the type of assistance required might help us to assist you more efficiently. The next Code & Data Clinics will be announced by email. section](../../news).
During a Code Clinic experienced programmers will look at your code and may
- review your code and make suggestions how you could improve it in many respects as
- suggest ways to speed up your program or make it run with less memory
- advice you on how to make it more readable, robust, maintainable and reusable
- help you to find bugs or to solve other programming related problems
- show you how to use the euler computing cluster and how to port your code to euler.
This was just a list of examples, contact us for whatever question you have about programming.
So if your specific problem does not exactly fit to the examples above or you are just curious about programming and never programmed before it might be worth to drop in.
In addition to the Code Clinics, which are primarily focused on programming questions, we also provide help in problems related to data analysis and data science in general.
For example, our staff competent in fields as computer science, mathematics and machine learning can help you
- to adapt known algorithms for specific purposes.
- to use algorithms which are mentioned in scientific publications but are not yet available as libraries for end users.
- to find appropriate algorithms for your data analysis.
- to pick the right machine learning methods for your data.
We provide courses and workshops on various problems of scientific computing that we develop based on the needs of our users.
The upcomping courses will be announced in our news section.
|openBIS & ETH RDH Trainings||4 hours|
|Research Data Management Workshop Series||5-8 half-day workshops|
|ETH RDM Summer School (with ETH Library)||5 days|
|High Performance Computing for Genomic Applications||1.5 days|
|Getting started with the scientific cluster||5 hours|
|Parallel programming with MPI/OpenMP||6 days|
|Introduction to Python||3-4 days|
|Introduction to programming with Python||8-10 lectures|
|Workshop on writing fast(er) Python code||4 days|
|Introduction to Machine Learning with Python||4 days|
|Scientific Visualization using Python||2 days|
|Workshop on best practices in Programming (git, unit testing, clean code)||2 days|
Many of the course topics, we develop together with our users, as they fulfill an immediate need of a research group or project.