Python and data analysis basics
- Understand Git and GitHub
- Git primer by Brad Voytek
- Make a GitHub account, practice with a test repo.
- Further practice: this wiki repo, add something in the Markdown language, and submit a pull request (see Home for instructions on home to contribute to the wiki).
- You can either download a visual interface like GitKraken, or the good old command
- Understand what a virtual environment is
- Install Anaconda and create your first environment, including
pandas and seaborn.
- Learn the basics of Python
- Intro to Python, by Todd Gureckis
- Download a Python IDE (Integrated Development Environments). I like PyCharm (you can create a free educational account), other great options are VSCode or
Atom. If you’re happy with a simpler solution, you can use a text editor like Sublime + the trusted Mac OS Terminal (not sure about Windows).
- You can also access DataCamp learning resources with your Leiden Uni account.
- When setting up your project, make sure you’re running it in the right virtual environment to access all your packages.
- Think about the structure of data
For most projects (especially those using behavioral data), your laptop will be more than sufficient to run Python
. If you need more heavy lifting, there are a few options:
- ALICE supercomputer @ Leiden Uni
-Get an account
- LISA / Cartesius clusters @ SurfSara
- Apply through NWO. Very well managed, but since Leiden doesn
’t have a contract with SurfSara you have to apply to extend your account every year.