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 line.
• 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
  • The structure of data, by Todd Gureckis
  • Learn to work with the pandas and seaborn packages for data handling and visualization.

Computing resources

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.