• Use of Git and GitHub for managing scientific projects and code
• Jupyter notebooks as a tool for data analysis and publication
• Python programming language for data analysis
• Reproducible programming practices (any language)

The course consists of a few notebooks, some python code and data - and this readme. It resides in a GitHub repository, which is open to anyone, also after the course. For the workshop, we have created a virtual environment for data analysis on our server, brainimaging.labs.vu.nl. You will receive a username and login with which you can login to this environment. If, at the end of the workshop, you want to install jupyter on your own computer, we can help with that.

• After the first instruction, you will go to GitHub and create a user account there. After this first registration you can have GitHub convert your account to an education account, which enhances your account for free - but that's for later.
• Once you have a GitHub account and are logged in, you can go to the course repository and fork it - there's a button on the right top. Forking copies the repository over to your user account, but instead of just copying, it retains the possibility of automatically recombining your version and mine in the future.
• Now that you have your own version of the "repo", you have to log into the brainimaging.labs.vu.nl server. Each of you has an anonymous user account beginning with student_, and a password that I'm sending you by email.
• Once you're logged in, you're greeted by a user interface. at the right top, there's a dropdown menu that says "New" - click it, and find that you can create new notebooks, but also terminals from this menu. Create a new terminal.
• In this terminal, type the following command: git clone my_repository_URL, where my_repository_URL is the url of your clone of our GitHub repository. You can find this URL by pressing the green 'Clone or Download' button on GitHub. You may have to type in your GitHub username and password.
• After cloning, you will find a new folder on the server (go back to the previous screen). In it you will find a python_tutorial.ipynb; open it.

• jupyter-notebook-tutorial

• Git tutorials @ atlassian
• GitHub tutorials and guides

• Python tutorial
• conda (python environment installation) tutorial

• Matplotlib tutorials
• Seaborn tutorials (Seaborn is built on top of Matplotlib)
• Altair, alternative plotting in python

• SciPy Cookbook (Scientific Computing, basic stats)
• SciKit-learn tutorials (Machine learning)
• Pandas tutorials (data wrangling)
• Statsmodels intro (more complex stats)