2.1 – Jupyter Notebooks

Rather than the more traditional Python code files you may already be familiar with, working in JupyterHub means working with so-called Notebooks. These are a type of interactive document which allow you to work with a variety of data formats all in one place. It is easy to switch between text (including Markdown or LaTeX), images, code (in Python, R, or even Julia), and any visualizations your code may produce.

If you’ve never worked with Jupyter Notebooks before, why not take a look at the Jupyter Community’s own introduction? Their introduction starts as a Jupyter Notebook in their own JupyterHub environment, which is very similar to the one hosted by LET and will give you a good idea of what to expect.

For some inspiration on how to use JupyterHub in your course, you might also look at this recording of a refresh teaching event on Computational Competencies in Spring 2022. The contributions by Mauro Werder and Greg De Souza show usage scenarios of Jupyter Notebooks in their courses.

2.2 – The JupyterHub Interface

To understand JupyterHub in more detail, including folders, different files, programming, markdown and so on, please consult the official documentation of the JupyterHub interface.

Some important notes about the JupyterHub:

• Initially your personal JupyterHub environment will be empty. It will be consistent across sessions which means, every time users come back to their personal JupyterHub, all the files are still around and can be worked on again.
• JupyterHub files are not shared among users. It is not possible to collaborate on the same files between different users. This said, even if you set up some basic structure and Jupyter Notebook files for your course, this initially can’t be seen by the students. If you want to share your files, you either have to distribute them through Moodle assignments or use a git repository, which will be explained further on.