Jupyter Notebooks
Contents
Jupyter Notebooks¶
Within the fourth session we’ll finish the virtualization section, that is exploring one aspect that might seem adjacent to utilizing python within research workflows but is actually a crucial component: computing environments. Don’t worry: this will also entail hands-on programming, just not directly in python. The things covered in this section will introduce central concepts for our subsequent endeavors in this session. More precisely, we’ll explore jupyter notebooks as one form of interacting with your computer, specifically python. Getting a good hang of this will be important as most of our python endeavors will happen within jupyter notebooks and they are a staple in research workflows.
Topics 💡👨🏻🏫¶
In the following you’ll find the objectives and materials for each of the topics we’ll discuss during this session.
Jupyter notebooks¶
During the previous session we had a look at different ways of communicating with our computers. Generally speaking we could either use a GUI or a CLI. In more detail we also have different options to utilize the same programming language to communicate with our computer, e.g. the shell, jupyter notebooks and IDEs. After we already explored the shell a bit, we’ll now spend a closer look at jupyter notebook and how we can work with them. This is a central topic as the python introduction and data analyzes parts of the course will be conducted via jupyter notebook and you’ll have to hand in most assignments via them as well.
Objectives 📍¶
learn basic and efficient usage of the jupyter ecosystem & notebooks
what is Jupyter & how to utilize jupyter notebooks
Ask and answer questions
Have a great time
Materials 📓¶
Please see the rendered version of the jupyter notebook Introduction IV - jupyter notebooks in the ToC on the left.
tasks for subsequent meeting 🖥️✍🏽📖¶
optional/reading/further materials¶
Fernando Perez’ presentation on From interactive exploration to reproducible data science: Jupyter from NeuroHackademy 2020.