You will be working with Jupyter notebooks, brought together as a GitBook (https://jupyter.org). Jupyter is a web-based computing environment, often used with the Python programming language, but other languages are also available. Jupyter notebooks contain both computer code and rich text elements, including visualisations, maps or interactive elements. Content is shared within cells, which are the building blocks of notebooks for both code and text. This introduction is the only notebook that does not contain code. Structured into cells, notebooks are human-readable as well as executable documents for data analysis. You can read a research story and interact with its execution and the methodologies at the same time. Sometimes, the coding cells will already be prepared for you. At other times, you will need to find your own solution.

If you feel uncertain about the environment, there are many video tutorials online, like the following by Colt Steele: 

The videos also explain how to install the environment on your own machine. You don’t need to do this, as you can run the notebooks directly in either Binder (https://mybinder.org/) or Google Colab (https://colab.research.google.com/). 

Please, note you might need an account for these environments. Once you are set up, everything should run smoothly.

But if you want to run the environment on your own machine, you can go to https://www.anaconda.com/ and follow the instructions to install Python and notebooks from https://www.anaconda.com/products/distribution. We would not recommend this for beginners, though, as it requires some understanding of command lines, environments, etc. Check out https://docs.jupyter.org/en/latest/running.html. This shows how you download a file from GitHub: https://www.wikihow.com/Download-a-File-from-GitHub. In the beginning, it is better to focus on the Python programming. It’s hard enough.