Programme

You have heard of Jupyter Notebooks already? But you do not quite understand the hype about it? Then this short course is exactly for you. We will show you the beauty in working with Jupyter Notebooks and the entire Jupyter environment.

With Jupyter Notebooks you have your code, visualisation and documentation all in one place. Widgets allow the setup of interactive visualisations, where you can e.g. include leaflet maps into your notebooks.
JupyterLab and JupyterHub provide the right working environments to create and host your Jupyter Notebooks and collaborate with others.

This short course will introduce you to Jupyter Notebooks and give you practical examples how environmental data (meteorological data and satellite images) can be analysed. After a general introduction to Jupyter Notebooks, we will give you examples how you are able to access large volumes of meteorological and satellite data from data repositories, such as ECMWF, and cloud environments, such as the Copernicus Climate Data Store or Google Earth Engine. We will analyse and interactively visualise the data with Jupyter widgets. Towards the end, we will introduce you to JupyterLab and JupyterHub, to better understand the full Jupyter environment.

The course will be structured as follows:
- Jupyter Notebooks - Data analysis made simple
- Data access with Jupyter Notebooks from different data repositories
- Jupyter widgets - Make your data analysis interactive
- Jupyterlab, JupyterHub, … - Getting to know the Jupyter environment

This short course is hands-on and you can bring your laptop along. All exercises are designed to be easy to follow. The Jupyter Notebooks of this course will be made available after the course.

Public information:
This short course will introduce you to Jupyter Notebooks and give you practical examples how environmental data (meteorological data and satellite images) can be analysed. After a general introduction to Jupyter Notebooks, we will give you examples how you are able to access large volumes of meteorological and satellite data from data repositories, such as ECMWF, and cloud environments, such as the Copernicus Climate Data Store (CDS) or Google Earth Engine (GEE). We will analyse and interactively visualise the data with Jupyter widgets. Towards the end, we will introduce you to JupyterLab and JupyterHub, to better understand the full Jupyter environment.