For the efficient handling of large gridded datasets, the concept of a datacube has received much attention in the last years. A datacube stores datasets with common axes (like latitude, longitude, time) in a neatly organized and easily accessible format, that e. g. allows fast data subsetting. Part of the convenience of a datacube originates from the data being stored in so called chunks; memory readable standardized subsets of the data that allow efficient data access and parallel processing. However, accessing data on disk also creates an overhead on computation time from input/output operations. Thus access to the data cube is only fast when the data is provided with suitable chunking aligned to the analysis in question: To illustrate, if data is chunked for time series access, it will be inefficient to access a map (one timepoint from each chunk), and vice versa if data is chunked for spatial processing, it will be inefficient to access a time series separated across many chunks. 
A proper chunking for efficient data reading and writing is especially important due to the following factors: The datasets that we have to handle in the earth system sciences are getting so large that they cannot be loaded in full into the working memory anymore. But when data have to be accessed on disk, the number of input/output operations should be minimized to avoid limiting computation speed. More and more data is also available in the cloud and needs to be made cloud compatible. Since data latency times become even more important in the cloud, the data is compressed. It is then very important to only decompress the data that is needed for the given analysis to optimize resources and computational speed. Both can be achieved by optimal chunking. 
This course provides interactive notebooks and explorable explanations to give the student an intuition of the usage of different chunking strategies and their influence on the performance of the computations. The material will be provided as interactive Jupyter notebooks, so that the learners could follow along, experiment and modify the code at their own pace. The notebooks will be made available in Binder, allowing interactive online code execution, to lower the entry barrier. The material will be provided in English. The target group is expected to have some programming experience and some experience in the work with gridded data.

Coding exercises are an important component of teaching data analysis in ESS today. Manually correcting assignments is often a heavy workload for exercise instructors. Students also often do not submit in time nor receive timely feedback. Therefore, automated code checking systems are promising for a wide range of teaching activities in ESS education. Several universities offer this service, based on different software architectures and infrastructures. Most of them are closed to their own students. In addition, the same basic content is often designed repeatedly at different universities, or even in different departments of a university. 
Nbgrader is an existing tool that supports creating and grading assignments for Jupyter Notebooks. It can be easily deployed in a conventional server, where student users can program Python code online in a Jupyter-Notebook interface and the exercise instructors can automatically grade their submissions. The Institute of Cartography and Geoinformatics at the University of Hannover has implemented such a system and successfully deployed it for teaching activities using Python as the programming language since 2021 for their courses such as GIS I - modeling and data structure, laser scanning data processing, SLAM and etc. 
The reuse of existing teaching materials is also of great importance. Within the education-oriented project ICAML - Interdisciplinary Center for Applied Machine Learning2 (Coordinated by co-applicant Martin Werner, BMBF funded 2018-2020), numerous Jupyter Notebook tutorials for machine learning topics in geospatial data analysis were developed and introduced to the community. While an interactive code checking process is important to further develop these tutorials and make these contents interactive and effortless to be included in future E-Teaching activities related to geospatial data analysis.

Changes in land use/cover are taking place worldwide on a variety of spatiotemporal scales and intensities. In this context, urbanization is a process that is affecting more and more areas of society and nature. Today, more than half of the world's population already lives in cities – in some European countries, the figure is up to 80%. Even though built-up areas account for only 2-3% of the land surface worldwide, their “ecological footprint” is enormous. Agricultural land, in particular, is being taken up for the expansion of settlement and transport areas. The analysis of such changes based on heterogeneous geospatial data sources is an important work step to estimate the future evolution of socio-ecological parameters such as migration, erosion, runoff patterns, biodiversity, etc. 
Regional case studies from “hot spots” of urbanization will be used to perform the necessary work steps to capture and quantify urbanization in the context of sustainable development (Sustainable Development Goal 11). Modern methods for accessing open geodata will be presented and the extraction of thematic information from volunteered geographic information (VGI), social media geographic information (SMGI) and earth observation (EO) data with Python will be taught. The learners can comprehend all work steps independently on their own computer. Basic knowledge of digital image processing and Geographic Information Systems is required.