Thanks to the digital, mobile and sensor revolutions, massive amounts of data are becoming available at
unprecedented spatial, temporal, and thematic scales. This leads to the practical problem of transforming
big geodatasets into actionable information that can support a variety of decision-making processes. In
this respect, scalable geodata science workflows are not only key to process big geospatial datasets, but
also to share the obtained information and knowledge and to ensure the reproducibility of the results.
To handle and analyse massive amounts of potentially heterogeneous spatio-temporal data, GIS specialists
and researchers need to 1) understand the particular characteristics of big geodata, 2) learn to work with
scalable data management and processing systems, and 3) develop distributed but robust data mining and
machine learning workflows. This course aims to provide the necessary know-how by presenting theories,
methods, and techniques to build scalable solutions for handling and analysing big geodata, and develop
the necessary skills through hands-on practical and code-along sessions.