. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "M-GEO 5.0"@en . "23" . "5.0" . "140.0" . "10.0" . "The knowledge gained during the Scientific Geocomputing course is advantageous but not strictly\nnecessary to follow this course. Some self-study material will be provided through Canvas for students that\ndo not follow the Geoinformatics specialisation. Practicals on best practices in developing research code\nin Python will be performed at the beginning of the course to improve the necessary skills."@en . "Yes"@en . "No"@en . "2.0" . "Thanks to the digital, mobile and sensor revolutions, massive amounts of data are becoming available at\nunprecedented spatial, temporal, and thematic scales. This leads to the practical problem of transforming\nbig geodatasets into actionable information that can support a variety of decision-making processes. In\nthis respect, scalable geodata science workflows are not only key to process big geospatial datasets, but\nalso to share the obtained information and knowledge and to ensure the reproducibility of the results.\nTo handle and analyse massive amounts of potentially heterogeneous spatio-temporal data, GIS specialists\nand researchers need to 1) understand the particular characteristics of big geodata, 2) learn to work with\nscalable data management and processing systems, and 3) develop distributed but robust data mining and\nmachine learning workflows. This course aims to provide the necessary know-how by presenting theories,\nmethods, and techniques to build scalable solutions for handling and analysing big geodata, and develop\nthe necessary skills through hands-on practical and code-along sessions."@en . "Big Geo Data"@en . . "Big Geo Data"@en . "Big Geo Data"@en .