. . . . "ITC Bok"@en . . "201900064" . "GIP_0001" . "5"^^ . "140"^^ . "10"^^ . "2022-11-10T23:00:00Z"^^ . "f2f" . "online" . "In this course, students will learn the fundamentals of big geodata processing. Then, they will be introduced (via lectures, demos and exercises) to various distributed big data solutions as well as the role of cloud computing. After that, they will work on a real-life problem involving a big geo-dataset. They will work in groups and create the necessary workflows to process the data. This requires programming skills and critical thinking to select the \"best\" algorithm and computational solution.\n\nIn this course, there will also be a strong emphasis on Open Science principles, with a focus on scientific reproducibility and triangulation. Lectures on archiving data and code will be provided too."@en . . . . . . . "Basic Programming skills ,The knowledge gained during the Scientific Geocomputing course is advantageous but not strictly necessary to follow this course. Some self-study material will be provided through Canvas for students that do not follow the Geoinformatics specialisation. You are advised to contact the course coordinator to discuss the materials' relevance for you."@en . . . . . . . . . "3"^^ . "1" . "1A " . . "2022-09-04T22:00:00Z"^^ . "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, geodata science workflows are not only key to processing big geospatial datasets but also to sharing the extracted information and knowledge and to ensuring the reproducibility of the results.\n\nTo handle and analyse massive and potentially heterogeneous amounts of spatio-temporal data, scientists need to 1) understand the particular characteristics of big geodata, 2) learn to work with scalable data management and processing systems, and 3) develop scalable and robust data mining and machine learning workflows. Hence, this course presents theories, methods, and techniques to build scalable solutions for handling and analysing big geodata."@en . "Big Geodata Processing"@en . . "Big Geodata Processing"@en . "Big Geodata Processing"@en . "https://ltb.itc.utwente.nl/page/792/concept/152893" . . "Data-driven modelling"@en .