. . . "Interpret the analytical results and demonstrate their reproducibility."@en . . "Interpret the analytical results and demonstrate their reproducibility."@en . . . "Prepare and maintain a code repository."@en . . "Prepare and maintain a code repository."@en . . . "Design scalable workflows that run in the cloud, and consider options for efficient computing."@en . . "Design scalable workflows that run in the cloud, and consider options for efficient computing."@en . . . "Explain to peers the fundamentals of big geodata processing."@en . . "Explain to peers the fundamentals of big geodata processing."@en . . . "Compare various big geodata solutions."@en . . "Compare various big geodata solutions."@en . . . "Create the required data management and analytical workflows to execute a big geo-data project."@en . . "Create the required data management and analytical workflows to execute a big geo-data project."@en . . "https://ltb.itc.utwente.nl/page/792/concept/152746" . . "Computational workflows"@en . . . . "https://ltb.itc.utwente.nl/page/792/concept/152767" . . "Big geodata"@en . . . . "https://ltb.itc.utwente.nl/page/792/concept/152835" . . "Open science."@en . . . . "https://ltb.itc.utwente.nl/page/792/concept/152853" . . "Machine learning"@en . . . . . . . . . . . . . . "https://ltb.itc.utwente.nl/page/792/concept/152867" . . "Reproducibility"@en . . . . . . "https://ltb.itc.utwente.nl/page/792/concept/152893" . . "Data-driven modelling"@en . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "The Master’s Programme Geo-Information Science and Earth Observation (M-GEO) is a two-year academic curriculum at MSc level, taught fully in English, dedicated to understanding the earth’s systems from a geographic and spatial perspective. The field of Geo-information Science and Earth Observation has, in recent years, witnessed fast scientific and technological developments. As a result, geographic information has become a vital asset to society and part of our daily life. The ubiquitous production and availability of spatial data require cloud computing and new technology to turn the increasing volume of ‘big data’ to good use. The growing range of global challenges, from climate change and resource depletion to environmental pollution and pandemic diseases, that our society and in particular the more vulnerable populations on our planet are facing, increases the demand for academic professionals who have the ability, attitudes and skills to design solutions that are sustainable, transdisciplinary and innovative with positive societal impacts. Our education focuses on addressing these global problems by means of advanced geo-information and earth observation applications."@en . "Master’s Programme Geo-Information Science and Earth Observation (M-GEO)"@en . . "Master’s Programme Geo-Information Science and Earth Observation (M-GEO)"@en . . . . "Python"@en . . . "Course"@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 Proce