Spatial Data Science v jazyce Python / Spatial Data Science in Python (10752)
Basic information | |||||||||
| Charles University | |||||||||
| Spatial Data Science v jazyce Python / Spatial Data Science in Python | |||||||||
| admission procedure in progress | |||||||||
| Online | |||||||||
| English | |||||||||
| Spatial Data Science v jazyce Python / Spatial Data Science in Python | |||||||||
| Standalone course validated by the micro-credential. 1. Introduction to Python for Data Science 2. Open Data Science, Data manipulation in Python (pandas) 3. Spatial data (geopandas) 4. Spatial relationships (libpysal) 5. Exploratory spatial data analysis (esda) 6. Point patterns (pointpats) 7. Clustering (scikit-learn) 8. Raster data (xarray) 9. Interpolation (tobler, pyinterpolate) 10. Regression (statsmodels, mgwr) |
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| The course introduces data science and computational analysis using open source tools written in the Python programming language. The course supports students with little prior knowledge of core competencies in Spatial Data Science (SDS). It includes: - Advancing their statistical and numerical literacy. - Introducing basic principles of programming and state-of-the-art computational tools for SDS. - Presenting a comprehensive overview of the main methodologies available to the Spatial Data Scientist and their intuition on how and when they can be applied. - Focusing on real-world applications of these techniques in a geographical and applied context. The course revolves around data typically used in social geography, but its applicability is not limited to social geography. In practice, you will work more with vector data than rasters (although we cover those a bit as well) and often with data capturing various aspects of human life. The spatial data science concepts, however, are universal. |
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| Basic understanding of Python and foundational statistics (e.g.linear regression). SIS will be used for “enrolment” and registration of students. Level of attendance: min. 60%. |
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| After finishing the course, students will be able to: • Describe advanced concepts of spatial data science and use the open tools to load and analyze spatial data. • Explain the motivation and inner logic of the main methodological approaches of open SDS. • Critically evaluate the suitability of a specific technique, what it can offer, and how it can help answer questions of interest. • Apply several spatial analysis techniques and explain how to interpret the results in the process of turning data into information. • Work independently using SDS tools to extract valuable insight when faced with a new dataset. |
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 Příloha 1_Formular_MCI (1) (3).pdf, U Příloha 2_Formular_MCII (1).pdf, U |
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Accreditation | |||||||||
| 11000 - Univerzita Karlova | |||||||||
| 252/24 | |||||||||
| 29.5.2024 | |||||||||
| 29.5.2034 | |||||||||
Further detailed information | |||||||||
| 4 | |||||||||
| 50 (total number of hours) | |||||||||
| 30 hodin synchronní výuka + 20 samostudium + zpracování závěrečné práce (30 hours of synchronous teaching + a final assignment - a computational essay.) | |||||||||
| 1 | |||||||||
| Earth sciences (0532) | |||||||||
| Praktické předvedení (na místě s ověřením totožnosti) | |||||||||
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| 1.7.2024 | |||||||||
| 31.8.2024 | |||||||||
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| Institutional License | |||||||||
| Obsah mikrocertifikátu byl vytvořen ve spolupráci se zástupci firem O2 a ARCDATA PRAHA a Českého statistického úřadu. | |||||||||
Date and venue of the course | |||||||||
| 12.08.2024 | |||||||||
| 16.8.2024 | |||||||||
| 2023/2024 | |||||||||
| summer semester | |||||||||
| pouze online (link bude zaslán všem přihlášeným/ online, the link will be sent to all registered users | |||||||||
Information for applicants | |||||||||
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| 25 | |||||||||
| 15000 Kč / kurz | |||||||||
| 27.06.2024 | |||||||||
| 09.08.2024 | |||||||||
| Martin Fleischmann, M.Sc., Ph.D. | |||||||||
| martin.fleischmann@natur.cuni.cz | |||||||||
| Albertov 6, 128 00 Praha 2 | |||||||||
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Enrolment information | |||||||||
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| 1.7.2024-9.8.2024 | |||||||||