30 ECTS Minor SDS

Der SDS-Minor kann in 3 Semestern studiert werden, wie durch die verschiedenen Farben im Musterstudienplan angezeigt.

Das Minor-Studienprogramm Spatial Data Science (30 ECTS Credits) umfasst folgende sechs Pflichtmodule mit je 5 ECTS Credits:

Pflichtmodule (30 ECTS Credits)

GEO 113 - Fernerkundung und Geographische Informationswissenschaft I

"Earth Perspectives – Introduction to Geographic Information Science and Remote Sensing"

• Herbstsemester, 5 ECTS, Vorlesung mit Übungen

In this course you will be introduced to concepts, data, methods and the interpretation of spatial data in geography. The course will open by introducing key concepts related to the conceptual modelling and representation of spatial-temporal data. You will learn how remote sensing and surveying methods can collect data from the ground, the air and space and be aware of the strengths and weaknesses of different sensors and methods. We will explore how spatial data can be mapped and analysed, focussing on the built and natural environments and the use of spatial data to both measure and understand processes in the real world. We will give examples of both applied and research applications of spatial data in diverse domains such as building cadasters, humanitarian relief, global land cover change, animal behaviour and natural hazards. The course will be taught through lectures, introducing key concepts, theories, data and methods and practical exercises where you will have the chance to put these ideas into practice. It serves as a foundation for further courses in Geographic Information Science and Remote Sensing throughout your studies.

.

Link zum Vorlesungsverzeichnis

SDS 110 - Grundlagen zur Arbeit mit digitalen räumlichen Daten I

"Fundamentals of Spatial Data"

• Herbstsemester, 5 ECTS, Vorlesung mit Übungen

This Spatial Data Science (SDS) module paves the way to one of the most dynamic and impactful areas of geoscience.  In an era where data drives decision making, Spatial Data Science offers a unique lens through which we can explore and understand the world around us. Whether it's monitoring environmental change, managing urban growth or, predicting natural disasters, Spatial Data Science provides the tools and insights needed to tackle the pressing challenges of our time. This module is designed to spark your curiosity and equip you with the skills you need to excel in the rapidly evolving field of Spatial Data Science. You'll delve into the fascinating world of geospatial applications and discover how spatial data is collected, analysed and applied to solve real-world problems. From understanding the nuances of data lifecycles to mastering the principles of data quality and reproducibility, you'll gain a comprehensive grounding in the essential elements of spatial data science. The content of this course is further consolidated and expanded in the following spring semester (SDS 210) and autumn semester (SDS 320).

Das Modul SDS 110 findet erstmals im Herbstsemester 2025 statt.

GEO 123 - Fernerkundung und GIS II

"Introduction to Cartography and Geovisualisation"

• Frühlingssemester, 5 ECTS, Vorlesung mit Übungen

This module introduces basic terminology, concepts and principles related cartographic depiction and geovisualisation. The purpose and characteristics of the map as a model of visual communication of geographic phenomena and processes, the conversion of spatial information into a cartographic symbolic language, map interpretation, map projections, thematic cartography, and special forms of geovisualisation are covered. The labs complement the associated lectures using ArcGIS Pro and ArcGIS Online. Labs focus on central elements of the creation of maps including for example the visual variables, color schemes, data classification, cartographic generalisation, design and execution of multicolour online maps and map evaluation. Students work individually, in groups, and independently under the guidance of teaching assistants.

Link zum Vorlesungsverzeichnis

SDS 210 - Grundlagen zur Arbeit mit digitalen räumlichen Daten II

"Programming with Spatial Data"

• Frühlingssemester, 5 ECTS, Vorlesung mit Übungen

This module introduces programming with Spatial Data. It dives into the world of Python programming tailored for geospatial applications. In an era of rapid technological advancement, the ability to write and organise code to analyse spatial data is an essential skill for tackling pressing challenges in the geosciences and beyond. This hands-on course introduces you to the powerful tools and techniques needed to programmatically manipulate, analyse and visualise spatial data. From mastering Python basics to using specialised geospatial libraries such as GeoPandas, RasterIO and Matplotlib, you'll develop practical programming skills to solve real-world problems. The course also emphasises the importance of well-structured, reusable and reproducible workflows, with a focus on working with Jupyter and Git. You'll complete the course by working on individual projects that demonstrate your ability to develop programming solutions to spatial problems, preparing you for advanced applications in research and industry. This course builds on the foundations of SDS 110 "Fundamentals of Spatial Data" and is further consolidated and extended in the fall semester (SDS 320).

Das Modul SDS 210 findet erstmals im Frühlingssemester 2026 statt.

GEO 243 - Fernerkundung und Geographische Informationswissenschaft IV

"Raumanalyse mit GIS"

• Frühlingssemester, 5 ECTS, Vorlesung mit Übungen

This module expands the bases of GEO 113 and GEO 123 regarding the knowledge of methods, operations, and applications of Geographic Information Systems (GIS). The lecture portion (GEO243.1) introduces basic methods of spatial analysis and their implementation in GIS, which are tested in practice in the associated exercises portion (GEO243.2).

Link zum Vorlesungsverzeichnis

SDS 320 - Anwendungskompetenzen digitaler Datenanalysen

"Spatial Data Analytics"

• Herbstsemester, 5 ECTS, Vorlesung mit Übungen

In this module students develop their own geoscientific project in which they will develop programmes based on existing or self-collected spatial data, carry out modelling and critically evaluate these tools and the results obtained. In addition to the practical work with digital tools, the collected data should be adequately documented and described (including quality analysis, metadata, etc.) in order to make it findable and reusable according to the FAIR principle and taking into account copyright and data protection. This course builds on the foundations of SDS 110 "Fundamentals of Spatial Data" and SDS 210 "Programming with Spatial Data".

Das Modul SDS 320 findet erstmals im Herbstsemester 2026 statt.