Minor Geospatial Data Science
Academic year 2024-2025, semester 1a and 1b
Offered by: Faculty of Spatial Sciences
ECTS: 30
Coordinator: dr.ir. S.G. Weitkamp
Maximum number of applications: 35
Introduction
Explore the world through data! Spatial information is crucial for understanding and addressing major societal challenges. From predicting climate change impacts to unraveling the spatial inequalities of wellbeing, these complex issues benefit from a multidisciplinary approach. By combining spatial and computational thinking, we can use the power of spatial data to find sustainable solutions.
The University of Groningen is excited to offer a cutting-edge minor in Geospatial Data Science, starting from the academic year 2024-2025. This interdisciplinary program is designed to equip you with the skills to analyze and interpret spatial data using advanced computational tools to tackle important global societal challenges.
Why choose this minor?
- Real-world impact: Work on a challenge-based project that addresses real societal issues in the Northern Netherlands, such as sustainable urban planning, renewable energy distribution, and social inequalities in mobility.
- Interdisciplinary learning: Bridging your own discipline with spatial science, computer science, and statistics, this program offers you a unique blend of knowledge and skills, preparing you for a career in a wide range of professions.
- Practical skills: Gain hands-on experience in spatial data analysis, visualization, and the application of programming skills to a variation of big spatial datasets.
Programme structure
- ECTS Credits: 30 EC
- Semester 1a and 1b
- Language of Instruction: English
- Participation Limit: Maximum 35 students
Prior to the beginning of the minor, you write a starting document in which you describe your personal learning objectives. In Semester 1a, you participate in an introductory course on Geospatial Data Science. Additionally, you select two skills courses of combined 10 ECTS from a range of options. In Semester 1b, you apply your spatial and computational skills, along with your disciplinary knowledge, to a collaborative real-world challenge-based project. See below for the more detailed programme. Starting 1 July you can view this and more details in Ocasys and the schedule of the courses on rooster.rug.nl.
| Course code | Course name | ECTS | Semester | Compulsory / Elective | Short description |
|---|---|---|---|---|---|
| GEIGSDS | Introduction to Geospatial Data Science | 5 | 1a | Compulsory |
Welcome to the introductory course on Geospatial Data Science (GDS), where we delve into the dynamic intersection of geography and data science. This course spans seven engaging lectures, designed to provide students with a solid foundation in geospatial data science. You'll gain valuable knowledge and insights into its relevance and applications across diverse disciplines. |
| GEASDA | Advanced Spatial Data Analysis | 5 | 1a | Elective |
In this course students further develop their spatial and computational thinking skills and learn to apply these with the use of new geospatial technologies to address spatial problems. More specifically, in this course you will learn the basics of programming in python, integrating python with QGIS, and how you can effectively use these in relation to spatial problems. |
| LPX065B05 | Introduction to GIS | 5 | 1a | Elective |
Almost all kinds of data can be linked to a location in one way or another, even though the conceptual difference between space and place has methodological implications. What contributions can spatial technologies such as Geographic Information Systems (GIS) make to spatial science? What are the challenges at stake? What are the new trends? During this module, students learn how GIS help address a variety of questions, and they are acquainted with the use of geographical and spatial data as a source for research and management within the different subfields of Spatial Science. |
| GEMLGS | Machine Learning in Geospatial Data Science | 5 | 1a | Elective |
The course introduces the fundamental concepts and techniques of machine learning (ML) within the context of geospatial data science. You will learn how to apply basic ML algorithms using Python to analyse and interpret geospatial datasets. Key topics include data preprocessing, regression, classification, clustering, and dimensionality reduction. The course emphasizes practical skills through hands-on exercises, integrating Geographic Information Systems (GIS) tools with machine learning workflows. |
| UCGMIN01 | Programming in Python | 5 | 1a | Elective |
As the computers have become prevalent both in academia and industry, it is important for the professionals to have “computational thinking” as a core competency. The students across many disciplines can greatly benefit from understanding the underlying principles of computing and gaining basic programming skills. This course introduces the fundamentals of programming including data types, control structures, algorithm development, functions and designing/implementing/debugging simple programs via the Python programming language. |
| CFMINGDS01 | Visualization of Spatial Data | 5 | 1a | Elective |
Data visualization is becoming an increasingly important format for communicating in almost every professional discipline. The ability to communicate and share information through storytelling with visual content is a powerful skill, as visualizations make it easier for the human brain to understand and pull insights from. Visualizations are more memorable and showing the data in the form of infographics makes it easier to identify patterns, trends and outliers in large data sets and aid faster decision making. Visualizing geospatial data can function as a tool to increase operational efficiency as it allows to display data on regions, communicate distance and direction and show spatial patterns. However, if visualizations are not crafted carefully, they can be a source confusion and are prone to foster misrepresentation. |
| GEIAGSC | Interdisciplinary Approaches to Geospatial Challenges | 15 | 1b | Compulsory |
This course provides students with the opportunity to apply their geospatial data analysis skills in a practical, challenge based setting. Students will work in groups on a research / analysis project in collaboration with an external client. |
Learning experience
- Foundation: Build your knowledge base with an introductory course in geospatial data science.
- Skill Courses: Choose two electives from courses like GIS basics, programming in python, machine learning with geodata, and data visualization to enhance your technical skills. You work mostly in small groups on collaborative assignments.
- Challenge based Project: Apply your skills and the knowledge from your own discipline in a comprehensive interdisciplinary project that involves real-world problems, provided by local organizations in the Northern Netherlands. You work in a group of maximum five students.
Course registration
To register for the minor in Progress, navigate to the first menu item labeled "Minors" and select "Spatial Sciences". For detailed information on registration deadlines and additional requirements, please visit the Minor website. For the academic year 2024-2025, registration opens on May 24, 2024, at 12:00 PM and closes on July 5, 2024, at 11:59 PM.
You must register for the course units separately at the Faculty of Spatial Sciences - Minor Geospatial Data Science in the Progress Portal. The course registration for semester 1a opens on 1 July and closes on 30 August 23.59 pm. The course registration for semester 1b opens on 16 September and closes on 13 October, 23.59 pm.
Admissions
- Open to: All third year bachelor students of the University of Groningen.
- Prerequisites: Candidates are expected to have the following competencies prior to enrollment:
- A solid understanding of basic statistical concepts and techniques, such as learned in course CFBDS07A05 or GESTAT1, and the ability to apply basic statistical methods to analyze and interpret data.
- Either basic skills in handling spatial data (e.g. using GIS software) OR basic programming skills (e.g python, R, or comparable programming language)
- Balance: to ensure a diverse and interdisciplinary cohort, a minimum number of students of the alpha, beta and gamma faculties are admitted.
Preparation
Once you have been registered to the program, you are required to prepare a Starting Document before the start of the minor. In this document, you should outline your motivation for joining the program and include proof of your existing skills in spatial analysis or computation, and statistics. Additionally, you specify your personal learning objectives, which should align with the program's learning outcomes. You also indicate which skills courses you are interested in enrolling in and the challenge based projects that have your interest. Submitting this document on time will ensure a smooth and efficient start to the minor program. More detailed information will be provided after you have been admitted to the minor programme.
You are expected to bring a laptop that you will use during computer practical sessions.
For more information, contact the minor coordinator, Gerd Weitkamp: s.g.weitkamp@rug.nl.
| Last modified: | 18 July 2024 09.04 a.m. |