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n.BA.AD.STDS.24HS (Spatio-temporal Data Science) 
Module: Spatio-temporal Data Science
This information was generated on: 20 March 2025
No.
n.BA.AD.STDS.24HS
Title
Spatio-temporal Data Science
Credits
2

Description

Version: 1.0 start 01 August 2024

 

Study Programme Applied Digital Life Sciences
Regulations Applicable RPO, 29 January 2008, School of Life Sciences and Facility Management Academic Regulations, 15 Dec. 2009, Annex for the Bachelor of Applied Digital Life Sciences degree programme
Module Type  
X Compulsory Module    Elective Module    Optional Module
Planned Semester 6th Semester
Module Coordinator Nils Ratnaweera
Telephone / E-Mail +41 (0)58 934 55 63 / nils.ratnaweera@zhaw.ch
Lecturer(s),
Speaker(s),
Associate(s)
  • Nils Ratnaweera
  • Patrick Laube
  • Martin Schüle
Entrance Requirements
Learning Outcomes and Competencies Technical Skills
 
Students...
  • ... are knowledgeable on a wide array of specialized geodata formats (the classics and the cutting edge) and their specific use cases, requirements, strengths and weaknesses
  • ... can process and analyse medium to large geospatial datasets with the support of geospatial scripting software.
  • ... can query comprehensive spatial databases and know how to optimize these queries for speed and efficiency
  • ... understand advanced concepts of geospatial data, for example spatial indexing, topology and the dimensionally extended 9 intersection model, simple features, scales of measurement, modifiable areal unit problem
 
Transferable Skills
 
Students...
  • ... can divide complex problems into multiple smaller and manageable tasks
  • ... can research and implement solutions to problems that they haven’t previously encountered
  • ...can acquire, analyse and criticize publicly available data
Module Content This comprehensive course is designed to equip participants with advanced skills in Spatio-temporal Data Science, covering essential topics to navigate and excel in the evolving landscape of geospatial data processing and analysis. Each topic combines theoretical knowledge with hands-on practical exercises, ensuring participants are well-prepared to tackle real-world challenges in the field.
 
  • Fundamentals of Advanced Data Handling
    • Introduction to the classic and the cutting-edge geospatial data formats
    • Hands-on exercises for efficient data processing techniques
    • Case studies showcasing real-world applications
  • Spatial Optimization Techniques
    • Exploration of spatial indexing methods
    • Practical implementation for enhanced data processing performance
    • Strategies for optimizing spatial queries and analyses
  • Integration with Big Data Technologies
    • Overview of big data technologies in the context of geospatial processing
    • Integration strategies for seamless analysis with large-scale datasets
    • Practical applications and use cases
  • Machine Learning for Spatiotemporal Patterns
    • Introduction to machine learning algorithms tailored for geospatial data
    • Hands-on exercises for pattern identification and trend analysis
    • Real-world examples illustrating the impact of machine learning in spatiotemporal contexts
  • Geospatial APIs and Data Science Workflows
    • Understanding the role of Geospatial APIs in data science
    • Practical exercises on leveraging APIs for enhanced data access and analysis
    • Integration of Geospatial APIs into comprehensive data science workflows
Follow-up Modules -
Methods of Instruction 
  • Theoretical principles and concepts are taught through lectures, seminars and reading assignments.
  • Practical skills are acquired through guided exercises, using existing online resources if available.
  • Project work as individual or partner work, coaching by the teachers during project implementation.
Digital Resources
  • Moodle
  • Learning and instructional videos
  • Online tutorials and user platforms
  • Case studies
Lesson Structure / Workload  
 Contact Hours 28
 Guided Self-Study 14
 Independent Self-Study 18
 Total Workload 60
Classroom Attendance Attendance is encouraged, but not enforced with attendance list
Assessment Course work 100%
Language of Instruction  Englisch
Comments -

 

Course: Spatio-temporal Data Science
No.
n.BA.AD.STDS.24HS.V
Title
Spatio-temporal Data Science

Note

  • No module description is available in the system for the cut-off date of 20 March 2025.