Syllabus

Syllabus

Prerequisites

  • Completion of the course GISc and Geodatabases or equivalent experience with:
    • R programming (tidyverse, basic data wrangling)
    • Vector data handling with sf
    • Raster data handling with terra
  • Basic familiarity with Git and GitHub
  • Familiarity with the command line

Assessment

The assessment of the course is based to 100% via the so called course work, which are your solutions to the tasks provided throughout the course.

What to Submit

You must submit solutions for all but one task assigned throughout the course — you have one “joker” that you may skip. Each submission will be checked for completeness (pass/fail). In addition, 2 tasks per student will be randomly selected for detailed grading using the criteria below.

The due dates are listed in the schedule.

Oral Review

In the final lesson, you will have the opportunity to present and discuss one of your solutions in a short individual conversation (~5–10 minutes). This is your chance to walk us through your approach, highlight what you learned, and reflect on your choices. This oral review will be graded pass / fail.

Grading Criteria

The randomly selected tasks are evaluated on three dimensions:

Criterion Weight Description
Correctness 40% Code runs without errors and produces the expected output
Documentation 30% Clear explanations of your approach, code is readable and commented where necessary
Reflection 30% Discussion of limitations, alternatives considered, or lessons learned

Use of AI Tools

You may use AI assistants (ChatGPT, GitHub Copilot, Claude, etc.) to support your work. However:

  • Your submission must reflect your understanding
  • The documentation and reflection sections are where you demonstrate this

As a general rule, the use of generative AI systems must be declared (based on der Z-RL-Guidelines AI in assessments, 01.04.2023).

Use of generative AI systems in graded assignments Graded assignments are a type of assessment which, unlike examinations, are completed over a longer period of time that generally exceeds four hours. They mostly have an individual character in terms of the solutions provided and are not supervised. The use of generative AI systems for graded assignments reflects a natural and expected approach towards digital tools by students and continuing education participants and is an expression of their digital competence and modern working methods. However, to ensure that their personal contribution can be assessed, and in the interests of academic integrity, the use of generative AI systems must be made as transparent as possible. The share or extent of the contribution made by generative AI systems to the creative output generated by students and continuing education participants in compiling their graded assignments must be recognisable to third parties. In principle, there is therefore an obligation to declare all generative AI systems that influence a graded assignment in terms of its content. The Annex governs the aforementioned declaration obligation in detail. The provisions contained therein are subsidiary 1 in nature.

Footnotes

  1. The provisions specified in the Annex thus serve as an “alternative” if no others have been specified.↩︎