1. Assessment overview¶
SDS320 is assessed through continuous semester engagement and an individual programming project.
The final mark is based on three components:
| Component | Weight |
|---|---|
| Course engagement | 40% |
| Project report & repository | 30% |
| Project presentations | 30% |
The project integrates the concepts, methods and coding practices developed throughout the course. It is your opportunity to demonstrate that you can design, implement and communicate a reproducible solution to a spatial data problem using Python.
2. Passing the course¶
To pass the course, you must achieve a total weighted score of at least 60% across all assessment components.
Because the final grade is cumulative, you do not need to pass every individual component separately. A lower score in one area can be compensated by stronger performance in others, provided that your overall weighted total reaches the 60% threshold.
3. Course engagement¶
Course engagement contributes 40% of the final grade.
Engagement is more than attendance. It reflects how actively and consistently you contribute to the learning process during the semester.
This can include:
preparing for class (e.g. summary slides on project progress),
working steadily on your project,
asking questions,
contributing to discussions,
sharing technical challenges and possible solutions,
giving constructive feedback to other students,
participating in in-class activities, quizzes, MS Teams discussions, Padlet activities, project feedback sessions and intermediate project updates.
You are not expected to have perfect solutions at every stage. What matters is that you actively use the course structure to develop your project, respond to feedback and support the shared learning environment.
4. Project report and repository¶
The project report and repository contribute 30% of the final grade.
Each project must be submitted with:
a short written report as a PDF,
a public GitHub or GitLab repository,
a link to the repository in the report.
The report should not exceed 6500 characters including spaces, excluding figures and references.
It should briefly document:
project motivation,
research question,
data,
methods,
implementation,
main results,
challenges,
limitations.
Key figures should be included in or attached to the report.
The repository should allow another student to understand and reproduce your work on a different machine. It should include:
a clear README file,
setup instructions,
information about the required Python environment,
relative file paths where possible,
notebooks or scripts that can be executed from start to finish.
Your code should be readable, well structured and sufficiently documented. Use meaningful variable names, comments where helpful, and functions, methods or classes where they improve the structure of your workflow.
The project report must be submitted via the corresponding MS Teams assignment by Monday, 14 December 2026, at 17:00.
Evaluation criteria¶
The report and repository are evaluated using the following criteria:
| Criterion | What this means |
|---|---|
| Project design and methodological fit | The research question, data, methods and analytical workflow are well aligned and feasible. |
| Implementation and code quality | The code runs correctly, is clearly structured and uses appropriate functions, classes or modules where useful. |
| Reproducibility and repository organisation | The repository includes clear setup instructions, required files, environment information and code that can be rerun by another student. |
| Results, interpretation and communication | The report presents the main results clearly, uses meaningful figures and explains what the results show. |
| Reflection and responsible practice | The report discusses limitations, uncertainty, challenges, methodological choices and the use of AI tools where applicable. |
5. Project presentations¶
Project presentations contribute 30% of the final grade.
You will present your project twice during the semester: first as a concept presentation and later as a final presentation.
Concept presentation¶
The concept presentation focuses on your project plan.
It should explain:
motivation,
research question,
planned data sources,
intended methods,
expected output,
possible bottlenecks.
The goal is to receive feedback early enough to improve the project before most implementation work is completed.
Final presentation¶
The final presentation focuses on what you actually implemented, found and learned.
It should present:
final workflow,
key results,
figures or maps,
interpretation,
limitations,
remaining challenges,
how the project changed compared with the initial concept,
which decisions you made during implementation,
what you would improve with more time.
The final presentation should not simply repeat the concept presentation. It should show the development from idea to implemented spatial analytics workflow.
Possible formats include a short live demo, a walkthrough of the repository, a visual explanation of the workflow, a comparison between planned and final implementation, or a focused discussion of one key result and its limitations.
6. How to use this page¶
Return to this page when you plan your project, prepare presentations, write your report and finalise your repository.
For more practical guidance, use the Project handbook, especially the pages on repository organisation, figures and maps, report writing and presentations.