Programming for Data Analysis
2025/2026
Content, progress and pedagogy of the module
Disclaimer.
This is an English translation of the module. In case of
discrepancy between the translation and the Danish version, the
Danish version of the module is valid.
PURPOSE
To enable the student to acquire skills in problem-oriented project
work in a group, as well as knowledge about the relationships
between problem definition, the role of model formation in
understanding and constructing programs, and programs as solutions
to a problem in a problem context. Furthermore, to gain knowledge
about the subject’s content and its further potentials.
JUSTIFICATION
Based on the experiences from P0, particularly the limitations of
the spreadsheet model, this project will focus on data analysis
that requires greater programmability. The project aims to provide
insight into and experience with the programmability of data
analysis.
Learning objectives
Knowledge
- understand and explain the theories and methods used in the project
- to analyze the chosen issue especially understand and explain the concepts within programming and modeling that have been used in connection with the project
- understand and account for the project's contextual conditions.
Skills
- choose, describe and use one of the methods proposed in the course Problem-based learning in science, technology and society for organizing group cooperation and for resolving any group conflicts
- apply concepts and tools for problem-based project work and reflect in writing on the problem-based learning in a project context
- communicate the project's work results and work processes in a structured and comprehensible way, both in writing, graphically and orally.
- be able to search for relevant literature and use correct citation techniques
Competences
- analyze a problem within data analysis and within this problem formulate a problem where data analysis can form part of the solution
- set up a model of the problem
- include relevant concepts and methods for analysis and assessment of the project's solutions in relation to the context of the problem
Type of instruction
Project work
Extent and expected workload
The student is expected to spend 30 hours per ECTS, which for this activity means 300 hours.
Exam
Exams
| Name of exam | Programming for Data Analysis |
| Type of exam | Oral exam based on a project
As part of the project, the group must collaboratively produce a
high-quality data analysis. In this context, there should also be a
description of the essential characteristics of the data analysis.
As documentation for the project work, the project group must: - prepare a project report, - develop a new P1 project proposal that can be presented at the next P1 course, - participate in experience gathering, - prepare a process analysis. Midway through the project period, a status seminar will be held where the project group presents its problem formulation, work results, and experiences with the project work process. At this seminar, at least one other project group and the respective group supervisors will participate. After the submission of the project report, an experience gathering will be held, where several P1 project groups present their experiences with the project’s work process. The experience gathering forms the basis for each group’s process analysis. |
| ECTS | 10 |
| Permitted aids | Aids are permitted during the preparation of the project, but not during the exam. Rules regarding AI are mentioned on the semester page in MOODLE |
| Assessment | 7-point grading scale |
| Type of grading | Internal examination |
| Criteria of assessment | The criteria of assessment are stated in the Examination Policies and Procedures |
Additional information
Contact: Study Board for Computer Science via cs-sn@cs.aau.dk or 9940 8854
Facts about the module
| Danish title | Programmering til dataanalyse |
| Module code | DSNDVMLB132 |
| Module type | Project |
| Duration | 1 semester |
| Semester | Autumn
|
| ECTS | 10 |
| Language of instruction | Danish |
| Empty-place Scheme | Yes |
| Location of the lecture | Campus Aalborg |
| Responsible for the module |
Organisation
| Education owner | Bachelor of Science (BSc) in Data Science and Machine Learning |
| Study Board | Study Board of Computer Science |
| Department | Department of Computer Science |
| Faculty | The Technical Faculty of IT and Design |
