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| Title |
Data & Things
|
| Semester |
F2026
|
| Master programme in |
Computer Science
|
| Type of activity |
Course |
| Mandatory or elective |
Mandatory |
| Teaching language |
English
|
| Study regulation |
Read about the Master Programme and find the Study Regulations at ruc.dk |
| REGISTRATION AND STUDY ADMINISTRATIVE | |
| Registration |
You register for activities through stads selvbetjening during the announced registration period, which you can see on the Study administration homepage. When registering for courses, please be aware of the potential conflicts and overlaps between course and exam time and dates. The planning of course activities at Roskilde University is based on the recommended study programmes, which should not overlap. However, if you choose optional courses and/or study plans that goes beyond the recommended study programmes, an overlap of lectures or exam dates may occur depending on which courses you choose. |
| Number of participants |
|
| ECTS |
10
|
| Responsible for the activity | |
| Head of study |
Maja Hanne Kirkeby (majaht@ruc.dk)
|
| Teachers |
|
| Study administration |
IMT Registration & Exams (imt-exams@ruc.dk)
|
| Exam code(s) |
U60058
|
| ACADEMIC CONTENT | |
| Overall objective |
Advanced data solutions and complex device systems. |
| Detailed description of content |
This course focuses on building data centric applications that utilize data to create new insights or features. In doing this, we need to understand how to analyze data and create statistical and machine learning models, as well as learning how to process, transform, and manage data. In managing data, we will go beyond the classical relational databases and cover topics such as parallel and distributed data systems, partial and graph databases, and data from IoT devices. Moreover, we will cover how to put data products, such as machine learning models, into production and monitor their performance (ML Ops). Thus, this course covers both hands-on introductions to data science, machine learning, data engineering, ML Ops, as well as building complex data centric systems that may utilize IoT devices and external data sources as input to statistical and machine learning models that may result in visualizations, features, or effects into the environment. Use of generative AI aids in the exam Generative AI aids (GAI) are permitted in the work with the exam if the use is declared. You must clearly declare how you have used generative artificial intelligence (GAI). This should be included as an appendix. This means that you must describe how you have used GAI, e.g. for the preparatory work, to ask questions and search for information, to receive feedback and criticism on your text, to carry out proofreading or to improve language and readability. It is important that you actively relate to your choice of tools in this way, as it is part of the entire process of creating the project, and thus part of your scientific method and professional communication. The use of any specific text that is GAI-generated requires citation, just as when using all other sources from which direct quotations are used. In the library’s guide, you can find more information on how to cite AI and how to account for your use of GAI, as well as RUC’s guidelines for the use of generative artificial intelligence (GAI). |
| Course material and Reading list |
Course materials will consist of part of books, papers, etc. The specific materials will be announced on Moodle for each lecture. |
| Overall plan and expected work effort |
The course will have a total workload of 270 hours consisting of approximately:
|
| Format |
|
| Evaluation and feedback |
Evaluation form to be filled out (anonymously) plus open discussion on the last course day |
| Programme |
|
| ASSESSMENT | |
| Overall learning outcomes |
After completing this activity, students will be able to:
|
| Prerequisites |
|
| Form of examination |
Individual oral exam based on a written product..
The character limit of the written product is maximum 48.000 characters, including spaces. The character limits include the cover, table of contents, bibliography, figures and other illustrations, but exclude any appendices. Time allowed for exam including time used for assessment: 20 minutes. The assessment is an overall assessment of the written product(s) and the subsequent oral examination. Permitted support and preparation materials for the oral exam: All. Assessment: 7-point grading scale. Moderation: Internal co-assessor. |
| Form of Re-examination |
Samme som ordinær eksamen / same form as ordinary exam
|
| Type of examination in special cases |
|
| Examination and assessment criteria (implemented) |
Information about the written product:
Information about the oral exam:
|
| Exam code(s) | |
| Last changed | 04/11/2025 |
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