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| Title |
Scientific Computing and Data Science
|
| Semester |
F2026
|
| Master programme in |
Physics and Scientific Modelling / Mathematical Bioscience
|
| Type of activity |
Course |
| Mandatory or elective |
Mandatory Physics and Scientific Modelling - Thematic profile 1 and 3. Mathematical Bioscience and Physics and Scientific Modelling - General profile: a choice between Advanced topics in Mathematics and Scientific Computing and Data Science. |
| Teaching language |
English
|
| Study regulation | |
| REGISTRATION AND STUDY ADMINISTRATIVE | |
| Registration |
Sign up for study activities at stads selvbetjening within the announced registration period, as you can see on the Studyadministration homepage. When signing up for study activities, please be aware of potential conflicts between study activities or exam dates. The planning of activities at Roskilde University is based on the recommended study programs which do not overlap. However, if you choose optional courses and/or study plans that goes beyond the recommended study programs, an overlap of lectures or exam dates may occur depending on which courses you choose. |
| Number of participants |
|
| ECTS |
10
|
| Responsible for the activity |
Thomas Schrøder (tbs@ruc.dk)
|
| Head of study |
Nicholas Bailey (nbailey@ruc.dk)
|
| Teachers |
|
| Study administration |
INM Registration & Exams (inm-exams@ruc.dk)
|
| Exam code(s) |
U60190
|
| ACADEMIC CONTENT | |
| Overall objective |
To give the student experience in choosing and applying the methods of Scientific Computing and Data Science to new problems and to give the student an overview of methods associated with: - Scientific Computing, i.e., the use of computers and applied mathematics to generate data from models by numerical methods and/or simulation. - Data Science, i.e., the use of computers, models, and applied mathematics to gain insight from data. |
| Detailed description of content |
The aim of the course is to give the student experience in choosing and applying methods of Scientific Computing and Data Science to (for the student) new problems and to give the student an overview of methods associated with the two subjects:
In the third and final mini-projct students groups can choose between the two subjects (Scientific Computing and Data Science) or a combination thereof. If a single subject is chosen, the mini-project must include a discussion of how the other subject could be involved. |
| Course material and Reading list |
To be specified on moodle |
| Overall plan and expected work effort |
10 ECTS course
- Total 270 hrs |
| Format |
|
| Evaluation and feedback |
The course includes formative evaluation based on dialogue between the students and the teacher(s). Students are expected to provide constructive critique, feedback and viewpoints during the course if it is needed for the course to have better quality. Every other year at the end of the course, there will also be an evaluation through a questionnaire in SurveyXact. The Study Board will handle all evaluations along with any comments from the course responsible teacher. Furthermore, students can, in accordance with RUCs ‘feel free to state your views’ strategy through their representatives at the study board, send evaluations, comments or insights form the course to the study board during or after the course. |
| Programme |
Theme 1: Scientific Computing. First mini-project: Molecular Dynamics Theme 2: Data Science. Second mini-project: Application of data science methods to scientific data Theme 3: Third mini-project: Scientific computing and/or data science |
| ASSESSMENT | |
| Overall learning outcomes |
After completing the course the students will be able to
|
| Prerequisites |
|
| Form of examination |
Individual oral exam based on a portfolio. The character limit of the portfolio is 1,200-120,000 characters, including spaces. Examples of written products are exercise responses, talking points for presentations, written feedback, reflections, written assignments. The preparation of the products may be subject to time limits. 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: 30 minutes. The assessment is an assessment of the oral examination. The written product(s) is not part of the assessment. 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) |
The students produce a portefolio consisting of 3 mini-projects. All 3 can be handed-in for review by the teacher. At the exam the student makes a presentation of the third mini-project. The presentation may be interrupted by clarifying questions and the presentation will be followed by a discussion and questioning with in the curriculum of the course. Students will be assessed by their ability to:
The assessment of the oral exam is based on the student’s ability to meet the criteria mentioned above and their ability to
Furthermore, whether the performance meets all formal requirements in regard to both for the written og oral exam Regarding the use of generative AI at the exam In this course, generative AI tools (GAI) are allowed in the work on the exam if their use is declared. You must clearly indicate how you have used generative artificial intelligence (GAI). This can, for example, be included as part of a methodology section or as a brief statement at the end of your exam paper or submitted as an appendix to your assignment. This means that you must describe how you have used GAI, for example, for preparatory work on the assignment, to ask questions, search and process information, receive feedback and critique on your text, perform proofreading, or improve language and readability. It is important that you actively consider your choice of tools in this way, as it is part of the entire creation process of the assignment and thus part of your scientific method and academic communication. The use of any specific text that is GAI-generated requires citation, just like the use of any other sources from which direct quotes are taken. The use of generative artificial intelligence (GAI) must always take place within the framework of Roskilde University's ‘Guidelines for using generative artificial intelligence in exams’. In the library's guide, you can see more about how to cite AI, how you can declare your use of GAI, and read Roskilde University’s Guidelines - https://libguides.ruc.dk/AI. Regular spell check and other language suggestions, as known from Word or other word processing programs, as well as programs for writing minutes and transcription, are allowed in all written exams and do not need to be declared. |
| Exam code(s) | |
| Last changed | 17/11/2025 |
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