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| Title |
Data & Things
|
| Semester |
F2023
|
| Master programme in |
Computer Science
|
| Type of activity |
Course |
| Teaching language |
English
|
| Study regulation |
Read about the Master Programme and find the Study Regulations at ruc.dk |
| REGISTRATION AND STUDY ADMINISTRATIVE | |
| Registration |
Sign up for study activities at STADS Online Student Service within the announced registration period, as you can see on the Study administration 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 | |
| Head of study |
Henrik Bulskov (bulskov@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 cover 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. |
| Course material and Reading list |
Will be announced on Moodle. |
| Overall plan and expected work effort |
The course will have a total workload of 275 hours. |
| 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:
|
| 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 |
IKKE UDFYLDT |
| Exam code(s) | |
| Last changed | 06/03/2023 |
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