Project: Data Engineering and Machine Learning (5 cr)
Code: R504D106-3002
General information
- Enrollment
- 24.03.2025 - 31.07.2025
- Registration for the implementation has begun.
- Timing
- 06.10.2025 - 05.12.2025
- The implementation has not yet started.
- Number of ECTS credits allocated
- 5 cr
- Local portion
- 2 cr
- Virtual portion
- 3 cr
- RDI portion
- 2 cr
- Mode of delivery
- Blended learning
- Unit
- Bachelor of Engineering, Information Technology
- Teaching languages
- Finnish
- Teachers
- Aku Kesti
- Teacher in charge
- Aku Kesti
- Groups
-
R54D23SBachelor of Engineering, Machine Learning and Data Engineering (full time studies), 2023
- Course
- R504D106
Evaluation scale
H-5
Objective
The student is able to carry out a practical data engineering project using agile methods. The student knows the DevOps principles and applies these principles in development and teamwork. The student is able to utilize DevOps tools in data management and knows the key principles of data security related to data security. The student is able to manage stakeholders.
Content
- Agile methods and DevOps
- Stakeholder management
- Data management security
- DevSecOps
- DataOPS
Location and time
Course will be arranged in Rovaniemi campus. More detailed information from the time schedules
Teaching methods
There are other semester course which are integrated to project. Agile method is used as a method in the process. There will be project workshop days and reviews during the semester. Reviews will be separately with each project group.
Student workload
Course is 5 credits and approximately 125 hours student work. It consists of
- SCRUM reviews
- Working with technological challenges in the project group
- Project meetings and documentation, reporting
- Project seminar at the end of the project