Grading
The Course is Differential!
The grade for the course will be 60% on multiple-choice questionnaires (MCQ) and 40% on a course project.
The MCQ will be online via the course page on Moodle. They will be in four parts. Each part will be also including questions about the respective previous parts.
The tentative schedule for these MCQs is
- MCQ1: Data lifecycle ~6th week.
- MCQ2: (Data lifecycle + Data modeling) ~11th week.
- MCQ3: (Data lifecycle + Data modeling + Big Data) ~14th week
- MCQ4: (all topics) ~16th week
The project data set and idea will be announced in the second week. You need to form teams to work on the projects. Team size will be announced in the lecture.
Regularly, there will be progress reporting and feedback. The project reporting, presentation, and discussion will be possible from week 16, right before the Christmas holidays.
Practice session activities
The course has weekly practices related to various technologies. The activities are listed below. Feedback will be given in practice sessions. The goal is to gain practical experience with the technologies studied in class and to reuse them for implementing your project.
- Docker containers
- Apache Airflow
- Pandas DataFrames
- DBT
- MongoDB
- Neo4J
- Singularity containers
Grading Scale:
- A 91 or above
- B 81 up to 90
- C 71 up to 80
- D 61 up to 70
- E 50 up to 60
- F 0 up to 49 (fail)