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) ~10th week.
- MCQ3: (Data lifecycle + Data modeling + Big Data) ~12th 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.
Homework
The course has weekly homework related to various technologies. The homework activities are listed below. They will be evaluated in a pass/fail manner. The goal of the homework is to gain practical experience with the technologies studied in class and to reuse them for implementing your project.
- SQL and Postgres
- Redis
- MongoDB
- Neo4J
- Apache Kafka
- Kafka Streams and KSQLDB
- Apache Airflow
- Dataframes
Extras
The following extras will give you a maximum of 10 points to the final grade (you must get at least 50% for the MCQ exams).
- Improvement to the course material (1 to 3 points)
- participation in class (e.g., small reports for each lecture class including answers to mini exercises in class) (1 to 5 points)
- Extra activities related activity, e.g., extensions of the material, support of classmates asking questions/ (10 points)
- More extras can be proposed by students or added later
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)