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Instructors: Prof. Dr. Ricardo Usbeck
Event type:
Lecture
Displayed in timetable as:
DE - VL
Hours per week:
2
Language of instruction:
English
Min. | Max. participants:
- | 40
Comments/contents:
In this teaching unit, theoretical and practical aspects of data engineering are considered, starting from classical ETL methods and various databases up to the latest, large-scale batch and stream processing systems. Different data sets and their specifics will be analyzed and various benchmark methods will be discussed. In addition to standard tasks, one or more application examples will be successfully implemented by the students in the exercise.
Learning objectives:
Students will be familiar with various methods of data engineering, their strengths, weaknesses, and theoretical foundations. They also know common data sets and evaluation metrics. They are able to support data scientists by means of data engineering methods and to build, maintain and evaluate corresponding systems.
Didactic concept:
More information will be provided via https://lernen.min.uni-hamburg.de/course/view.php?id=2133
Literature:
See Moodle.
Additional examination information:
Course achievements: Regular and successful participation in the exercises; participation is generally considered successful if all tasks have been worked on and at least 51% have been solved correctly; in case of deviating criteria, these must be announced before registration for the module.
Examination results: The final module examination is usually in the form of an oral examination (on the overall content of the lecture and practical component) in the language of instruction. Alternatively, a written examination (written test) is possible; the type of examination will be announced before registration for the module. The module examination is graded differentiated.
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