623.202 (24S) Seminar in Data Science and Engineering
Überblick
- Lehrende/r
- LV-Titel englisch Seminar in Data Science and Engineering
- LV-Art Seminar (prüfungsimmanente LV )
- LV-Modell Präsenzlehrveranstaltung
- Semesterstunde/n 2.0
- ECTS-Anrechnungspunkte 4.0
- Anmeldungen 5 (15 max.)
- Organisationseinheit
- Unterrichtssprache Englisch
- LV-Beginn 07.03.2024
- eLearning zum Moodle-Kurs
Zeit und Ort
LV-Beschreibung
Intendierte Lernergebnisse
Participants
- deepen their abilities and skills in the search and in the study of scientific literature
- learn by example to provide an overview over a given topic to structure it
- learn to distinguish between scientific-technologically founded work and less reliable or meaningful publications
- improve their presentation skills
- extend their knowledge within a selected topic of Data & Knowledge engineering
Lehrmethodik
seminaristic: presentations, discussions, paper writing, reviews
Inhalt/e
Web services, Ontologies, Workflow systems, temporal information systems, interoperability
Erwartete Vorkenntnisse
database technology; knowledge engineering; writing lab informatics - is highly recommended
Literatur
will be provided at the seminar
Intendierte Lernergebnisse
Participants
- deepen their abilities and skills in the search and in the study of scientific literature
- learn by example to provide an overview over a given topic to structure it
- learn to distinguish between scientific-technologically founded work and less reliable or meaningful publications
- improve their presentation skills
- extend their knowledge within a selected topic of Data & Knowledge engineering
Lehrmethodik
seminaristic: presentations, discussions, paper writing, reviews
Inhalt/e
Web services, Ontologies, Workflow systems, temporal information systems, interoperability
Erwartete Vorkenntnisse
database technology; knowledge engineering; writing lab informatics - is highly recommended
Literatur
will be provided at the seminar
Prüfungsinformationen
Prüfungsmethode/n
evaluation of the deliverables (extended abstract, paper, presentation, reviews)
details will be discussedin the first seminar
Prüfungsinhalt/e
n/a
Beurteilungskriterien/-maßstäbe
Factors:
- Quality of the results
- Extended abstract (15 %)
- Seminar paper (40 %)
- Presentation of Seminar paper (15%)
- Reviews written (20%)
- Participation in the discussions (10%)
Prüfungsmethode/n
evaluation of the deliverables (extended abstract, paper, presentation, reviews)
details will be discussed in the first seminar
Prüfungsinhalt/e
n/a
Beurteilungskriterien/-maßstäbe
Factors:
- Quality of the results
- Extended abstract (15 %)
- Seminar paper (40 %)
- Presentation of Seminar paper (15%)
- Reviews written (20%)
- Participation in the discussions (10%)
Beurteilungsschema
Note BenotungsschemaPosition im Curriculum
- Masterstudium Informatics
(SKZ: 911, Version: 19W.2)
-
Fach: Data Science and Engineering
(Wahlfach)
-
Seminar (
2.0h SE / 4.0 ECTS)
- 623.202 Seminar in Data Science and Engineering (2.0h SE / 4.0 ECTS) Absolvierung im 3. Semester empfohlen
-
Seminar (
2.0h SE / 4.0 ECTS)
-
Fach: Data Science and Engineering
(Wahlfach)
- Masterstudium Artificial Intelligence and Cybersecurity
(SKZ: 993, Version: 20W.1)
-
Fach: Specialisation in Artificial Intelligence and Cybersecurity
(Wahlfach)
-
Fachlich relevante Lehrveranstaltungen (
0.0h XX / 34.0 ECTS)
- 623.202 Seminar in Data Science and Engineering (2.0h SE / 4.0 ECTS) Absolvierung im 2., 3. Semester empfohlen
-
Fachlich relevante Lehrveranstaltungen (
0.0h XX / 34.0 ECTS)
-
Fach: Specialisation in Artificial Intelligence and Cybersecurity
(Wahlfach)
Gleichwertige Lehrveranstaltungen im Sinne der Prüfungsantrittszählung
-
Wintersemester 2022/23
- 623.202 SE Seminar in Data Science and Engineering (2.0h / 4.0ECTS)
-
Sommersemester 2022
- 623.202 SE Seminar in Data Science and Engineering (2.0h / 4.0ECTS)
-
Sommersemester 2021
- 623.202 SE Seminar in Data Science and Engineering (2.0h / 4.0ECTS)
-
Sommersemester 2020
- 623.202 SE Seminar in Data Science and Engineering (2.0h / 4.0ECTS)