040190 KU Advanced Topics in Business Informatics (MA) (2017W)
Business Intelligence
Prüfungsimmanente Lehrveranstaltung
Labels
An/Abmeldung
Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").
- Anmeldung von Fr 08.09.2017 09:00 bis Do 21.09.2017 12:00
- Abmeldung bis Sa 14.10.2017 23:59
Details
max. 50 Teilnehmer*innen
Sprache: Deutsch
Lehrende
Termine (iCal) - nächster Termin ist mit N markiert
Participation on all lessons is mandatory. For an admission to the course, it is mandatory to be present on time at the first lesson and having chosen a seminar thesis topic within the first lesson.
A participation on the first lesson without having chosen a seminar thesis topic leads to a deregistration from the course (without grading). As soon as a topic has been chosen by the student, he/she will be graded and cannot be deregistered any more.After positive registration, a non participation on one of the three lessons leads to a negative grading of the course, as one of the mandatory results (e.g. expose, thesis) cannot be delivered.- Freitag 06.10. 11:30 - 14:45 Hörsaal 16 Oskar-Morgenstern-Platz 1 2.Stock
- Freitag 06.10. 15:00 - 18:15 Hörsaal 17 Oskar-Morgenstern-Platz 1 2.Stock
- Montag 06.11. 08:00 - 20:00 Hörsaal 16 Oskar-Morgenstern-Platz 1 2.Stock
- Donnerstag 14.12. 08:00 - 09:30 Hörsaal 16 Oskar-Morgenstern-Platz 1 2.Stock
- Donnerstag 14.12. 09:45 - 14:45 Hörsaal 17 Oskar-Morgenstern-Platz 1 2.Stock
- Donnerstag 14.12. 15:00 - 20:00 Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Stock
- Freitag 15.12. 11:30 - 14:45 Hörsaal 16 Oskar-Morgenstern-Platz 1 2.Stock
- Freitag 15.12. 15:00 - 18:15 Hörsaal 15 Oskar-Morgenstern-Platz 1 2.Stock
Information
Ziele, Inhalte und Methode der Lehrveranstaltung
Various topics in Business intelligence - from a business and technical point of view - will be focused on. The students will prepare seminar thesis and presentations on these topics. The topics will cover typical BI architecture, Data Warehouse, Data Mining, OLAP, and furthers. The course will be held only in German if only German speaking students will participate. Otherwise, the course will be held in English (usually the case).
Art der Leistungskontrolle und erlaubte Hilfsmittel
The exposees and seminar thesis can be delivered in german or english - up to the students preference. The presentation can only be done in German if all participating students are German speaking. Otherwise ALL presentations have to be made in English (usually the case).Grading relevant are the deliveries of the exposee paper and presentation slides, the seminar paper and presentation slides and each of the presentations.
Mindestanforderungen und Beurteilungsmaßstab
Minimum requirements to pass the course are:
- mandatory participation on all lessons (see above)
- On time delivery of the exposee (paper and presentation) AND seminar thesis (paper and presentation) via Moodle. No late submissions, deadlines will be communicated in the first lesson.
- presentations can only be held if the paper (exposee / thesis) AND the presentation slides (exposee / thesis) have been submitted on time via Moodle.
Grading will be done on the exposee paper (30%), the exposee presentation (10%), the thesis paper (40%), the thesis presentation (10%) and the students knowledge on his topics (10%).
A minimum of >65% over all grading relevant topics has to be achieved to pass the course.
Any plagiarizing will automatically lead to neg. grading. A special focus will be held on scientific writing of the papers.
The student can achieve max. 100 points (according to % distribution above), the grading is >92 points (1), >83 (2), >73 (3), >65 (4), <=65 (5).
- mandatory participation on all lessons (see above)
- On time delivery of the exposee (paper and presentation) AND seminar thesis (paper and presentation) via Moodle. No late submissions, deadlines will be communicated in the first lesson.
- presentations can only be held if the paper (exposee / thesis) AND the presentation slides (exposee / thesis) have been submitted on time via Moodle.
Grading will be done on the exposee paper (30%), the exposee presentation (10%), the thesis paper (40%), the thesis presentation (10%) and the students knowledge on his topics (10%).
A minimum of >65% over all grading relevant topics has to be achieved to pass the course.
Any plagiarizing will automatically lead to neg. grading. A special focus will be held on scientific writing of the papers.
The student can achieve max. 100 points (according to % distribution above), the grading is >92 points (1), >83 (2), >73 (3), >65 (4), <=65 (5).
Prüfungsstoff
Literatur
Zuordnung im Vorlesungsverzeichnis
Letzte Änderung: Mo 07.09.2020 15:28