Universität Wien

040309 VU Doing Data Science (MA) (2022S)

6.00 ECTS (2.00 SWS), SPL 4 - Wirtschaftswissenschaften
Prüfungsimmanente Lehrveranstaltung
GEMISCHT

The course language is English.

Only students who signed up for the class in univis/u:space are allowed to take the class (that means, that you have to at least be on the waiting list if you want to take this class). No exceptions possible.

An/Abmeldung

Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").

Details

max. 40 Teilnehmer*innen
Sprache: Englisch

Lehrende

Termine (iCal) - nächster Termin ist mit N markiert

This class will be offered in hybrid form. There will be lecture videos that students are expected to watch and discuss. All live appointments will be streamed through Moodle/Zoom. First meeting: Tuesday, March 1st, 13:15-14:45, SR1, Kolingasse 14-16.

Dienstag 01.03. 13:15 - 14:45 Hybride Lehre
PC-Seminarraum 1, Kolingasse 14-16, OG01
Mittwoch 02.03. 15:00 - 16:30 Hybride Lehre
PC-Seminarraum 1, Kolingasse 14-16, OG01
Dienstag 08.03. 13:15 - 14:45 Hybride Lehre
PC-Seminarraum 1, Kolingasse 14-16, OG01
Mittwoch 09.03. 15:00 - 16:30 Hybride Lehre
PC-Seminarraum 1, Kolingasse 14-16, OG01
Dienstag 15.03. 13:15 - 14:45 Hybride Lehre
PC-Seminarraum 1, Kolingasse 14-16, OG01
Mittwoch 16.03. 15:00 - 16:30 Hybride Lehre
PC-Seminarraum 1, Kolingasse 14-16, OG01
Dienstag 22.03. 13:15 - 14:45 Hybride Lehre
PC-Seminarraum 1, Kolingasse 14-16, OG01
Mittwoch 23.03. 15:00 - 16:30 Hybride Lehre
PC-Seminarraum 1, Kolingasse 14-16, OG01
Dienstag 29.03. 13:15 - 14:45 Hybride Lehre
PC-Seminarraum 1, Kolingasse 14-16, OG01
Mittwoch 30.03. 15:00 - 16:30 Hybride Lehre
PC-Seminarraum 1, Kolingasse 14-16, OG01
Dienstag 05.04. 13:15 - 14:45 Hybride Lehre
PC-Seminarraum 1, Kolingasse 14-16, OG01
Mittwoch 06.04. 15:00 - 16:30 Hybride Lehre
PC-Seminarraum 1, Kolingasse 14-16, OG01
Dienstag 26.04. 13:15 - 14:45 Hybride Lehre
PC-Seminarraum 1, Kolingasse 14-16, OG01
Mittwoch 27.04. 15:00 - 16:30 Hybride Lehre
PC-Seminarraum 1, Kolingasse 14-16, OG01
Dienstag 03.05. 13:15 - 14:45 Hybride Lehre
PC-Seminarraum 1, Kolingasse 14-16, OG01
Mittwoch 04.05. 15:00 - 16:30 Hybride Lehre
PC-Seminarraum 1, Kolingasse 14-16, OG01
Dienstag 10.05. 13:15 - 14:45 Hybride Lehre
PC-Seminarraum 1, Kolingasse 14-16, OG01
Mittwoch 11.05. 15:00 - 16:30 Hybride Lehre
PC-Seminarraum 1, Kolingasse 14-16, OG01
Dienstag 17.05. 13:15 - 14:45 Hybride Lehre
PC-Seminarraum 1, Kolingasse 14-16, OG01
Mittwoch 18.05. 15:00 - 16:30 Hybride Lehre
PC-Seminarraum 1, Kolingasse 14-16, OG01
Dienstag 24.05. 13:15 - 14:45 Hybride Lehre
PC-Seminarraum 1, Kolingasse 14-16, OG01
Mittwoch 25.05. 15:00 - 16:30 Hybride Lehre
PC-Seminarraum 1, Kolingasse 14-16, OG01
Dienstag 31.05. 13:15 - 14:45 Hybride Lehre
PC-Seminarraum 1, Kolingasse 14-16, OG01
Mittwoch 01.06. 15:00 - 16:30 Digital
Hybride Lehre
Mittwoch 08.06. 15:00 - 16:30 Hybride Lehre
PC-Seminarraum 1, Kolingasse 14-16, OG01
Dienstag 14.06. 13:15 - 14:45 Hybride Lehre
PC-Seminarraum 1, Kolingasse 14-16, OG01
Mittwoch 15.06. 15:00 - 16:30 Hybride Lehre
PC-Seminarraum 1, Kolingasse 14-16, OG01
Dienstag 21.06. 13:15 - 14:45 Hybride Lehre
PC-Seminarraum 1, Kolingasse 14-16, OG01
Mittwoch 22.06. 15:00 - 16:30 Hybride Lehre
PC-Seminarraum 1, Kolingasse 14-16, OG01
Dienstag 28.06. 13:15 - 14:45 Hybride Lehre
PC-Seminarraum 1, Kolingasse 14-16, OG01
Mittwoch 29.06. 15:00 - 16:30 Hybride Lehre
PC-Seminarraum 1, Kolingasse 14-16, OG01

Information

Ziele, Inhalte und Methode der Lehrveranstaltung

This course covers the fundamentals of setting up, managing, and conducting data science projects. Students acquire knowledge of processes describing how to approach and implement data science projects. They know the particular steps of the CRISP industry-standard, learn about various cases of how to apply this to different applications (from different areas such as business, humanities, astronomy), and are able to conduct data science projects themselves.

This course consists of lectures, tutorials, showcases, and project presentations. Students will work on their own data science projects in interdisciplinary groups.

Art der Leistungskontrolle und erlaubte Hilfsmittel

Midterm test (30%): March 30, 15:00
Final test (30%): May 17, 13:15
Project work (40%):
- Review meetings: May 24, 15:00
- Final presentations: **June 22, 15:00**

Mindestanforderungen und Beurteilungsmaßstab

For project work, attendance is mandatory, including kick-off and project presentations.

In total, 100 points can be achieved. Grades are assigned as follows:
In total, 100 points can be achieved. Grades are assigned as follows:
[88,100]: 1
[76,88[ : 2
[63,76[ : 3
[50,63[ : 4
< 50 : 5

Prüfungsstoff

Midterm test/Final test: Slides and topics covered in the lectures.
Project work: topic-specific poster presentation, handout, KNIME workflow.

Literatur

Provost, Foster; Fawcett, Tom (2013): Data Science for Business. What you need to know about data mining and data-analytic thinking. Köln: O`Reilly.

Berthold, Michael R.; Borgelt, Christian; Höppner, Frank; Klawonn, Frank; Silipo, Rosaria (2020): Guide to Intelligent Data Science. Cham: Springer International Publishing.

Zuordnung im Vorlesungsverzeichnis

Letzte Änderung: Do 11.05.2023 11:27