Universität Wien

070172 UE Methodological course - Data Structures and Data Management in the Humanities (2023W)

5.00 ECTS (2.00 SWS), SPL 7 - Geschichte
Prüfungsimmanente Lehrveranstaltung
Moodle

An/Abmeldung

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

Details

max. 25 Teilnehmer*innen
Sprache: Deutsch, Englisch

Lehrende

Termine (iCal) - nächster Termin ist mit N markiert

  • Donnerstag 05.10. 13:15 - 14:45 Seminarraum 18 Kolingasse 14-16, OG02
  • Donnerstag 12.10. 13:15 - 14:45 Seminarraum 18 Kolingasse 14-16, OG02
  • Donnerstag 19.10. 13:15 - 14:45 Seminarraum 18 Kolingasse 14-16, OG02
  • Donnerstag 09.11. 13:15 - 14:45 Seminarraum 18 Kolingasse 14-16, OG02
  • Donnerstag 16.11. 13:15 - 14:45 Seminarraum 18 Kolingasse 14-16, OG02
  • Donnerstag 23.11. 13:15 - 14:45 Seminarraum 18 Kolingasse 14-16, OG02
  • Donnerstag 30.11. 13:15 - 14:45 Seminarraum 18 Kolingasse 14-16, OG02
  • Donnerstag 07.12. 13:15 - 14:45 Seminarraum 18 Kolingasse 14-16, OG02
  • Donnerstag 14.12. 13:15 - 14:45 Seminarraum 18 Kolingasse 14-16, OG02
  • Donnerstag 11.01. 13:15 - 14:45 Seminarraum 18 Kolingasse 14-16, OG02
  • Donnerstag 18.01. 13:15 - 14:45 Seminarraum 18 Kolingasse 14-16, OG02
  • Donnerstag 25.01. 13:15 - 14:45 Seminarraum 18 Kolingasse 14-16, OG02

Information

Ziele, Inhalte und Methode der Lehrveranstaltung

The aim of this course is to familiarise students with the basic structure of digital data and, in particular, to teach them about semantic data modeling based on analysis of requirements. This is a practice-based class; students will generate appropriate data models based on real data sets from the humanities and implement them technically. No programming knowledge is necessary in advance, although there will be synergies with the LV "Introduction to DH: Tools and Techniques". Students will acquire the necessary knowledge through hands-on work over the course of the semester (as is usual in DH), working in small teams to develop data, structures and models for a project of their own choice. They will present requirements, planned solutions, and finally an implementation of their data for a final project, which will also be documented in writing. Along the way we will discuss empirical and theoretical frameworks of data mining and data processing.

Art der Leistungskontrolle und erlaubte Hilfsmittel

Active participation in class, small project-based exercises, project presentation and final project (including written abstract, data management plan, database model) Where possible we will use Datacamp (https://www.datacamp.com/) for homework assignments.

Mindestanforderungen und Beurteilungsmaßstab

Active participation in class (20%); homework assignments (40%); final project presentation (10%); final project written submission (30%).

Prüfungsstoff

- Data types and basic data structures (scalars, tuples, arrays, sets, dictionaries)
- Relational databases, schemas and modelling
- XML data structures
- NoSQL / graph-based data modelling

Literatur

Available through Moodle

Zuordnung im Vorlesungsverzeichnis

MA Geschichte: SP Digital Humanities
MA DH: DH-S I

Letzte Änderung: Mo 15.01.2024 13:05