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

5.00 ECTS (2.00 SWS), SPL 7 - Geschichte
Continuous assessment of course work
Moodle

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

max. 25 participants
Language: German, English

Lecturers

Classes (iCal) - next class is marked with N

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

Information

Aims, contents and method of the course

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.

Assessment and permitted materials

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.

Minimum requirements and assessment criteria

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

Examination topics

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

Reading list

Available through Moodle

Association in the course directory

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

Last modified: Mo 15.01.2024 13:05