Universität Wien FIND

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070066 UE Methodological course - Data Structures and Data Management in the Humanities (2021S)

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

Registration/Deregistration

Details

max. 25 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

Due to the continuing public health restrictions, the class will be held exclusively online.

Wednesday 03.03. 09:00 - 10:30 Hybride Lehre
Seminarraum Geschichte 3 Hauptgebäude, 2.Stock, Stiege 9
Wednesday 10.03. 09:00 - 10:30 Hybride Lehre
Seminarraum Geschichte 3 Hauptgebäude, 2.Stock, Stiege 9
Wednesday 17.03. 09:00 - 10:30 Hybride Lehre
Seminarraum Geschichte 3 Hauptgebäude, 2.Stock, Stiege 9
Wednesday 24.03. 09:00 - 10:30 Hybride Lehre
Seminarraum Geschichte 3 Hauptgebäude, 2.Stock, Stiege 9
Wednesday 14.04. 09:00 - 10:30 Hybride Lehre
Seminarraum Geschichte 3 Hauptgebäude, 2.Stock, Stiege 9
Wednesday 21.04. 09:00 - 10:30 Hybride Lehre
Seminarraum Geschichte 3 Hauptgebäude, 2.Stock, Stiege 9
Wednesday 28.04. 09:00 - 10:30 Hybride Lehre
Seminarraum Geschichte 3 Hauptgebäude, 2.Stock, Stiege 9
Wednesday 05.05. 09:00 - 10:30 Hybride Lehre
Seminarraum Geschichte 3 Hauptgebäude, 2.Stock, Stiege 9
Wednesday 12.05. 09:00 - 10:30 Hybride Lehre
Seminarraum Geschichte 3 Hauptgebäude, 2.Stock, Stiege 9
Wednesday 26.05. 09:00 - 10:30 Hybride Lehre
Seminarraum Geschichte 3 Hauptgebäude, 2.Stock, Stiege 9
Wednesday 02.06. 09:00 - 10:30 Hybride Lehre
Seminarraum Geschichte 3 Hauptgebäude, 2.Stock, Stiege 9
Wednesday 09.06. 09:00 - 10:30 Hybride Lehre
Seminarraum Geschichte 3 Hauptgebäude, 2.Stock, Stiege 9
Wednesday 16.06. 09:00 - 10:30 Hybride Lehre
Seminarraum Geschichte 3 Hauptgebäude, 2.Stock, Stiege 9
Wednesday 23.06. 09:00 - 10:30 Hybride Lehre
Seminarraum Geschichte 3 Hauptgebäude, 2.Stock, Stiege 9
Wednesday 30.06. 09:00 - 10:30 Hybride Lehre
Seminarraum Geschichte 3 Hauptgebäude, 2.Stock, Stiege 9

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, data(base) model; c. 5 pages.) 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

Flanders, Julia, and Fotis Jannidis. 2015. “Data Modeling.” In A New Companion to Digital Humanities, edited by Susan Schreibman, Ray Siemens, and John Unsworth, 229–37. Chichester: Wiley Blackwell.

Gitelman, Lisa, ed. 2013. “Raw Data” Is an Oxymoron. Cambridge, Massachusetts ; London, England: The MIT Press.

Association in the course directory

SP Digital Humanities
DH-S I

Last modified: We 21.04.2021 11:26