070066 UE Methodological course - Data Structures and Data Management in the Humanities (2021S)
Continuous assessment of course work
Labels
MIXED
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).
- Registration is open from Mo 08.02.2021 09:00 to Mo 22.02.2021 14:00
- Registration is open from We 24.02.2021 09:00 to Fr 26.02.2021 14:00
- Deregistration possible until We 31.03.2021 23:59
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
19.05.
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
- 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
DH-S I
Last modified: Fr 12.05.2023 00:13