Universität Wien
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080107 UE Course: Coding, Automating, and Visualizing Art History (2025S)

5.00 ECTS (2.00 SWS), SPL 8 - Kunstgeschichte und Europäische Ethnologie
Continuous assessment of course work
Th 06.03. 14:15-15:45 Seminarraum 2 d. Inst. f. Kunstgeschichte UniCampus Hof 9 3F-EG-20

Details

Language: English

Lecturers

    Classes (iCal) - next class is marked with N

    • Thursday 13.03. 14:15 - 15:45 Seminarraum 2 d. Inst. f. Kunstgeschichte UniCampus Hof 9 3F-EG-20
    • Thursday 20.03. 14:15 - 15:45 Seminarraum 2 d. Inst. f. Kunstgeschichte UniCampus Hof 9 3F-EG-20
    • Thursday 27.03. 14:15 - 15:45 Seminarraum 2 d. Inst. f. Kunstgeschichte UniCampus Hof 9 3F-EG-20
    • Thursday 03.04. 14:15 - 15:45 Seminarraum 2 d. Inst. f. Kunstgeschichte UniCampus Hof 9 3F-EG-20
    • Thursday 10.04. 14:15 - 15:45 Seminarraum 2 d. Inst. f. Kunstgeschichte UniCampus Hof 9 3F-EG-20
    • Thursday 08.05. 14:15 - 15:45 Seminarraum 2 d. Inst. f. Kunstgeschichte UniCampus Hof 9 3F-EG-20
    • Thursday 15.05. 14:15 - 15:45 Seminarraum 2 d. Inst. f. Kunstgeschichte UniCampus Hof 9 3F-EG-20
    • Thursday 22.05. 14:15 - 15:45 Seminarraum 2 d. Inst. f. Kunstgeschichte UniCampus Hof 9 3F-EG-20
    • Thursday 05.06. 14:15 - 15:45 Seminarraum 2 d. Inst. f. Kunstgeschichte UniCampus Hof 9 3F-EG-20
    • Thursday 12.06. 14:15 - 15:45 Seminarraum 2 d. Inst. f. Kunstgeschichte UniCampus Hof 9 3F-EG-20
    • Thursday 26.06. 14:15 - 15:45 Seminarraum 2 d. Inst. f. Kunstgeschichte UniCampus Hof 9 3F-EG-20

    Information

    Aims, contents and method of the course

    This course is designed for Art History students interested in learning basic programming and data analysis. You'll learn Python to automate repetitive tasks and visualise certain art historical data. Ideal for art historians who want to develop some quantitative and empirical skills. No prior experience is needed. Bringing a laptop is recommended, and we will guide you through installing the necessary software.

    · Contents:
    The course will cover:
    - Python
    - Statistics
    - Correlation & Regression
    - Social Network Analysis
    - Time Series Analysis
    - Clustering
    - Dimensionality Reduction

    · Language:
    Course materials will be presented in English, but participants may choose to communicate with the instructor and complete assignments in either English or German.

    Assessment and permitted materials

    Examination and Grading:
    Assessment will be based on a series of coding assignments distributed throughout the semester (100%).

    Minimum requirements and assessment criteria

    Minimum requirement:
    - Compulsory attendance. In the event of an absence due to illness or an exceptional family situation, written proof must be presented.
    - All partial achievements must be completed in order to successfully complete the course.

    Assessment criteria:
    90-100: Very Good (1)
    80-89: Good (2)
    70-79: Satisfactory (3)
    60-69: Sufficient (4)
    0-59: Failed (5)

    Examination topics

    The examination material is the content of the course.

    Reading list

    Slides

    Association in the course directory

    Last modified: Fr 10.01.2025 00:01