Universität Wien
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070175 UE Methodological workshop - Historic Social Network Analysis (2021W)

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

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: Englisch

Lehrende

Termine (iCal) - nächster Termin ist mit N markiert

The course will be taught remotely, links to the lectures can be found on the course moodle. Please note that the class begins at 9 am not at 8 am as stated in the schedule.

  • Dienstag 05.10. 08:00 - 10:30 Digital
  • Dienstag 12.10. 08:00 - 10:30 Hybride Lehre
    Seminarraum 4 Hauptgebäude, Tiefparterre Stiege 9 Hof 5
  • Dienstag 19.10. 08:00 - 10:30 Hybride Lehre
    Seminarraum 4 Hauptgebäude, Tiefparterre Stiege 9 Hof 5
  • Dienstag 09.11. 08:00 - 10:30 Hybride Lehre
    Seminarraum 4 Hauptgebäude, Tiefparterre Stiege 9 Hof 5
  • Dienstag 16.11. 08:00 - 10:30 Hybride Lehre
    Seminarraum 4 Hauptgebäude, Tiefparterre Stiege 9 Hof 5
  • Dienstag 23.11. 08:00 - 10:30 Hybride Lehre
    Seminarraum 4 Hauptgebäude, Tiefparterre Stiege 9 Hof 5
  • Dienstag 30.11. 08:00 - 10:30 Hybride Lehre
    Seminarraum 4 Hauptgebäude, Tiefparterre Stiege 9 Hof 5
  • Dienstag 07.12. 08:00 - 10:30 Hybride Lehre
    Seminarraum 4 Hauptgebäude, Tiefparterre Stiege 9 Hof 5
  • Dienstag 14.12. 08:00 - 10:30 Hybride Lehre
    Seminarraum 4 Hauptgebäude, Tiefparterre Stiege 9 Hof 5
  • Dienstag 11.01. 08:00 - 10:30 Hybride Lehre
    Seminarraum 4 Hauptgebäude, Tiefparterre Stiege 9 Hof 5
  • Dienstag 18.01. 08:00 - 10:30 Hybride Lehre
    Seminarraum 4 Hauptgebäude, Tiefparterre Stiege 9 Hof 5
  • Dienstag 25.01. 08:00 - 10:30 Hybride Lehre
    Seminarraum 4 Hauptgebäude, Tiefparterre Stiege 9 Hof 5

Information

Ziele, Inhalte und Methode der Lehrveranstaltung

Overview
In this methodology seminar students are introduced to the study of historic social networks and key network science concepts. In addition, students will explore a number of historic network analysis projects as case studies from the ancient, medieval, and early modern periods in order to better understand the unique problems that studying historic networks present. Throughout the course students will develop a social network analysis project of their own and learn how to explore the data that they collect in the R statistical coding language. Students will learn about data collection, project development, exploratory data analysis, visualisation, and communication of their results. This course is aimed at students in the History and Digital Humanities masters’ programmes and no prior knowledge of the subject or methodologies is required for participation.
Learning Objectives
Theory: Students will learn about key network science concepts including: power, density, centrality, brokerage, and community detection. Students will also gain a critical knowledge of applying these concepts to historical projects and how to deal with the problems this presents such as: how to deal with missing data and limited data sets, how to collect data from historical sources, how to deal with data from broad time periods. Through the study and critique of case studies, students will not only learn how to develop their own projects but also how to interpret and use the results of other historic SNA research in their own work.
Methodology: Students will become familiar with the coding language R and it’s use in SNA projects. Students will become familiar with several R packages and libraries including “ggplot2” and the “tidyverse” and will become proficient in using these packages for data manipulation and visualisation. Students will become proficient in the exploratory data analysis workflow and learn how to apply this to their historic SNA projects and other data analysis projects.
This class is supported by DataCamp, the most intuitive learning platform for data science and analytics. Learn any time, anywhere and become an expert in R, Python, SQL, and more. DataCamp’s learn-by-doing methodology combines short expert videos and hands-on-the-keyboard exercises to help learners retain knowledge. DataCamp offers 350+ courses by expert instructors on topics such as importing data, data visualization, and machine learning. They’re constantly expanding their curriculum to keep up with the latest technology trends and to provide the best learning experience for all skill levels.

Art der Leistungskontrolle und erlaubte Hilfsmittel

Assessment
The Course uses continuous assessment and there is no final examination. Throughout the semester students develop a social network analysis project and are assessed on their progress using homework assignments (60% of grade) and the final submission of the project (30%). The remaining 10% of the course grade is awarded for engagement in the course activities.

Mindestanforderungen und Beurteilungsmaßstab

Prüfungsstoff

Literatur


Zuordnung im Vorlesungsverzeichnis

MA Geschichte (2014): 3 ECTS, Schwerpunkt: DH
MA Geschichte (2019): 5 ECTS, Schwerpunkt: DH
MA DH: DH-S II

Letzte Änderung: Fr 12.05.2023 00:13