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

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

Details

max. 25 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

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.

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

Information

Aims, contents and method of the course

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.

Assessment and permitted materials

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.

Minimum requirements and assessment criteria

Examination topics

Reading list


Association in the course directory

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

Last modified: Fr 22.10.2021 00:05