Vast swathes of our social interactions and personal behaviours are now conducted online and/or captured digitally. Thus, computational methods for collecting, cleaning and analysing data are an increasingly important component of a social scientist’s toolkit. Social Network Analysis (SNA) offers a rich and insightful methododolgical approach for uncovering and understanding social structures, relations and networks of assocation.
This training series, run over two half days, introduces participants to core concepts, data structures, and methods of analysis associated with SNA. The training consists of a mix of lectures, coding demonstrations, practical exercises, and group discussions. The training is suitable for individuals new to SNA and looking to gain a good grounding in the fundamentals of this methodology.
The course programme can be viewed here.
This repository houses the materials underpinning a one-day WISERD course on social network analysis run by Dr Diarmuid McDonnell, University of the West of Scotland. The course was first run on 2021-10-06.
The training materials can be found in the following folders:
- code - Jupyter Notebooks containing executable Python code for the social network analysis lessons.
- presentations - PDF versions of the course lectures.
- reading - lists of interesting and relevant online articles.
I am grateful to the Wales Institute of Social and Economic Research and Data (WISERD) for funding this course and the associated set of online learning resources.
Please do not hesitate to get in contact if you have queries, criticisms or ideas regarding these materials: Dr Diarmuid McDonnell