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.
The following topics are covered under this training series:
- Fundamental Concepts - understand the fundamental concepts and terms underpinning social network analysis.
- Getting and Marshalling Data - learn how to collect and clean social network data.
- Techniques and Methods of Analysis - learn how to apply a range of analytical methods and techniques to derive substantive insights from social network data.
The training materials - including webinar recordings, slides, and sample Python code - can be found in the following folders:
- code - run and/or download code using our Jupyter notebook resources.
- installation - view instructions for how to download and install Python and other packages necessary for working with new forms of data.
- reading-list - explore further resources including articles, books, online resources and more.
- webinars - watch recordings of our webinars and download the underpinning slides.
We are grateful to UKRI through the Economic and Social Research Council for their generous funding of this training series.
- To access learning materials from the wider Computational Social Science training series: [Training Materials]
- To keep up to date with upcoming and past training events: [Events]
- To get in contact with feedback, ideas or to seek assistance: [Help]
Thank you and good luck on your journey exploring new forms of data!
Dr Julia Kasmire and Dr Diarmuid McDonnell
UK Data Service
University of Manchester
React
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