We are delighted to announce the launch of four new data skills modules designed to help social science data service professionals and social science researchers develop foundational skills in computational social science.
As we continue to see fast paced change in the data landscape, the data skills modules will support the use of new forms of data, new methods, ever-greater computing power and rapidly evolving research opportunities.
Building capacity
Many social science data service professionals and researchers – both new and experienced – have strong foundations in traditional social science data and methods. The modules build on this expertise by introducing foundational computational social science skills and approaches.
Funded by UKRI through the Digital Research Infrastructure Programme, the modules have been delivered as part of the Building Computational Capacity in global data service professionals project.
The aim of the project was to build capacity within the international data services community by developing foundational level data skills modules in computational social science both for the UK and wider global data services community.
The new Computational Social Science Data Skills Modules address a growing need for data-service staff to support social-science research that uses newer data types, methods and computational approaches.
Accessing the modules
The new modules, which are free to access, are:
- What is Computational Social Science?
Explore computational approaches, tools, and workflows in social science research. This module introduces the foundations of computational social science, focusing on concepts rather than coding or technical implementation. - Documentation for Reproducible Research
Create clear, reusable documentation for reproducible and collaborative research. This module introduces key principles and approaches for documenting research workflows, code and computational environments. - Introduction to Synthetic Data for the Social Sciences
Understand synthetic data, fidelity, and research uses in social science. This module provides a conceptual overview of synthetic data, covering key concepts, common methods, and research uses, rather than technical implementation. - Introduction to Prompt Engineering for the Social Sciences
This module provides an introduction to prompt engineering and its relevance for social science research and professional practice. It focuses on the responsible use of AI Large Language Models (LLMs), the fundamentals of prompt design, common use cases, and key prompting techniques. It does not cover advanced use of AI LLMs or model development.
These modules provide foundational-level, conceptual introductions to tools and methods that are becoming increasingly important for supporting social science research.
To access the modules, follow this link: Data skills for the social sciences | UK Data Service
Further support
To support continued learning beyond the modules, you can use our discussion forum or attend one of our monthly Computational Social Science drop-ins. The discussion forum is designed for asking questions, sharing experiences, and discussing the module content.
The Computational Social Science drop-ins offer informal discussion and further support. The next drop-ins will take place on 10 November and 8 December. Further opportunities will be advertised on our training and events pages.
More modules are currently in development. Watch this space for new modules coming soon on topics such as Choosing Software for Computational Social Science Research and Programming Languages for Computational Social Science Research.