Social scientists have a huge opportunity to address current research problems with the rise of big data and technology. However, this opportunity comes with challenges, too – ethical, methodological and theoretical.
Lecturers also face the pedagogical challenge of bringing new people into the community, often teaching students who have disparate knowledge of and experiences with data, statistics and programming on the one hand, and social research on the other. More than ever, lecturers need pragmatic support for teaching computer scientists alongside social scientists.
SAGE Publishing has over 50 years as a leader in the field of Research Methods, meaning our textbook programme is exceptionally well-placed to empower students, lecturers and researchers to develop the additional skills and experience they need to take advantage of the opportunities social data science offers.
We have created a collection of teaching materials and this interactive space in which to share material from our forthcoming titles. One of our ambitions is to hear what you think about the content we’re sharing.
We recognise that there will be a diverse range of opinions within the community and aim to start a conversation about how our content can best support teaching and learning. We know that there is no one right way to do this, so whether you’re a student, lecturer or researcher, we are interested in your feedback and want to hear from you.
The material shared on this site is in draft form. It is free to access, read and comment on, but must not be reused without prior permission of SAGE Publishing. For further information about reuse, please contact contracts@sagepub.co.uk.
This is a repository for reviewing draft chapters of the book From Social Science to Data Science, written by Bernie Hogan. This book is under contract with, and will be published by, SAGE Publishing.
Full citation: Hogan, B. (forthcoming), From Social Science to Data Science: Scaling Up Your Programming Skills in Python, London: SAGE. Paperback ISBN: 9781529707489.
The peer review process helps us and our authors develop the material they have written to ensure it is engaging, up to date and enables effective teaching and learning. We ask for feedback on various aspects of a proposal or chapter, from its structure and writing style to the technical aspects of its accompanying code.
Key to this process is feedback from anyone looking to use the book, whether as a teaching text, for their research, or for their individual study. We welcome feedback from voices across the community – researchers, students and academics at any stage of their career.
Do you have an opinion on the draft material available to view here? We invite to you to offer feedback through an issue template:
- Navigate to the chapter on GitHub
- Raise a GitHub issue for the chapter
- Fill in the placeholder questions
If you have other feedback please raise a regular issue. If you would like to send more detailed or anonymous feedback on this material, please email Charlotte Bush.
If you would like to discuss producing material for teaching computational social science, please email Jai Seaman.