Doing Computational Social Science
A Practical Introduction
- John McLevey - University of Waterloo, Canada
Computational approaches offer exciting opportunities for us to do social science differently. This beginner’s guide discusses a range of computational methods and how to use them to study the problems and questions you want to research.
It assumes no knowledge of programming, offering step-by-step guidance for coding in Python and drawing on examples of real data analysis to demonstrate how you can apply each approach in any discipline.
The book also:
- Considers important principles of social scientific computing, including transparency, accountability and reproducibility.
- Understands the realities of completing research projects and offers advice for dealing with issues such as messy or incomplete data and systematic biases.
- Empowers you to learn at your own pace, with online resources including screencast tutorials and datasets that enable you to practice your skills and get up to speed.
For anyone who wants to use computational methods to conduct a social science research project, this book equips you with the skills, good habits and best working practices to do rigorous, high quality work.
Nice introductory text on computational methods for social scientists using Python. The text is rather comprehensive and covers a lot of contemporary problems which may be of great interest for aspirant scientists/ practitioners in their daily work.
The McLevey book covers modern machine learning style research for both information science and computing students. For other social science students who have programming skills, or are at a graduate level, then this book might also be a good resource with plenty of useful information. However it is written using a handbook style and lacking an academic thrust. It is very practical and exercise based. Students would still need the Oates book to help them writeup.