As data become ‘big’, fast and complex, the software and computing tools needed to manage and analyse them are rapidly developing. Social scientists need new tools to meet these challenges, tackle big datasets, while also developing a more nuanced understanding of – and control over – how these computing tools and algorithms are implemented. Programming with Python for Social Scientists offers a vital foundation to one of the most popular programming tools in computer science, specifically for social science researchers, assuming no prior coding knowledge. It guides you through the full research process, from question to publication, including: • The fundamentals of why and how to do your own programming in social scientific research • Questions of ethics and research design • A clear, easy to follow ‘how-to’ guide to using Python, with a wide array of applications such as data visualisation, social media data research, social network analysis, and more. Accompanied by numerous code examples, screenshots, sample data sources, this is the textbook for social scientists looking for a complete introduction to programming with Python and incorporating it into their research design and analysis.
Written in straightforward language for those with no programming background, this book will teach you how to use Python for your research and data analysis.
Text is everywhere, and it is a fantastic resource for social scientists.
Biernacki, R. (2012) Reinventing Evidence in Social Inquiry: Decoding Facts and Variables. New York: Springer. Biernacki, R. (2015) 'How to do things with historical texts'. American Journal of Cultural Sociology, 3 (3): 311–52.
This open access book offers an initial introduction to programming for scientific and computational applications using the Python programming language.
This comprehensive guide provides a step-by-step approach to data collection, cleaning, formatting, and storage, using Python and R.
This book introduces students with little or no prior programming experience to the art of computational problem solving using Python and various Python libraries, including numpy, matplotlib, random, pandas, and sklearn.
... Analysis in Python for Social Scientists: Discovery and Exploration Dirk Hovy Unsupervised Machine Learning for Clustering in Political and Social Research Philip D. Waggoner Using Shiny to Teach Econometric Models Shawna K. Metzger ...
Auto-didactic Approach To get the most out of the book for self-teaching purposes, we recommend starting with the introduction to Python in Chap.1 and following the references for additional reading until you are comfortable writing and ...
F. H. Wild III, Choice, Vol. 47 (8), April 2010 Those of us who have learned scientific programming in Python ‘on the streets’ could be a little jealous of students who have the opportunity to take a course out of Langtangen’s Primer ...
Real-world data sets are messy and complicated.