On this page, you can find a collection of resources that can help you in doing Computational Social Science using Python. For recent work happening hear in Amsterdam, have a look at the Computational Communication Science Lab Amsterdam.
First of all, the following manual – which is the basic teaching material of the course Big Data and Automated Content Analysis – should be a good starting point.
Trilling, D. (2017). Doing computational social science with Python: An introduction. Social Science Research Network. Version 1.0 http://ssrn.com/abstract=2737682
The manual is regularly updated. The link above provides the most recent version of record, but if you want, you can also find the very latest updates (which might not be finalized) on github.
Once you have a good grasp of the basics of Python, you might want to use some of the following additional tutorials that cover use cases that are not included in the manual.
- A general introduction to data wrangling with pandas (Pandas Cookbook by Julia Evans) [download it from github]
- Descriptive statistics, correlations, t-tests, linear regressions, and simple visulalizations using pandas, statsmodels, and matplotlib (by Damian Trilling) [download it from github]
- Topic Modeling (LDA) (by Cornelius Puschmann, Tatjana Scheffler, Damian Trilling) [download it from github]
- Time series analysis using pandas and statsmodels (by Joanna Strycharz, Damian Trilling, & Nadine Strauß) [download it from github]
- I recently encountered a really good tutorial about Network analysis with Python by Vincent Traag, which I would like to recommend you [github].