Data Science for Social Science Research

Quantitative research in political science requires the measurement or prediction of the outcomes or variables that we use in research. This course provides students with the tools from data science to be able to do this. It covers a variety of topics in social data science. These include, among others, (1) data collection through APIs; (2) pre-processing techniques for text data; (3) the processes of training supervised learning models (performance metrics, cross-validation, training-test splits); (4) the models used in supervised learning; (5) neural networks, word embeddings, and transformers; (6) applications of large language models; and (7) item-response models for scaling. The class is taught in R.