Python Programming for Social Science Research

In this Open Lab, Digital Humanities Analyst Namig Abbasov will utilize Python to navigate the key stages of quantitative data analysis in social science research. First, we will preprocess data, recoding and transforming necessary variables to ensure that the dataset is ready for statistical analysis. Subsequently, we will employ Python libraries to conduct Exploratory Data Analysis (EDA), making statistical graphs and various data visualizations to illustrate patterns, detect possible anomalies and understand overall data structure. In our final step, we apply inferential statistical methods, where we will generate regression tables and calculate marginal effects to interpret the findings.