This repository contains example codes in Python, Stata, and R that may be relevant in assessing my proficiency in computational and empirical research.

Contains three .ipynb assignment files for the class Large-scale Computing for the Social Sciences at the University of Chicago, exemplifying exposure to methods such as PySpark, GPU computing (with pyopencl), Slurm batch jobs, as well as those used in conjunction with AWS resources

Contains two .py files that collects URL and within-page information for the UChicago MA thesis research, which uses Selenium to conducting "dynamic" web-scraping and joblib for embarrassingly-parallel execution.

Contains a .ipynb file that compares different machine learning models with hyperparameter tuning (via cross-validation) to show basic exposure to methods in machine learning. Also contains a .pdf version of an .ipynb file used in completing an assignment for the class Applied Econometrics at the University of Chicago, testing different nonparametric methods.

Contains three .do files. simulation_exercise.do was written to complete an assignment for the class Applied Econometrics at the University of Chicago, testing different estimation methods (fixed effects and instrumental variables). survival_methods.do was written to complete another assignment for the class Econometric Applications at Princeton University, testing different survival regression methods. Finally, data_analysis_example.do is a sample for data cleaning and conducting basic regressions using Stata.

Contains a .rmd file written to complete an assignment for the class Applied Multivariate Analysis at the University of Chicago, which contains basic commands for statistical analyses and visualization.