Skills

• Programming and Tools: R, Python, SQL, LaTeX, Git, Stata
• Packages and Libraries:
  • R: ggplot2, caret, tensorflow, tidyverse, forecast, lme4, glmnet, keras, shiny, stringr, xgboost
  • Python: pandas, numpy, tensorflow, keras, matplotlib, sklearn, seaborn, plotly
• Data Science and Machine Learning: Lasso and Ridge Regression, Random Forest (Classification and Regression), Logistic Regression, Linear Regression, Optimization Methods, Decision Trees, K-Nearest Neighbors (KNN), K-Means Clustering, XGBoost, Principal Component Analysis (PCA), Monte Carlo Simulation, Data Mining, High-Dimensional Data Analysis
• Mathematical and Statistical Skills: Linear Algebra, Vector Calculus, Data Wrangling, Statistical Inference, QA/QC, Imputation, Experimental Design
• Visualization and Communication: Data Visualization (R and Python), Mathematical communication at various skill levels
• Languages: Mandarin (Proficient)