Machine learning (random forest, gradient boosted machines, lasso, etc.) in R using caret/h2o.ai/SuperLearner, text analytics / NLP, causal inference (propensity scores, matching, IPTW, targeted learning), randomized experiments in Stata, parallelization & high performance computing, and general statistics & survey-related data analysis. Scientific computing using SQL databases, Git, Amazon EC2, Markdown, Latex/Knitr, Unix command line, Savio / SLURM, and Berkeley Common Environment.
When submitting a consultation request please give a brief description of the research project & dataset, the specific problem you are having, and your rough availability to meet. Unfortunately I do not have capacity to assist with coursework - apologies.