Data Analysis

Overview

During my internships (especially during my tenure at the Harvard Kennedy School) and while working on personal projects, I’ve picked up many data analysis related skills. In school, I tuned my understanding of theory and statistics and familiarized myself with a variety of the methodologies, tools, and patterns used in the broadly defined field of "data science.”

I’ve learned techniques of data cleaning through GUI tools like Data Wrangler in addition to more conventional libraries like Pandas in Python and to a lesser degree, Dplyr in R.

I’ve also worked extensively with helper libraries like numpy and scipy to perform regressions, do statistical tests, and conduct various analyses depending on the task. I’m quite comfortable playing with and molding data in Jupyter notebooks to then be able to run a swath of analysis on the data using the aforementioned tools (and others).