Business Analyst
Business Analyst from San Jose, CA interested in learning more about data analysis and visualization. Enjoys cooking and watches a lot of cooking shows (favorite cooking shows: America's Test Kitchen and Great British Bake Off). Listens to podcasts (favorite podcasts: Keep It!, The Splendid Table, and Milk Street). Loves video games and playing all things Nintendo. Uses YouTube for pretty much everything (favorite YouTube channels: Bon Appetit, Binging with Babish, MKBHD, Zoe Hong, CollegeHumor, Today I Found Out, and anything by PBS).
I love working with others and learning new things. Below are a few of my strengths:
My three favorite projects are listed below. See my GitHub for a complete list of my work.
Story | Code
Analysis predicts housing prices in Ames, IA given a variety of metrics and descriptive factors. Uses Python (Scikit-Learn, Pandas, NumPy, SciPy, MatPlotLib, and Seaborn) to produce a single variable regression, multiple variable regression, and random forest regression and tells a story using Tableau.
Live | Code
Dashboard that visualizes listings, hosts, and reviews data for vacation rental properties in four Seattle neighborhoods. The web application uses Python (Flask and SQLAlchemy) to connect its back-end SQLite database to its front-end visualizations (created using the Plotly and ZingChart JavaScript libraries).
Code/Writeup
Creates a relational database covering peer-to-peer lending data from Kiva. Takes multiple CSV files, uses Python's Pandas library to clean the data and separate the data into concise dataframes, creates a schema of four tables (loans, regions, loan purposes/themes, and lenders) in MySQL, and pushes the cleaned dataframes into the SQL schema.