Core Technologies: Python, MySQL, Spark, Tableau, Pandas, NumPy, Matplotlib, Seaborn, SciKitLearn, Anaconda, Jupyter Notebooks, Git/GitHub
Core Competencies: Data Storytelling, Applied Statistics, Machine Learning, Natural Language Processing, Classification, Regression, Clustering, Time Series Analysis, Anomaly Detection
Primary Focus: Data Analytics
Additional Skills: Microsoft SQL • Python • Tableau
I possess a remarkable ability to adapt to new environments and challenges, coupled with an unwavering determination to accomplish any task presented. My previous experience demonstrates my capacity to excel in diverse situations and deliver exceptional results. With my adaptable nature and tenacious work ethic, I am confident I will be an invaluable contribution to your team and exceed your expectations.
My Capstone Project: A Tale of Two Rates – A Market Analysis of HUD Fair Market Rent
Every year the Department of Housing and Urban Development (HUD) releases a list of Fair Market Rents (FMRs) for over 2,600 areas across the United States. FMR is the baseline for various government programs that provide housing assistance to low-income families, such as the Housing Choice Voucher Program. The accuracy of FMR is imperative for the livelihood of the millions of Americans who depend on these programs. This project aims to analyze HUD’s FMR against 3rd party market rent data to determine the accuracy of FMR in a real world market.
This project is Python coded. A combination of APIs, Pandas, Matplotlib, and Seaborn were used to acquire and explore the data set. Through exploration it was discovered FMR is not keeping pace with market rent, and there is a decline in ZIP code affordability in the San Antonio metropolitan area.