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: Customer Service • Python • Tableau
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I am a critical thinker, curious, hard-working, out-of-the-box, a keen listener and problem-solver. My experiences are wide-ranging. I can lead and follow. I can deal with uncertainty, but wrong answers are a splinter in my mind.
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My Capstone Project: A Tale of Two Rates: An Analysis of HUD Fair Market Rents and the Availability of Choice in Housing Choice Vouchers
We used time series and geospatial analysis to answer the following questions: 1) Since 2017, have Fair Market Rents accurately tracked actual Median Market Rents? and 2) How much choice of housing location have Housing Choice Vouchers actually provided? We limited the scope of this project to the San Antonio-New Braunfels Metropolitan Statistical Area and focused on two-bedroom properties. Data was acquired from HUD, Realty Mole, and Bigger Pockets APIs as well as HUD aggregated historical data in .csv format. We found that HUD FMRs did not keep pace with the actual rental market between 2017 and 2023 and that this substantially reduced the number of ZIP codes where someone with a Housing Choice Voucher could afford housing.