Throughout my career, I have worked at the intersection of legal processes, operational coordination, and data-driven decision-making—helping teams manage large-scale workflows, improve documentation, and execute consistently in high-demand environments.
Applying data analysis and visualization has strengthened my ability to examine complex systems through a quantitative lens and identify opportunities to improve workflows and operational outcomes. Through these projects, I focus on using data to uncover patterns, improve processes, and support more informed decisions. I’m particularly interested in how technology and analytics can enhance operational efficiency and help organizations better understand the systems that drive their work.
HTML5, CSS3, JavaScript, Splinter, BeautifulSoup,
Bootstrap, Flask, Mapbox, Leaflet, Tableau,
R, TensorFlow, PostgreSQL, PG Admin, SQLite, SQLAlchemy, MongoDB,
AWS, PySpark, Google Colaboratory, Excel, Jupyter Notebook, Visual Studio Code
Python & related libraries including Pandas, SKlearn, and Holoviews
This project presents an interactive dashboard hosted on GitHub Pages that allows users to
filter and explore study results through dynamic visualizations. By enabling users to interact
directly with the dataset, the dashboard helps reveal patterns and trends, providing a clearer
understanding of the underlying data.
Technologies: JavaScript, HTML, CSS and GitHub Pages
This project analyzes potential bias in Amazon’s paid Vine reviews by examining patterns
within a large review dataset. Using a structured data pipeline, the dataset was processed
and cleaned with PySpark in Google Colab, stored in AWS, and further analyzed using PostgreSQL
and Python in Jupyter Notebook. The analysis explores whether paid product reviews show different
rating trends compared to non-Vine reviews.
Technologies: Python, Pandas, Jupyter Notebook, AWS, Google Colaboratory and PG Admin
This project analyzes Citi Bike ridership data to assess the feasibility of launching a similar bike-sharing program in Des Moines. Key patterns in rider behavior and system usage
were explored and presented through an interactive Tableau dashboard to highlight trends and support data-driven insights.
Technologies used: Tableau, Python, Jupyter Notebook
This project uses statistical analysis in R to evaluate vehicle production data and
identify factors influencing manufacturing performance. Techniques such as summary statistics,
linear regression, and hypothesis testing were applied to examine variability in vehicle
performance metrics and uncover potential sources of production issues. The analysis
demonstrates how statistical methods can support data-driven decision-making in manufacturing
environments.
Technologies: R, Rstudio
This project demonstrates an interactive web interface that allows users to explore and filter a dataset
using multiple criteria. Built with JavaScript, the application dynamically updates a table stored as a
JavaScript array, enabling users to refine results and quickly locate relevant records. The project
highlights how client-side scripting can be used to create responsive, data-driven web experiences.
Technologies: JavaScript, HTML, CSS, Bootstrap
The aim of this project is to analyze weather data to assist a potential
client in assessing the viability of a proposed business venture.
Technologies used: Python, Jupyter Notebook, SQLite and SQLAlchemy
The main goal of this project is to build functions for creating an automated pipeline.
This pipeline will streamline the Extraction, Transformation, and Loading (ETL) process
of data from multiple sources, ensuring the availability of clean data ready for loading into an SQL database.
Technologies used: Python, Pandas, Jupyter Notebook, PostgreSQL and PG Admin
The objective of this project is to create an interactive web application
designed to scrape Mars-related data and present it to users in a user-friendly format.
Technologies used: Python, Jupyter Notebook, HTML, Bootstrap, Flask and MongoDB
This project applies unsupervised machine learning techniques to analyze and categorize
cryptocurrencies based on market data. Using tools such as Pandas, Scikit-learn, and HoloViews,
the dataset was cleaned, processed, and visualized to identify patterns and group similar
cryptocurrencies through clustering. The analysis demonstrates how machine learning can be used to
uncover structure within complex financial datasets and support data-driven market exploration.
Technologies: Python, Pandas, Jupyter Notebook, PostgreSQL and PG Admin
This project analyzes workforce demographic data to identify employees approaching retirement
eligibility and assess potential mentorship opportunities. Using PostgreSQL and PGAdmin,
SQL queries were developed to examine employee records, generate summary datasets, and highlight
trends related to workforce transitions. The analysis demonstrates how structured data querying can
support workforce planning and strategic organizational decision-making.
Technologies used: Python, Jupyter Notebook, HTML, Bootstrap, Flask and MongoDB