About Me

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Hi! I'm Keegan, a data analyst with a background in business intelligence, analytics, and AI-enhanced data systems. I enjoy working on projects that combine technical problem-solving with practical business impact, whether that's building dashboards, developing data pipelines, analyzing complex datasets, or improving the reliability of AI-driven analytics tools.

My experience includes SQL, Python, Power BI, Snowflake, and machine learning projects spanning analytics, visualization, and data-focused web applications. I'm always looking for opportunities to deepen my expertise, explore new technologies, and build solutions that make data more actionable and accessible.

Feel free to explore my experience and projects below and reach out if you'd like to connect.

Professional Experience

Insight Analyst @ Retail Insight

Worked with analytics, reporting, and AI-assisted data systems to support retail-focused insights and business workflows. Contributed to semantic layer refinement, validation of AI-generated outputs, SQL analysis, and BI reporting within Snowflake and MS SQL Server-based environments.

Volunteer Data Analyst @ Modern Military Association of America

Performed analysis of organization case files, which involved data cleaning, transformation, visualization, and reporting. The goal was to assist the organization with quantifying their progress towards goals and to help set new, realistic ones.

Projects

Chicago Real Estate Data Mining & Analysis

Built a real estate analytics project involving large-scale housing data collection, transformation, and analysis using Python, SQL, and Power BI.

Sentiment Analysis in Spotify Playlists

Developed a Flask-based application that analyzes playlist sentiment using Spotify audio features, lyrical sentiment analysis, clustering, and custom metrics. Designed a SQL database to store pertinent data to allow for comparison between playlists.

Real Time VG Frame Classifier

Built a machine learning application capable of classifying real-time video game frames using computer vision and deep learning techniques.

Anomaly Detection in Automated Vehicle Sensors

Explored anomaly detection techniques on automated vehicle sensor datasets to identify irregular operational behavior and evaluate model performance.

Web Relational Database

Designed and implemented a relational database-backed web application demonstrating database modeling, querying, and web integration concepts.