Apopka, Florida, United States
I bring a unique blend of clinical expertise and technical skill to data analytics. As a chiropractor, I spent over eight years analyzing patient data, improving workflows, and supporting clinical decisions for better outcomes. That experience gave me firsthand insight into how data can drive real improvements in patient care. To deepen my impact, I built a strong technical foundation in SQL, Python, data visualization, and machine learning. I have applied these skills to projects in predictive modeling, time series forecasting, and process optimization, always with a focus on turning raw data into actionable insights. My background allows me to bridge two words. I understand the challenges clinicians face, and i know how to design data solutions to actually support decision making. Whether it is validating data, building dashboards, or optimizing workflows, I deliver results that combine precision with practicality. I leverage both my clinical experience and technical expertise to improve patient outcomes and organizational performance. Let's connect! [email protected]
- Collected, cleaned, and transformed large datasets from multiple sources using SQL and Python to generate actionable insights - Applied exploratory data analysis, pattern recognition, and feature extraction to identify trends, outliers, and hidden relationships - Conducted risk and gap assessments by modeling datasets, developing metrics, and recommending data driven improvements - Created clear visualizations and reports to present findings, improving decision making across technical and nontechnical teams - Developed and evaluated machine learning models, including classification and regression tasks, to predict outcomes and strengthen strategic planning Key Skills: SQL, Python, Data Visualization, Exploratory Data Analysis, Machine Learning
- Managed patient care for over 1,000 individuals, applying imagine, diagnostics, and outcomes data to guide treatment decisions - Designed and implemented intake and workflow processes that reduced patient processing time by 25% - Collected, validated, and analyzed patient data to improve compliance and treatment accuracy - Delivered education and data tools that increased diagnostic precision and patient adherence by 30% - Partnered with insurance providers, healthcare teams, and regulatory bodies to ensure accurate documentation and reporting - Conducted analysis of imaging studies, identifying patterns and data that supported accurate diagnoses and treatment planning - Built reporting workflows that improved data reliability and reduced documentation errors - Applied evidence based methods and patient outcome tracking to strengthen care planning and operational decision making