Skilled in statistical programming, clinical data analysis, and health analytics — with hands-on experience in SAS, Python, SQL, and Tableau. Focused on turning structured data into reliable, evidence-based insights.
Built a full SAS reporting pipeline to generate baseline demographics and stratified cardiovascular risk summaries from a 5,209-record clinical dataset. Cleaned and validated data, recoded missing categorical variables, and automated professional clinical-style PDF reports using ODS.
Performed end-to-end data wrangling and exploratory analysis to identify emerging trends in programming languages, databases, and frameworks. Built interactive dashboards and presented findings through data storytelling techniques.
Preprocessed the Kaggle Wine Quality dataset using PCA and K-means clustering. Developed and evaluated a Decision Tree classifier identifying alcohol content as the strongest quality predictor, visualized through confusion matrices and scatterplots.
I have a strong foundation in data analysis, with experience working across Python, SQL, SAS, Excel, Tableau, and IBM Cognos Analytics to clean, analyze, and interpret data for real-world insights. Through academic projects and professional experience, I've worked on building structured datasets, performing data quality checks, and supporting data-driven decision-making.
My SAS experience includes DATA step processing, PROC MEANS, PROC FREQ, PROC SQL, and ODS PDF reporting — tools I've used to clean, audit, and generate reports on structured clinical-style datasets. One of my key projects involved building an end-to-end reporting pipeline that produced baseline demographics and stratified cardiovascular risk summaries from over 5,000 records.
With Python, I work primarily in Pandas, NumPy, Matplotlib, and scikit-learn — applying these across exploratory data analysis, statistical modeling, and machine learning workflows. I'm drawn to work at the intersection of data and health, particularly roles in clinical data analysis, health analytics, and statistical programming.
I am actively seeking opportunities where I can contribute to meaningful, evidence-based decisions while continuing to grow through my graduate studies.
Relevant Coursework: Exploratory Data Analysis · Data Mining · Big Data Analytics · Applied Linear Models · Statistical Inference · Principles of Machine Learning
Open to opportunities in clinical data analysis, health analytics, and statistical programming. Feel free to reach out.