Hello! I'm Tyler, a financial data analytics professional with just over a decade of experience, primarily in the healthcare industry. Recently, I've been working to expand my skillset by incorporating more data science techniques into my existing financial and healthcare analytics work. My goal is to drive even more impactful, data-driven solutions and insights across these areas.
- 💼 Professional Background: Extensive experience in healthcare, with a focus on financial model development, dashboard and report building, and data analytics
- 🎓 Education:
- IBM Data Science Professional Certificate (2024)
- Data Science in Stratified Healthcare and Precision Medicine Certificate (2024)
- Udemy Power BI Desktop for Business Intelligence (2024)
- B.S. in Finance (2013)
- 🌱 Currently Learning: Building more robust dashboards in Power BI utilizing DAX and Power Query.
- 🥅 Career Goals: As I further develop my data science toolbelt, I aim to serve in roles where I can utilize machine learning to improve public health and better understand the hidden social determinants (X) of health (y).
- 🛠️ Tools:
- Languages: SQL, Python
- Data Manipulation/Machine Learning: Pandas, NumPy, Scikit-Learn
- Visualization: Tableau, Matplotlib, Seaborn
- Others: Excel, Cuisinart ice cream maker
- 📫 Connect with me: LinkedIn
| Project | Skill Area | Description | Tools |
|---|---|---|---|
| Health Center Data ELT | ELT, database management, EDA, data querying | This project was motivated by the limitations of pre-built reports provided on the HRSA website. While high-level summaries were available, I wanted full access to the raw data to enable more granular analysis and custom reporting. To achieve this, I retrieved HRSA's annual UDS data, loaded it into a MySQL database, and performed data wrangling to ensure the dataset was clean and optimized for querying. By building my own SQL queries, I gained the flexibility to extract specific insights not readily available through standard reports. | SQL |
| Health Center Dashboards | Data visualization, dashboarding | My goal for this project was to transform my health center data into dynamic visualizations that offer deeper insights. To achieve this, I built two Tableau dashboards: one to visualize aggregated national-level data, providing a broad overview of HRSA's health center program, and a second to enable analysis of individual health centers through an interactive dropdown menu, allowing users to select and explore any specific health center’s data. | Tableau |
| Payer Mix Linear Regression | Linear regression, lasso regression, supervised machine learning | Accurately predicting payer mix percentages is important for health centers to optimize financial planning and resource allocation. To address this need, I developed linear regression models to predict payer mix distributions based on financial, clinical, and patient demographic data of health centers. | Python (Pandas, Scikit-Learn, Matplotlib, Seaborn) |
| NHANES k-means Clustering Analysis | k-means clustering, unsupervised machine learning | To explore patterns and groupings within health-related data, I applied k-means clustering to the NHANES dataset, focusing on key health features such as demographics, lifestyle factors, and clinical measurements. By clustering survey respondents, I was able to identify distinct population segments with similar health profiles within the NHANES dataset. | Python (Pandas, Scikit-Learn, Matplotlib, Seaborn) |