Beyonka G. Powell

About Me

I am a dedicated and analytical problem-solver with over 13 years of experience as a Medical Laboratory Technician and a growing passion for data science. My journey from the lab bench to the data world was sparked by one powerful realization: data can drive decisions that save lives.

Now, I’m combining my deep understanding of clinical workflows with skills in Python, SQL, Power BI, and Excel to analyze problems, uncover insights, and improve healthcare operations. I bring a unique lens to data work that is grounded in real patient care, quality control, and time-critical decision-making.

Currently pursuing a Bachelor’s in Data Science at Arizona State University and recently earning a Certificate in Data Science & Visualization from SMU, I’m building the tools to turn complexity into clarity. Whether it's lab turnaround time or patient flow, I believe the right data, in the right hands, can make a measurable impact.

I'm excited to connect, collaborate, and continue growing in the world of health data and analytics.

Contact

beyonkap1986@email.com

https://github.com/Beyonka86

https://www.linkedin.com/in/beyonkapowell0405

(903)422-2266

Education

• Arizona State University
  Bachelor’s in Data Science (Expected 2028)
• Southern Methodist University (SMU)
  Certificate in Data Science & Visualization (2024)
• Navarro College
  Associate Degree in Biology (2007) & Medical Laboratory Technology (2012)

Projects

• Epic Lab Turnaround Analysis – Leveraging 13+ years of experience as a Medical Laboratory Technologist and my growing expertise in data science, I developed an end-to-end reporting dashboard that simulates the role of an Epic Beaker Analyst. This project identifies lab processing delays, highlights bottlenecks, and supports data-driven decisions to improve operational efficiency and patient care.
• Python API Weather App – The objective of this project was to analyze and visualize weather data over 500 cities, each located at varying distances from the equator. Utilizing linear regression techniques, the aim was to identify potential correlations between different weather parameters and their geographical positioning.
• Machine Learning – The objective of this project was to develop a predictive model to assess the probability of credit card customer attrition. This analysis will leverage a comprehensive dataset encompassing demographic details, financial metrics, and banking behavior. By examining these variables, the model aims to identify key indicators of customer turnover, ultimately enabling more informed strategic decisions to mitigate attrition risk.

Work Experience