A blend of academic rigor and hands-on training, this is how a lifelong learner reverse-engineered a data science education.
Credentials first. Story follows.
Credential Links
- Data Analytics Boot Camp, George Washington University
- Stanford: Statistical Learning
- Google Advanced Data Analytics
- cs50: Artificial Intelligence (AI)
Advanced Analytics & AI
- Google Data Analytics
- cs50: Python
- cs50: SQL
- cs50: R
- cs50: Web Programming
- cs50: Introduction to Computer Science (HarvardX)
Data & Coding Foundations
- Excel for Data Analytics
- Statistics with Excel
- Excel Forecasting
Excel Analytics & Forecasting
- Google UX Design
- Google Project Management
- cs50: Cybersecurity
Strategic Extensions
Academic Degrees
- M.A. International Affairs, American University
- M.A. Natural Resources and Sustainable Development, United Nations University for Peace
Reverse-Engineering a Data Science Education
Two Master’s degrees were enough tuition for a lifetime. When I pivoted to data analytics, I didn’t go back to school; I built my own. Starting with a rigorous boot camp, I layered in top-tier MOOCs from Harvard, Google, and soon Stanford, stitching together the depth of a data science degree without the debt.
I don’t collect credentials; I pursue mastery. I follow knowledge down the rabbit hole wherever it leads—past Python and SQL, through statistics and machine learning, and into the frontier of what’s next.
These certifications reflect more than technical ability. They reflect curiosity, intention, and a respect for craft over shortcuts. I wanted the hard skills to complement a broad, interdisciplinary background, and the fluency to make data speak human.
Credentials
Advanced Analytics & AI
Data & Coding Foundations
Excel Analytics & Forecasting
Strategic Extensions
Academic Degrees
Side Quests
Easter Eggs. Conversation starters and whimsical extras.