My Story

Hey there. Just a long distance-running, Bukowski-loving, data-driven explorer here, taking nonlinear paths to linearly driven destinations since discovering my deeply ingrained sense of curiosity at an early age.

My own transition back into a quantitative mindset began in the Bay Area hub of data science. After taking on statistics and R at UC Berkeley followed by tackling my own cognitive neuropsychology research at UCSF, I quickly revitalized my love for solving complex problems with computational tools.

While actively pursuing this love, I have learned principles of machine learning, coding, project pipelines, and data management through both online and offline ventures. Along the way, I also found that embracing the data nerd within didn’t necessarily lead to simultaneously negating the inner artist. For me, forging a harmonious path between the quantitative and qualitative has underscored the value of integrational tech and platforms featuring diverse voices.

For a closer look into the technical side of my journey, check out my learning path below.

My Learning Path

Check out my timeline or my Learning: Data Science GitHub repo for a deeper look into my quantitative training and the tools I've picked up and continue to expand upon along the way.

Technical skills include: Python, R, SQL, Tableau, Excel, HTML, CSS, SPSS

"Learning: Data Science" Repo

Github repository of programming projects and related materials completed throughout my personally-customized data science training.

Learn more

\ “All things change in a dynamic environment. Your effort to remain what you are is what limits you.” \