My roundabout road into data science may be atypical.
In college, I took extensive computer science coursework; I discovered a love for mathematics,
to my surprise, through studying philosophy and
logic. But, when I graduated, I rejected working with computers, and instead wanted to follow my
passion for ecological work.
In what became an entrepreneurial endeavour, I helped farmers build
their onsite soil remediation infrastructure, using microscopy.
Wanting to learn more from publically available data sources on soil type and agriculture,
I became familiar with SQL and using Python for GIS analysis (using the geopandas
library).
I gradually became skilled in working with unruly, mangled, & enormous sources of data. I
witnessed firsthand
the transformative power of technology; by working with data streams, I gained skills to
further strategic goals of principled organizations and businesses.
Wanting to see what other data tools were available,
I fell in love with the evolving data science tech stack, and began to learn the ins and outs of
machine learning
algorithms while studying in Seattle. Now I'm
happy to contribute by using and improving the tools for building machine learning
pipelines.
Feel
free to
reach out if you'd like to collaborate.