Reflections on the 2018 California Water Data Challenge

UPDATE: I am so honored to have won an award in the 2018 CA Water Data Challenge! You can view the wining project here. After graduate school, I’ll probably apply to as many jobs as I can and see what sticks. But deep down, a career that integrates my domain knowledge of groundwater and California water with the broad field of data science is what I seek. So when the California Water Data Challenge was announced earlier this year, I was pretty excited.

Installing the R kernel in Jupyter Lab

I was recently pretty impressed with Jupyter’s newest creation, the Jupyter Lab IDE. You can test drive an online demostration of Jupyter Lab with a Python and R kernel here. I downloaded a version, and found that the R Kernel was missing! To get the R kernel up and running in Jupyter Lab was a bit more complicated than expected. This guide is meant to break things down step by step.

Aquifer thickness calculated from ~300,000 well logs

Data visualization from hundreds of thousands of well completion reports reveals aquifer characteristics in California groundwater basins.

Reflections on Rstudio::conf2018

That’s little ol' me in the middle. Not Hadley Wickham – the other non-noticable guy with the black hair. Photo credit: sharon000 Heading North on I-5, we pass by brown hills and palm trees as San Diego fades in the rear view mirror. The smell of last night’s beach bonfire wafts from our jackets stowed in the back seat, and Google Maps notifies us we’re about 7 hours from our respective destinations: Berkeley and Davis, CA.

Tidy chi-squared stats in infer

Using the infer package for inference, with attention towards chi-squared tests.