What I’m working on

I have a couple of side projects I’m pursuing while I’m juggling my full time employment and two graduate school courses.

First is comparing schools using a metric I created called Student Independent Performance, hosted at github.com/josephscheidt/sip, where I am attempting to provide a way for parents, educators, and administrators to evaluate school performance independent of the racial and economic demographics of its student body.

Also, just for fun, I’m tracking out-of-state endorsements in the 2020 Democratic presidential primary at github.com/josephscheidt/2020_endorsements. I’m hoping to pinpoint the moment when a national groundswell gets behind a particular candidate.

What I’m learning about

I’m in the middle of my third term in the Master in Data Science program at Johns Hopkins University.

I’m taking a data visualization course where I am learning the science behind how we process information and the latest tools and techniques for producing clear, complex, and engaging visualizations.

I’m also taking a machine learning course where I get to not only study different machine learning algorithms, but implement them from scratch. I’m not allowed to post my code for these publically, but if anyone not in the JHU program wants to see how I implemented naive bayes, k means clustering, HAC, or k nearest neighbors shoot me an email and I’ll send it to you.