This is a visualization of votes on the Supreme Court of the United States (SCOTUS) from 1946-2022. The goal is to see if and how Justices on the Court appear to relate to one another – do they form voting blocs, and if so, do they appear meaningful?
Data is everything. Even for physics. Especially for physics.
Physicists (both praciticing and in training) rely on data for insight into the laws of the universe. However, processing data effectively, analyzing data correctly, and presenting data elegantly are skills often learned on an ad hoc basis, rather than in a classroom setting.
Will Beason and I set out to correct that.
Will and I co-wrote Data Analysis for Physics as an online introductory textbook using Brad Miller's runestone system. In it, we cover the basics of using Wolfram Mathematica, LaTeX, and basic statistics as a bare-bones introduction to professional physics (really, professional research). Though many of the examples come from a physics perspective, the techniques are far more general.
The course materials have been used for multiple 1-hour, for-credit, student-led classes at UT-Austin since Will and I co-led the first iteration in Spring 2014. I'm especially thankful to Dr. Greg Sitz who handed us the reins for the curriculum in a grand educational experiment.
What's in a Like?
MathematElection is a computational politics blog that uses Like data from Facebook to predict Election 2016. My goal is to provide exploratory data analysis and (admittedly lighthearted) predictions, delivering insights from data to a broad audience. So if you want to know who our next president will be, want to learn some coding or statistics, or even become the next Nate Silver, this is the blog for you!
In addition to the creating the blog itself, I've presented on this project at the 2015 Wolfram Technology Conference, as Wolfram technologies comprise the computational backbone for my work. The code is available on GitHub. My presentation may be found here (PDF), here (Mathematica Notebook), and on YouTube.
To help simplify the constant decision process for eating out, I built my own restaurant generator. It's doubtful that it's helpful to anyone outside central Austin, perhaps only useful to me (and even then, the pandemic sadly claimed some of these institutions). But, I had fun putting it together and used it regularly. It's an idea completely taken from restaurantgenerator.com which works seemingly anywhere in the USA, and pulls live data from Yelp. It's great, but kept suggesting places that I didn't want after using it for many months.