Continuing my fascination with cruise ships, I decided to try and build heatmaps from the cruise ship location data I gathered in a previous post.

I just got back from my honeymoon, my first cruise to Alaska on the Explorer of the Seas. Aside from having a fantastic time with my wife, I gained a new fascination with the breadth and scale of cruise travel. Our ship housed upwards of 2,000 passengers and was often one of two or three ships docked in a port. Multiply that by the 100+ ships that are currently running cruises, you can have 200,000 people on the ocean at a time visiting dozens of ports across the globe.

For this year’s March Madness tournament, I built an algorithmic bracket picker based on Dungeons and Dragons dice-roll battles. The bracket it generated, that I submitted for my company’s bracket competition, did surprisingly well: it correctly picked UNC to win the tournament and placed first overall in our bracket competition.

This March several podcast publishers are participating in “Try pod,” a campaign to encourage people to give podcasts a try. Unlike most people using the #trypod hashtag, I’m not going to use the event as an excuse to hawk my own podcast (I don’t have one.) Instead, I am publishing a large dataset of podcasts and episodes on data.world in the hopes that it will inspire some cool analytics or tools.

A while back, there was a thread on Hacker News asking people to submit their employer and salary information. Being in the start-up scene and following Hacker News pretty closely, I was instantly very interested in the data this survey was going to produce. I started working on cleaning it up that night, and published the code to Github. In this post I’ll be presenting the ETL code I wrote and analyzing the results.