Product @ Kaggle
. Community, data science, language, & weirdness
Yesterday, it was a great honor to be a guest on Nick Wan’s data science livestream on Twitch to talk through bad (and less bad) data viz that I’ve created.
-- It was a lot of fun! Talking about the history of my popular Titanic R notebook on Kaggle was a great opportunity for me to reflect on my data science journey. Its explosive success was very unintended. But as a result I’ve got a couple of cool insights to share about this experience and how I apply them in my role as a product manager at Kaggle today. Keep Reading...
I’ve worked at Kaggle on-and-off since 2016. In this time, THE most consistent source of user feedback is about the reliability of Kaggle Notebooks. Sessions were slow to start and, far worse, sometimes users would lose hours of work. While progress had been made over the years, we’d never systematically addressed the problems.
Over the past half year or so since I’ve rejoined Kaggle, the Notebooks team renewed its focus on reliability. Keep Reading...
This week I asked for feedback on something I wrote from a trusted colleague. My audience was larger than usual for me and 100% men (as far as I knew). Although I’m already pretty confident in my prose-writing skills, I felt greater than usual pressure to make a compelling, tight story. I wanted what I wrote to sound authoritative and well informed.
My colleague gave me great constructive feedback and overall really liked what I wrote. Keep Reading...