November 2020 was the lowest time for me during the COVID-19 pandemic. My boyfriend left to spend a month with his faimly across the country, while I stayed home in our apartment. Aside from a few days visiting my sister, I spent the month without human contact.
Hello readers! It’s been a long time since I posted on this blog. Years, actually. During that time, I finished my PhD at UC Berkeley and started working at Pinterest. It was an adjustment, to say the least. I traded my 10 minute walk to campus for an hourlong bus ride across the Bay Bridge; freedom to work from anywhere with Wifi for butt-in-chair from nine to five; the pursuit of knowledge for the pursuit of measurable business impact.
If you follow me on social media, you might’ve seen that I’ve been traveling a ton this past year, and most of it has been related to my grad school work. In my five years as a PhD student, I’ve visited five states and five countries for conferences and other events. As someone who didn’t travel much as a kid, I’ve been loving these opportunities!
Last week, I attended my first voting conference: E-VOTE-ID. I’ve presented at statistics conferences before but never an interdisciplinary one like E-VOTE-ID. It brought together people working on electronic voting issues from a whole range of disciplines: legal studies, sociology, cryptography and security, voting systems developers, former election officials, and one statistician. This guy!
I ALWAYS forget to put a license on my work until someone reminds me. I’ve learned over and over that it’s important, but I think the reason why it hasn’t stuck is that I was never taught why it’s important.
I’ve really been gotten on the crunchy bandwagon this year – buying high quality grassfed meats, organic produce, paraben-free beauty products, and swapping out plastic food storage containers for glass ones. Up until recently, I was skeptical about the evidence that these choices really make a difference for your health.
I participated in my first hackathon two weekends ago. I use code to do data analysis most of the time, not write apps or websites. For me, it was more of a fun learning experience and I got to see what kinds of work are expected and rewarded.
Fewer grad students are on the job market for faculty positions – is it because they realize that there are fewer jobs or because they are genuinely more interested in other career paths? Roach and Sauermann studied interest in academic careers in a way that has never been done before: longitudinal surveys of current graduate students. By giving people the survey twice, once in their first or second year of the PhD and then again three years later, they are able to measure changes in interest. Previously, people have only looked at cross-sectional data and compared two groups at different points in their PhD.
This week I had the privilege of participating in two workshops: I was a participant at a train-the-trainer workshop to become a Software Carpentry instructor and an instructor at the R Bootcamp put on by the Statistics Department and D-Lab. It was a unique opportunity to spend two days learning how to teach one of these bootcamps, and then to put my skills to the test a few days later.
A lightweight markup language is a simple, human-readable language for formatting text. It’s easy to read and compatible with most text editors. Documents written in lightweight markup are usually then converted to things that are harder for people, but easier for computers, to read, like HTML. The most common ones that I’ve heard of people using are Markdown, R Markdown, and reStructured Text. I imagine that most people who do data analysis/exploratory visualization/data science use a markup language more often than they write in raw HTML.
I’m in the early stages of creating several Python packages right now (shameless self plug – see permute, cryptorandom, and pscore_match). I want people to actually use them when they’re ready. They have potential for wide use, but they have narrow functionality compared to big packages like numpy or scipy. I could imagine that somebody looking to do a particular task in Python, like propensity score matching, would do a Google search and stumble upon my package.