I was looking for some fun time series data to hack on. Turns out bike computers provide a great way to interface between your friends’ unfounded modesty and their actual rides. One friend was kind enough to donate some data for my entertainment. This friend wears spandex, rides a titanium frame road bike, and sweats a lot. I knew he was a strong cyclist. He’s also relatively consistent, which meant the data he gave me wasn’t too terribly noisy.

A lot of people have asked me why I’m not working in academia or biotech, how I learned to code, or how I got into data science. Here are some answers to some of those questions. Are you sure you wouldn’t rather be in academia? Yes and no. Yes, I wanted my own lab. No, we don’t always get what we want, even after 7 years of postdoc. Long story short: bad timing.

This post is about a couple of painful problems I’ve had with reading in text files. They’re just text files, right? What could be hard about that? 1. Fun with encoding The problem: A friend sent me a dump of data from iTunes, as a table. Just a text file. She’s on a Mac. I’m on a Mac. But when I went to read in the file, Python choked in a way I hadn’t seen before.

So let’s say you’re thinking about attending a conference in tech, or some STEM field. Maybe you’ll be going alone. Maybe you’ve never been to a conference before, or this conference has a reputation for having, shall we say, “a higher risk of harassment”. Here’s a ‘game plan’ for things to keep in mind. I’m not saying anyone should ever have to do this, I’m saying this is more or less what I did when I was younger and had to go to STEM meetings, usually alone, and didn’t always feel safe.

I spent about a year learning Python when I first got back into coding. A lot of people have asked me where to begin. Here’s a guide to what I found useful (or not) and why. Learn Python The Hard Way by Zed Shaw. I started here. I did the free online version. I like the advice he gives you to just type things in, even if you don’t know what’s happening yet.

Once upon a time, I looked at a couple of data sets that involved geographical data. I wanted to actually plot the data on a map, so I had to do some shopping around for easy ways to do this quickly with python. What I wanted to do One of the data sets provided zipcodes, which I was able to convert to latitude and longitude (more on this below). At a minimum, I knew I wanted to be able to plot: a) location, b) a number value and text label, c) multiple colors designating groups of data points, e.

More than once, and probably not for the last time, I have done a technical interview for which I was underprepared. I feel like no matter how much I try to prepare, I am always underprepared for technical interviews. I’m going to tell you about a time I was underprepared for a few reasons, including: a) It was the first interview where I was asked to write more than a couple lines of recursive code

Inspired by a couple of great posts by Michael Gibson, I want to talk about what robustness means to me, as someone coming from a science background. I started working in a “wetlab” doing benchwork cancer research when I was in high school (I was 16). I learned early on that mistakes are: normal avoidable necessary. That may sound paradoxical, so I’ll explain. Some failures will always happen: the phone rings and you drop something.

Over the years, I’ve attended a lot of technical (read: software) talks. Once upon a time, I attended PyData, at Facebook. I’ve noticed at all of these events that the quality of presentations varies widely. If you’re like me, and you go to technical meetups and conferences around the Bay Area, for example, you’ll notice that some talks are well-rehearsed. But some seem like they were thrown together at the last minute.