I know that from the outside, ‘science’ seems like The Place Where Scientists Live. But ‘science’ is not a monolithic, homogenous thing. Not all scientists are the same.

Today someone called me a Biologist. But I was never really a Biologist. My undergraduate degree was in a chemistry department.

My past life as a researcher was always very interdisciplinary. To better understand cancer cells, I used a lot of sophisticated software, and mathematical intuition, in addition to chemistry and physics.

I have had a few pleasant job interviews. Here’s what’s different about those interviews, that made them really stand out from the others I’ve done. I’ll describe a specific example, and then give some specific suggestions.

1. The hiring manager contacted me directly
2. He had done his homework. He had looked at my GitHub repos.
3. He told gave me pretty specific information about the structure of the interview, and gave me ~2 weeks to prepare.
4. The interview was 1-on-1, in person. It lasted 2-3 hours.
5. The first exercise was to have me go through an app and describe what all the pieces were doing.
6. The second exercise was to interact with the app and add a feature or two.
7. The third exercise was to look at a script and identify the bugs (or other problems with it).

It was an outstanding experience because he had clearly put time and effort into preparing the process, and was patient with understanding that while he knew the app inside and out, I did not.

Once upon a time, a friend recruited me to do some technical writing for the company where he works now. Basically, they needed someone to quickly revise and update the documentation for their software.

Most modern user-friendly software documentation isn’t just writing, though. It’s screenshots. A LOT of screenshots. So you don’t just write “click on the blue box”, you also show a picture of it, like this. See the blue box?

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. But everyone forgets to turn on the computer sometimes, or forgets to charge the battery.

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. It wasn’t bad enough that my thesis advisor had some personal and professional crises while I was his first grad student, but one of our lab members committed suicide on September 10th, 2001. I graduated the following year. Then my first postdoc advisor died. Plenty of other things went wrong, but at the end of all that, in spite having published some papers, I still couldn’t get a faculty position.

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.

Thanks to a friend who wants to help more women get into tech careers, last year I attended Developer Week, where I was impressed by a talk about Neo4j.

Graph databases excited me right away, since this is a concept I’ve used for brainstorming since 3rd grade, when my teachers Mrs. Nysmith and Weaver taught us to draw webbings as a way to take notes and work through logic puzzles.

In Biochemistry, we used this kind of non-linear flowchart all the time to keep track of mechanistic models and signal transduction pathways.

In a previous post, I wrote about using probability binning to create categories out of numerical data. This simplifying approach works well on various kinds of distributions.

In this post, I wanted to show an example of how well it can work.

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.

1. 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. This required a bit of an adjustment to my usual mindset, where I question everything all the time.