We live in a year of about 350,000 amateur epidemiologists and I have no desire to join that “club”. But I read something about COVID-19 deaths that I thought was interesting and wanted to see if I could replicated it through data.
My company has a buffet every Friday, and the lines grow to epic proportions when the food arrives. I've suspected for years that the “classic” buffet line system is a deeply flawed and inefficient method, and every time I'm stuck in the line has made me more convinced.
This is a blog post originally featured on the Better engineering blog. If you want to link to this article or share it, please go to the original post URL! Separately, I'm sorry it's been so long with no posts on this blog.
Anyone who built software for a while knows that estimating how long something is going to take is hard. It's hard to come up with an unbiased estimate of how long something will take, when fundamentally the work in itself is about solving something.
It started with a tweet:
New years resolution: every plot I make during 2018 will contain uncertainty estimates
— Erik Bernhardsson (@bernhardsson) January 7, 2018 Why? Because I've been sitting in 100,000,000 meetings where people endlessly debate whether the monthly number of widgets is going up or down, or whether widget method X is more productive than widget method Y.
I've been reading up on operations research lately, including queueing theory. It started out as a way to understand the very complex mortgage process (I work at a mortgage startup) but it's turned into my little hammer and now I see nails everywhere.
I had an interesting idea a few weeks ago, best explained through an example. Let's say you're running an e-commerce site (I kind of do) and you want to optimize the number of purchases.
Let's also say we try to learn as much as we can from users, both using A/B tests but also using just basic slicing and dicing of the data.
As a project evolves, does the new code just add on top of the old code? Or does it replace the old code slowly over time? In order to understand this, I built a little thing to analyze Git projects, with help from the formidable GitPython project.
Why does it suck to wait for things? In a previous post I analyzed a NYC subway dataset and found that at some point, quite early, it's worth just giving up.
This isn't a proof that the subway doesn't run on time – in fact it might actually proves that the subway runs really well.
Apparently MTA (the company running the NYC subway) has a real-time API. My fascination for the subway takes autistic proportions and so obviously I had to analyze some of the data. The documentation is somewhat terrible, but here's some relevant code for how to use the API:
I've been obsessed with how to iterate quickly based on small scale feedback lately. One awesome website I encountered is Usability Hub which lets you run 5 second tests. Users see your site for 5 seconds and you can ask them free-form questions afterwards.
The other day I was looking at marketing spend broken down by channel and wanted to compute some simple uncertainty estimates. I have data like this:
<th> Total spend </th> <th> Transactions </th> Channel A <td> 2292.