Moving from Humanities to Data (and around the world)

My geographical and intellectual journeys aren’t directly related, but over the past few years, my lack of a stable location has certainly played into my sense of what’s possible, what’s measurable, and how best to understand the forces that shape the world. Long story short, I’ve been making the jump from math-averse humanities major towards a me that is comfortable with numbers, models, uncertainty, and testing hypotheses.

I’ve made a lot of different mental jumps in a lot of different places, though, and since my history education instilled in me a strong sense of narrative (something humans love to impose on the world, whether it belongs there or not), I feel driven to connect the two. In this post: how I apparently am choosing to explain some of my life choices.

tl;dr: I move around a lot, mostly between relatively affordable urban areas in different countries. Humans and cities can only be fully explained with data, which is why I’m going to be making a series of posts recording some interesting pieces of my now multi-year journey towards getting better at doing that. 

Global variables

Since graduating, I’ve lived long-term (6+ months) in several parts of the US, two cities in South Korea, one city in Thailand, and I’ll be moving on to Tbilisi in the Republic (not state) of Georgia pretty soon. Since I’ve sort of fallen into online work, it makes sense for me to live in places that offer more amenities and a lower cost of living than the US.

This has also given me a lot of opportunities to encounter a lot of different ways that things work, as well as radically different perspectives on the way they should work. I’ve generally come around to the idea, though, that these differences exist more on the surface than the photographs and travelogues would have you believe: humans tend to be humans. All other things being equal, the reasons for our behavior tend to change more than the behavior itself does.

The common currency I’ve fixed upon in trying to understand these forces is data. Obviously, you can’t plug variables into a regression and predict every aspect of a country’s culture–but you can make a pretty darn good guess about how things work in that country. As complex as our constructs are, there are global variables underlying them.

The data density of cities

That may be why my primary target in a new country is always the cities: nowhere can you find a higher density and diversity of available data. Walking around a new city for a few days, with open eyes and random feet, is basically skydiving for a certain type of data nerd. You won’t discover everything, and a lot of your impressions will be wrong (they’ll be wronger the less time you spend), but if you pay attention you’ll end up with a collection of means and standard deviations for everything from the price of a beer to the general quality of life experienced by residents.

My infovorous (what is the adjective form of “infovore”?) tendencies probably explain why, despite my rural upbringing, I’m an urbanite at heart. That’s an increasingly expensive thing to be in the states, where urban density and mixed-use zoning tend to meet stiff resistance, which, for better or for worse, has pushed me to venture out into other countries. Most of the cities I find attractive aren’t the ones with idyllic suburban neighborhoods or adorably preserved downtown boulevards, but the ones where you can find a new apartment building going up every corner, gradually being surrounded by the shops and restaurants its residents demand. As far as I’m concerned, aesthetics take a clear back seat to affordability and convenience.

A personal geography

This personal preference for efficiency over beauty probably explains a lot about what frustrated me with humanities (a tendency to emphasize the subjective and unquantifiable aspects of human experience) and what I find attractive about the prospect of engaging with data (the drive to measure what can be measured, to quantify the unquantifiable where possible, and a certain level of comfort with error). Neither extreme is preferable, of course: purely data-based decisions are likely to ignore things that are difficult to measure, while purely qualitative decisions are likely to be subject to a wide array of human psychological errors and biases.

I’m very happy to fall somewhere in the middle of the qual-quant spectrum, as that’s where a lot of truth (with varying confidence intervals) tends to lie, and especially since that’s where I’m likely to remain in terms of my abilities. I’m decidedly weaker on quantitative skills than I’d like, though, which is why, since graduating university in 2014, I’ve been on a journey to improve them. It’s been slower than I’d like, hindered by things like having to “earn money” and “live life,” but I know I’m not the only one trying to reconcile their idealistic teenage degree choices with the facts of a rapidly expanding reality, which is why I’m hoping to make this a series detailing my steps and missteps, the resources I’ve used, the progress I’ve made, and the gravities that have pulled me into various orbits.

This post hopefully takes care of a lot of the “why,” and in future ones I’ll mostly be focusing on the “how.”

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