[1] professionally, i build custom software tooling and solve traditional-style ( as opposed to “deep learning” ) ML problems for the data science group at a finance company – but they use Python, and i rudely describe Python as “a gross way to live” in meetings, and vocally pine for “a compiled language with an actual type system”
[1a] – but that just pays my bills, and while the finance company is nice, well-intentioned folks, it is difficult to feel as if helping to make money with money is “important” for anyone other than the few folks who the money ultimately belongs to. avocationally, i poke around for civic-minded problems i can try to solve using public datasets – my firmest accomplishment to date has been to assemble a year’s worth of incarceration records from the local county sheriff’s department [ this is in the United States ], then clean and aggregate them into nice, concise reports that illustrate things like systemic racism in incarcerations and mismanagement in the local criminal justice system. i chose to use Julia for this!
[2] i hesitate to brand anything i work on as “important” – as importance assumes a vector of moral valuation and general “worlding”. but i choose to poke around through grimy public datasets and turn them into charts because i know that no one else will. the public sector in the U.S. is brutally starved of resources; the tech departments of local governments struggle to fulfill even the most basic functions, and almost never attract the most capable engineers ( or rather – those who are capable work just long enough to build a resume, and then leave as quickly as they can ). as such, i can be confident that no one else with any sort of technical aptitude is going to work on such a small, localized problem. things will sit and fester in obscurity, and local officials will shrug impotently every time someone asks what’s really going on: “we don’t have the data; it will take so long to build that report, if we even can” ( i have specific stories that starkly illustrate this self-serving incompetence ). in contrast, i’ve watched data science wield massive power within my professional work – when executed with vision and discipline. i do not have the effort or resources of an entire team, but i would like to marshal what power i can, in the service of civic causes that might otherwise get no help at all.
[3] i know that doesn’t seem much to do with Julia, nor does it represent an accomplishment that would dazzle an experienced engineer with its technical challenge or nuance. for what it’s worth, i chose Julia because it just has the feel of a good tool. i did a lot of graduate work in programming languages and compilers, and when i saw what Julia was, i immediate recognized it as something remarkable: a tool that strikes the razor balance between user experience and technical execution. ( i also rely heavily upon DataFrames.jl and Gadfly.jl, both of which have been a delight to use, in contrast to my experiences with pandas and matplotlib.pyplot ) i wanted to use it simply because it does all of the things that i wish Python did ( and that i think most Python users would also wish for, if they better understood the limitations of what they were working with ). they say that “a poor carpenter blames the tools” – and i can say, with confidence, that i have quietly suffered to build quite a few things in Python that i am genuinely proud of, and some of which may even have been technically impressive. but there is something demoralizing about having to hack onward with a bad tool. it can be done, for sure, but it is hard not to feel burdened or even humiliated by it. ( how many runtime checks of hasattr and isinstance must i really write?! ) as i wade through this obscure uphill battle of my avocational work, it lifts my spirits just a little to know that the tool in my hand is powerful and technically sound, that even my humble task is worth a tool built with care.
[*] - i mean no insult to any technically competent civil servant out there; i’m sure they must exist, but they also appear to be vanishingly rare. if you are that person, or know that person, then i apologize for any offense, and offer my utmost respect
