Once upon a time poets were categorically masters of every science under the sun, including mathematics, natural history, anatomy, botany, celestial navigation, and the like, to paraphrase Artaud's letters from Mexico; poet that I am, I celebrated that notion by picking up some new skills as of late, of which programming has come most winsomely.
There is an organic sophistication to it, as it were, quite unlike anything else, although various metaphors and similes float about: “coding is like cooking,” “coding is like learning a language,” “coding is like magic but it’s real.”
Even setbacks can wield whimsical bathos about them for the coder who reaches the end of their existential patience only to discover one unassuming keystroke later that all their anguish was but cumulative trifling error caught in the reflection of a text-laden command line interface.
Python even has a dependency management tool called Poetry. How alluring is that?
Perhaps not as broadly as you’d imagine, at least among practitioners of poetry lowercase.
The few writers I know who do program are academics and journalists, not programmers. I’m hardly one myself yet. My software enlightenment came around the time I began learning to write in Darwin Information Typing Architecture (DITA) with Oxygen (as well as Git), which is a flavour of XML; one of many languages, along with MySQL, CSS, PHP and Bootstrap, used to manage CMS stuff, like WordPress.
Leveraging that, I made my first forays into what was most familiar to me first: XML, SQL, Git and web development. I enrolled in Harvard’s notoriously challenging CS50 course, thanks to a video that was trending on Youtube at the time, picked up a bit of C, and, as soon as possible, dove headfirst into finding my bearings with Python.
I have continued branching out in every direction, contrary to most advice you’ll happen across online as a noob.
The challenge with the two-language problem in programming today is that you can’t scale your code's performability as an autodidact without learning two things at once (although many professionals manage to do everything with Python; par contre, many also rely on Cython and Jython).
Fortunately, David Malan at Harvard is very encouraging, teaching the fundamentals of computer science before applying them to problems in various languages. So why follow the advice of people in the comments section who say “just focus on one language”? There are hundreds out there!
I’m familiarizing myself with data engineering now, through SQL, R, and Python; as well as the web development ecosystem of Java, React, Typescript, PostgreSQL, Django, Golang, Docker, Kubernetes, and AWS. As important as technical documentation is, I hope to tack my career towards full stack, then cast my anchor in software dev or data science somewhere over the horizon.
Lastly, I’m superlatively thrilled to be learning Julia, which is what this site is composed with, using the Franklin.jl library and VS Code.
I’m humbled and honoured to welcome you to this, my first deployment, with much thanks to the tutorial published by Ifihan Olusheye.