Day 1 · the through-line
Humanities & Coding
Why a humanist learns to code
What do you do with knowledge that can't be measured?
That question is the humanities. Love and grief, justice and beauty, the meaning of a poem or a protest — the things that matter most tend to be exactly the things that resist a number.
Eight weeks turning human language into numbers. Let's be honest on day one about what that does and doesn't get us.
The human mess
The humanities study the mess of the human condition — and they don't tidy it up. They explore it.
- Contradiction — we hold conflicting values at once; we can love and resent the same person
- Ambiguity — a text means more than one thing. Is Hamlet mad, or pretending?
- Subjectivity — beauty and justice look different from different lives
The human mess
Many fields look for a principle — one law that explains many cases at once.
The humanities often do something else: they map the contours of the complexity itself. What one method sets aside as noise is often exactly what we came to study.
Keats called the needed temperament negative capability — being
"in uncertainties, mysteries, doubts, without any irritable reaching
after fact and reason."
This whole course lives inside that phrase.
So why learn to code?
The productive tension you'll feel all term:
Code wants to categorize, measure, and systematize. Culture is ambiguous, contextual, and messy.
A real comment from the file you'll analyze:
"If having the 10 commandments on the wall actually worked, we would need to post it in every room in the white house."
Is that positive, negative, or neutral? Guess — we'll see what the machine says.
So why learn to code?
What gets lost when a heated public argument is flattened into one average number? Who decides which categories matter when we collect data?
These aren't bugs in the software — they're humanities questions, and a humanist is exactly the person equipped to ask them.
We learn to code to bring humanities questions to computational methods: to read more voices than one person could ever read by hand, while staying honest about what the counting flattens.
More voices — and no false tidy answer. That's the course.
What it looks like in the wild
Before the cautions, the payoff — real projects that turn a pile of culture into a pattern you can suddenly see. Two that count words much as you soon will:
- "Trucks and Beer" — what country songs actually sing about
- The Pudding, "The Largest Vocabulary in Hip-Hop" — rappers ranked by how many different words they use (right: the Wu-Tang Clan, compared)
A few more worth a look
- Every Noise at Once — a navigable map of 6,000+ music genres, drawn from audio analysis (no longer updated, still explorable)
- Open Syllabus — millions of collected syllabi, showing which texts get assigned most often
Notice the move every one of them makes: a question about people comes first, then the counting. The tool serves the question.
Use the programs, or shape them
The underlying capability of the computer era is actually programming —
which almost none of us know how to do. We simply use the programs that
have been made for us, and enter our text in the appropriate box…
This means [we] have access to the capabilities given to [us] by others,
but not the power to determine the value-creating capabilities
of these technologies for [ourselves].
— Douglas Rushkoff, Program or Be Programmed (qtd. in Nick Montfort, Exploratory Programming for the Arts and Humanities, 327–28)
Use the programs, or shape them
Nick Montfort names the upside just as plainly. Learning to code in the humanities lets us think in new ways, understand culture and media systems more deeply, and maybe help improve society.
You don't have to leave here a programmer. But knowing what's inside the box — even a little — is the difference between using the tools that sort our culture and being able to question them.
The score is never the meaning
Look at this diagram. We'll come back to it every week.
It begins much further from "the truth" than you'd guess. Let's follow one woman down it.
The score is never the meaning
Her part.
- Noumena — the thing as it actually is (Kant's word, borrowed loosely). A woman in Texas reads that her child's classroom will display the Ten Commandments, and feels something complete and entirely her own.
- Phenomena — she finds words for some of it and posts them under a news video: public, partial, written for strangers.
We will never have that feeling. We have what she chose to type.
The score is never the meaning
Our part begins.
- Raw data — we scrape it: a free browser tool copies comments off the page. Her name and the date are lost; a line break can split her comment in two.
- Structured data — we clean it: fix and standardize what was copied, put it in rows, decide what counts as a "word."
"If having the 10 commandments on the wall actually worked…"
→ ['if', 'having', 'the', '10', 'commandments', …]
The score is never the meaning
- Analyzed data — we model it: apply a method that scores, sorts, or groups. A sentiment tool looks up each word in a list someone wrote — "great" is +3.1, for instance — and adds them up.
Meanwhile, "Move out of Texas if you dont want the 10 commandments shown. In God we Trust!" scores +0.67 — "positive," because trust and God are on the list as good words.
And the sarcastic comment? None of its words are on the list.
Its score: 0.0 — "neutral."
The score is never the meaning
Then we make something of the scores.
- Insight — beside the other scores, hers says something. Maybe: "the crowd is split."
- Knowledge — a claim we'd defend, once we've checked it against what she actually wrote.
- Wisdom — maybe, by the end: knowing how to speak about this law in public without flattening her.
Two steps were hers. Every step after that is ours.
Every arrow is a choice
Every arrow we draw on that diagram is a human decision — not a neutral pipe, but a choice, with a person and a value behind it.
It's tempting to call each step contamination, as if a clean signal up top got dirtied on the way down. But look again: there is no clean signal up top. Even her own words were already an interpretation.
Every arrow is a choice
One line of code, three honest answers
comments_text.count("commandments") # 13
comments_text.lower().count("commandments") # 25
comments_text.lower().count("commandment") # 27
"The 10 commandments are the foundation of America" — all three count it
"Doesn't matter if they post the Ten Commandments or not." — the first misses it: capital C
"…broken every Commandment except…" — only the third finds it: singular
Same 93 comments. Each choice is defensible, and each one gives a different number.
Every arrow is a choice
Choices aren't what sneak into the data — choices are what the data is made of.
That's not cause for despair, and not a reason to distrust everything. It's the reason for the one habit this course asks of you constantly: make your choices visible.
Every time you delete a word, set a threshold, or name a topic, you leave a #comment saying what you did and why.
The honest move isn't pretending you found the truth. It's showing where you stood.
What this course is really grading
This is why we don't grade you on clean code.
Insight, knowledge, wisdom aren't outputs you can compute — they're what's left after you've been honest about every arrow above them.
By your final reflection the question won't be "did you get the right answer?" It will be:
"Can you explain the choices you made along the way?"
What this course is really grading
One more thing about that bottom-right corner.
The climb from knowledge to wisdom can fail — and the failure isn't ignorance, which more information fixes. It's stubborner: being full of knowledge that never changes you. The Greeks had a word for it, and in a few weeks you'll learn it.
Tools can help carry you across every other arrow. Not this one.
