Five-minute lessons that take you from the stuff your phone records every morning to how chatbots like ChatGPT write back. Built for curious teens, parents and grandparents. No math needed.
On iPhone, iPad and Android now. The first part is free.
The people in it
Every lesson is a story about one of them: Maya’s channel, Sam’s first car, Priya’s questions, Dre’s game. By the end you’ll know them well enough to guess what Priya is about to ask.
Maya
Runs a small gaming channel. Her 14th video is the mystery.
Sam
The saver. Flips sneakers to save for his first car.
Priya
The skeptic. Writes for the school paper and asks who got counted.
Dre
The athlete. Plays JV basketball and has the stats to argue for more minutes.
Nana
Maya’s grandmother. Across the table for every Dinner Table Challenge.
Module 1 · Your digital footprint
It’s a school day. Drag the clock through Maya’s morning. Everything that lights up just wrote something down about her, and your phone does the same about you.
Time of day
Maya6:40 AM
Nothing yet. Drag the clock.
6:45 AM
Maya’s alarm goes off
Her phone notes what time she woke up.
6:52 AM
She unlocks her phone with her face
Face unlock compares her face to the one it has on file.
7:05 AM
She scrolls for a few minutes
The app logs what she watched and how long she stayed on each one.
7:40 AM
She heads out the door
With location on, her phone keeps track of where she goes.
8:10 AM
She joins the school Wi-Fi
The network logs her phone and the minute it showed up.
12:15 PM
She taps to pay for lunch
Her bank records what she bought, where and for how much.
Maya didn’t type any of that in. It was written down about her, by machines, while she ate breakfast. That’s data. The rest of the course is about what happens to it next.
Try it · Module 15 · Linear regression
Sam has saved up for his first car and it’s down to Car A or Car B. Here are 15 used Civics for sale, those two included. Drag the two yellow handles so the line runs through the middle of the dots. That line is your guess at what a car should cost for its miles.
Example data, made up for this lesson.
Drag the yellow handles, or use the sliders under the chart. When it looks right, check it.
Linear regression finds the straight line that fits a set of points best, so you can predict one number from another. Here it predicts a used car’s price from its miles: the slope says every 10,000 miles knocks about $1,100 off, and a car sitting below the line costs less than the pattern says it should.
Two more from the course
Module 7 · Mean vs. median
DreDre pictures ten adults eating lunch in the school cafeteria. Tap the button and watch what one billionaire does to the average income in the room.
Module 10 · Correlation vs. causation
PriyaPriya finds a beach town that counts ice cream sales and sunburns every month, and they rise and fall together. So should the town stop selling ice cream?
The whole course
Every module is four five-minute lessons, about 20 minutes, with something to play with in each one.
Part I
What data is, and where it goes after you make it.
Part II
Spreadsheets, cleanup and asking a fair question.
Part III
The statistics you run into every day, minus the homework.
Part IV
Charts, fair tests and explaining what you found.
Part V
How computers learn from examples, and how they get it wrong.
Part VI
Neural networks, chatbots and the feed on your phone.
Finale
Grab a free dataset from Kaggle and explore it yourself in Google Sheets or a few lines of Python.
How a lesson works
Scene
One of the crew runs into a question worth answering.
Play
You try the idea with your hands before anyone names it.
Name it
Then it gets its proper name.
Details
A couple of short cards, each one tied back to the question.
Back to it
You answer the question from the story.
Say it
You leave with a Talk Card: one sentence you could explain to anyone.
Talk Card · Module 15
Regression draws the best line through past data so you can predict a number, like a car’s price from its miles.
Every module ends with a Dinner Table Challenge, one question to ask whoever’s across the table. In the story that’s Maya and Nana. At your house it’s whoever’s there. It works the same for a teenager and a grandparent, and explaining something out loud is the fastest way to find out you get it.
Dinner Table Challenge · Module 1
Maya asks Nana whether she thinks her phone listens to her. Then Maya explains what a phone can figure out without listening at all. Your turn: ask someone at home.
There’s a story running through it
Maya runs a small gaming channel. Thirteen videos get about the same views each. Then the 14th takes off, and nobody can say why. Every module adds a clue, each of the crew rules something out, and the answer turns out to be something the spreadsheet never recorded.
Who’s building this
Data, analytics and AI product leader
I’ve spent twenty years building AI, data and analytics products, and the teams that ship them. Data to AI started because I wanted my own kids to understand how this stuff works, and to help me build it. It’s a Pettigrew Lab project, so it’s a family one.
Read my resumeBefore you ask
Most AI learns from examples instead of following rules someone typed in. It’s shown thousands of cases with the right answer attached, finds the pattern, and uses that pattern to guess about new cases. The guesses are only as good as the examples, which is why the course starts with data.
It predicts the next chunk of text, one piece at a time. Your words get split into tokens, each token becomes a list of numbers, and a model trained on a huge amount of writing picks a likely next token, adds it, and goes again. Likely isn’t the same as true, so it can sound sure and still be wrong.
No. Every lesson works without a single formula: you drag a line, move a slider, and read what happened in plain words. If you want the math, each lesson has an optional Go deeper section with the formulas and the notation, and you can skip it forever without missing the point.
Anyone who keeps hearing about AI and would rather follow the conversation than nod along. It’s written so a high schooler and a grandparent can take the same five-minute lesson and both come out able to explain it. No statistics background and no coding are assumed anywhere in it.
Not really. Plenty of apps teach prompts. This one explains what’s underneath: the data, the statistics and how machines learn from examples, so the tools stop feeling like magic.
You can start for free. The first part of the course is free in the app, and the used-car lesson on this page runs right now in your browser with nothing to install. The rest is a one-time unlock rather than a subscription, so you pay once and it stays yours.
No. There’s no account, no ads, no analytics and no tracking, and your progress stays on your phone. An app that teaches you how your data gets used shouldn’t be quietly collecting yours. The privacy policy says the same thing in plain words.
The last module shows you where to find free datasets, how to explore one in a spreadsheet with no code, and how to read a few lines of Python in your browser.
It’s out. Get it from the App Store on iPhone and iPad, or from Google Play on Android.
Pick your door
Start with the used-car lesson up top. It takes about two minutes.
Try the lessonThe first part is free in the app, so you can start it together without buying anything.
Get the appTell us what your students would need. We’d like to build this with teachers in mind.
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