Before we dive in, answer six quick questions about what you already know. Not to judge — to calibrate. You've written a bit of Python before. Maybe copy-pasted things and tweaked them. That's a great place to start.
What exactly will this track add on top of what I already know?
You already recognize a function. You've probably used a loop. The gaps are the one-line equivalents, the edge cases that crash, and the data structures you reach for and second-guess. This track fills those — moving you from "I can read Python" to "I can design it."
What does "design it" actually look like day to day?
You see a problem — extract values from 300 raw records — and instead of searching for a library, you think: split, strip, convert, store. Four operations you already know. Designing means composing them. That's the skill every lesson here rehearses. Answer honestly — you'll see the same six prompts on Day 30.
Python for Explorers assumes you've seen Python before — a tutorial, a class, some tinkering. You know what a function looks like. You've probably used a loop. But there are gaps: edge cases that crash your code, patterns you haven't internalized, data structures you reach for and then second-guess.
This track fills those gaps through a single coherent skill: taking raw data and turning it into structured, analyzed output. Every lesson builds a reusable function. By Day 28 those functions compose into a working analysis pipeline you can adapt to any dataset.
The six prompts below are your starting snapshot. Answer honestly — you'll see the same prompts again at the end of the track to measure how far you've come.
Before we dive in, answer six quick questions about what you already know. Not to judge — to calibrate. You've written a bit of Python before. Maybe copy-pasted things and tweaked them. That's a great place to start.
What exactly will this track add on top of what I already know?
You already recognize a function. You've probably used a loop. The gaps are the one-line equivalents, the edge cases that crash, and the data structures you reach for and second-guess. This track fills those — moving you from "I can read Python" to "I can design it."
What does "design it" actually look like day to day?
You see a problem — extract values from 300 raw records — and instead of searching for a library, you think: split, strip, convert, store. Four operations you already know. Designing means composing them. That's the skill every lesson here rehearses. Answer honestly — you'll see the same six prompts on Day 30.
Python for Explorers assumes you've seen Python before — a tutorial, a class, some tinkering. You know what a function looks like. You've probably used a loop. But there are gaps: edge cases that crash your code, patterns you haven't internalized, data structures you reach for and then second-guess.
This track fills those gaps through a single coherent skill: taking raw data and turning it into structured, analyzed output. Every lesson builds a reusable function. By Day 28 those functions compose into a working analysis pipeline you can adapt to any dataset.
The six prompts below are your starting snapshot. Answer honestly — you'll see the same prompts again at the end of the track to measure how far you've come.
