Thirty days. Twenty functions. One coherent analysis pipeline. Before you go, answer the same six questions from Day 1. Not a test — a mirror.
A few of these I know will be different. The comprehension one especially — on Day 1 I could barely read one, now I write them without thinking.
What surprised you most about the journey?
How much the standard library covers. I thought I'd need pandas or numpy. But comprehensions, sorted, sets, dict — that's most of what real data work needs.
The standard library takes you further than most people realize. Answer honestly — the comparison between your Day 1 and Day 30 scores is the most honest measure of what this track actually changed.
Over thirty days, you went from reading loops to writing pipelines.
Week 1 replaced manual loops with comprehensions — list, dict, and set — the one-line equivalents that express transformation directly.
Week 2 scaled that to ranking and safety — sorted with lambda, filter with predicates, and try/except for the numeric parsing that can blow up.
Week 3 added the collection algebra — set intersection and difference, dict merging, zip, and inversion — the patterns that make cross-referencing feel effortless.
Week 4 composed all of it into real analysis — grouping, aggregating, ranking within groups, normalizing records, and a capstone analyze_data function that ties every pattern together.
Next track: python-for-makers extends this with regex for pattern matching, generators for memory-efficient pipelines, and argparse for CLIs.
The six prompts below are the same ones you saw on Day 1. Your answers are a before-and-after snapshot of what this track actually changed.
Thirty days. Twenty functions. One coherent analysis pipeline. Before you go, answer the same six questions from Day 1. Not a test — a mirror.
A few of these I know will be different. The comprehension one especially — on Day 1 I could barely read one, now I write them without thinking.
What surprised you most about the journey?
How much the standard library covers. I thought I'd need pandas or numpy. But comprehensions, sorted, sets, dict — that's most of what real data work needs.
The standard library takes you further than most people realize. Answer honestly — the comparison between your Day 1 and Day 30 scores is the most honest measure of what this track actually changed.
Over thirty days, you went from reading loops to writing pipelines.
Week 1 replaced manual loops with comprehensions — list, dict, and set — the one-line equivalents that express transformation directly.
Week 2 scaled that to ranking and safety — sorted with lambda, filter with predicates, and try/except for the numeric parsing that can blow up.
Week 3 added the collection algebra — set intersection and difference, dict merging, zip, and inversion — the patterns that make cross-referencing feel effortless.
Week 4 composed all of it into real analysis — grouping, aggregating, ranking within groups, normalizing records, and a capstone analyze_data function that ties every pattern together.
Next track: python-for-makers extends this with regex for pattern matching, generators for memory-efficient pipelines, and argparse for CLIs.
The six prompts below are the same ones you saw on Day 1. Your answers are a before-and-after snapshot of what this track actually changed.
