skill~480 mindifficulty

Python for Data

Nearly every data pipeline glue task, extraction script, and transformation in data engineering is written in Python.

1

Learn it

Build on your existing Python by focusing on the data stack: pandas DataFrames, NumPy vectorization, and file I/O across formats. Practice reading messy source files and producing clean, typed outputs. Treat scripts as code you will schedule and rerun, not one-off notebooks.

By the end you’ll be able to

  • Load, clean, and reshape tabular data with pandas
  • Read/write CSV, JSON, and Parquet files

Free resources

2

Practice

Take a raw public CSV, clean nulls and types, derive 3 new columns, and write the result to Parquet.

3

Build it — your output

A parameterized Python script that ingests a raw file and emits a cleaned, typed Parquet dataset.

4

Prove it — check yourself

You’re ready to move on when you can honestly say yes to each:

  • Can you explain when to use vectorized pandas operations instead of Python loops?
  • Can you read a file, enforce dtypes, and write Parquet without looking it up?
  • Can you structure a script so it can be rerun idempotently?

Do it with AI

Use a free AI tool as your tutor and pair-programmer. Copy a prompt below into Claude, ChatGPT, or Gemini (for building, try Cursor or Copilot).

The habit: Try it yourself first — a little struggle is where the learning actually happens. Then ask for a hint before the answer — never copy blindly.

  • Understand itClaude / ChatGPT / Gemini

    I'm a beginner learning "Python for Data". Explain it in simple terms with a real-world analogy, then ask me ONE question to check my understanding — don't reveal the answer.

  • PracticeClaude / ChatGPT / Gemini

    Give me 3 short practice problems on "Python for Data" (my goal: load, clean, and reshape tabular data with pandas), from easy to hard. Don't include the solutions yet — I'll try them first, then ask you to check.

  • Get unstuckClaude / ChatGPT / Gemini

    I'm stuck on this exercise about "Python for Data": [paste your attempt here]. Give me ONE small hint to move forward — do NOT give me the full solution.

  • Build & reviewCursor / Copilot

    Here is my work for a "Python for Data" task: [paste your code or answer]. Review it: point out what's wrong and WHY, and suggest improvements — but let me make the fixes myself.

  • Prove itClaude / ChatGPT / Gemini

    Quiz me on "Python for Data". Ask me 5 questions one at a time, wait for my answer each time, then tell me if I'm right and briefly why.

My notes