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Data and programming

Build reproducible data code with input contracts, small functions and boundary tests. Includes a runnable Python example.

4 lessons + an integrated projectStart the first lesson

Four lessons and an independent integrated task. Allow roughly 15–25 minutes per lesson, plus time for projects and delayed retries.

Review the existing foundation

Course contents

  1. Specify input types

    Specify normal, blank, zero, invalid and missing-column cases; run the downloadable example.

  2. Use small checkable functions

    Run summarize; record count/missing/mean and verify input is unchanged.

  3. Test boundaries and AI code

    Run at least five assertions; deliberately break zero handling, observe failure, then restore.

  4. Make outputs reproducible

    Download into a fresh folder, run tests and summary, record Python version and output.

  5. Integrated task and acceptance

    CSV: group A contains 0,6,blank; B contains 3,9. Deliver code, tests, raw CSV and output; test quoted group names and invalid values.

No login required. Exercises include explanations; open work uses self-check criteria without automatic grading or certification.

Written and maintained by Fineuralab. Updated 2026-10-15. Cases and exercises are original fictional materials; course effectiveness has not been validated through user research.

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Download and run

data-summary.py · sample.csv · README

python3 data-summary.py --test
python3 data-summary.py sample.csv

Requires local Python 3.10+, no external dependencies. Python does not execute in the browser.

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Keep your own answer before consulting an AI candidate. Check sources, calculations, inference and conditions; explain adoption or rejection. Then solve a new-context task independently and apply the checks to this lesson.

Practice the verification workflow and keep a record

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