Module 2: Data handling with Python libraries

Theme

Data handling with Python libraries

Essential Question

How do Python tools support reproducible data work?

Module Components

• Book prose: conceptual framing, domain scenario, methods, and failure modes

• Assignment: evidence-backed production of a specific artifact

• Slides: presentation sequence for seminar or lecture delivery

• Narration: spoken version of the slide flow

• Rubric: criteria for evaluating the module artifact

• Notebook: executable lab aligned with the module theme using synthetic API requests, validation outcomes, latency measurements, and test-case results

Module Artifact

tested Python AI component with interface contract, CI evidence, and deployment notes focused on data handling with python libraries: Load, validate, and transform a dataset.

Professional Setting

Students work as if advising an engineering team converting prototype AI code into a maintainable application component. Their work must be intelligible to software engineer, ML engineer, QA lead, product owner, and operations reviewer.

Use This Module in Order

1. Read the learning chapter.

2. Review the slide deck with the matching narration.

3. In Populi, open the private student-repository link for this course and enter modules/module-2.

4. Clone the repository once or open its Codespace/Colab copy; run lab.ipynb and complete exercise.ipynb there.

5. Self-check with the rubric, commit and push the work, then submit exactly what Populi requests.