Module 2 Overview

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

• Instructor notes: facilitation plan, discussion prompts, and grading cues

• 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.