# Module 2 Overview

## Theme

Data preparation and feature pipelines

## Essential Question

How do preprocessing choices shape model behavior?

## 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 tabular observations with features, labels, train/test split, baseline score, and error slices

## Module Artifact

predictive modeling report with baseline comparison, validation evidence, and model card focused on data preparation and feature pipelines: Build a reproducible train/validation preprocessing pipeline.

## Professional Setting

Students work as if advising an analytics team choosing a predictive model for an operational decision. Their work must be intelligible to analytics lead, domain owner, operations manager, and model risk reviewer.