Data Preparation

Step 2 of the machine learning workflow

NoteData Preparation · at a glance

Workflow step	Step 2 of 5 · Data Prep
What you’ll learn	How to clean, transform, and structure data so it’s ready to model.
Standards assessed	DATA
Key terms	cleaning · transformation · encoding · missing values
Materials	readings · slides · homework · lab
Self-quiz	standards & practice →

Data preparation (one-sentence definition goes here — bold the term, define it plainly.) (Then 1–2 more sentences previewing what this chapter covers. This lead should stand alone as a summary.)

Overview

(1–3 short paragraphs: what this is, why it matters, and where it sits in the five-step workflow. Flesh out later.)

Key ideas

NoteKey idea

(State the single most important takeaway of this chapter.)

(List or briefly explain the 2–4 core concepts. Split into ### subsections as you flesh out.)

Readings

Before class, read the assigned material.

• Required: add reading + link
• Optional / going deeper: add reading + link

Slides

In class we’ll work through these decks:

• add slide deck links

Homework

• Assignment: link
• What it assesses: short description

Lab

TipTry it

(One-line pointer to the hands-on lab.)

• Lab: link
• Objectives: add objectives

Self-quiz

Use the standards below as a checklist — you should be able to do each — then try the practice questions to test yourself.

Standards

(Add the standards for this unit, each framed as “you should be able to…”.)

Practice questions

Click a question to reveal the answer.

NoteQ1. (Add a practice question.)

(Answer — students click to reveal.)

NoteQ2. (Add a practice question.)

(Answer.)

Key terms

Term

(definition)

Term

(definition)