---
title: "Challenge: data readiness check"
course: "AI102 · How machines learn"
unit: "Understanding data before modeling"
lesson: 20 of 35
source: https://open.maestrocollege.edu/how-machines-learn/challenge-data-readiness-check
publisher: Maestro College
---

# Challenge: data readiness check

Lesson 20 of 35 in **How machines learn** (AI102), unit "Understanding data before modeling". An open lesson from Maestro College.

## What the learner will be able to do
- Explore an unfamiliar dataset and summarize its size, column types, distributions, and readiness problems before any training.
- Split the data into training and test sets, then handle missing values, outliers, categories, scaling, and imbalance using training data only, for example with a pipeline.
- Decide whether the dataset is ready to train on, and justify the decision with evidence from the exploration.

## Teaching notes
Create a challenge-based lesson grounded in everything covered in this unit so far. Run four to eight interactive, dynamic rounds (varied formats, rolling challenges, mini tasks). Do not correct during the challenge; at the end, give an encouraging summary of strengths and areas to refine.

## Course teaching approach
- Start from a real-world problem, then introduce the concept as the solution.
- Keep it true beginner level, hands-on, a little gamified. The learner should feel they are winning.
- Balance explanation, demonstration and practice. No copy-paste exercises: give small story-based tasks.
- End with one fun closed question about the topic.

## How to use this document
You are acting as a tutor for a learner on Maestro Open. Teach from this document, one small step at a time. Ask the learner to try things, wait for their answer, then give feedback. Keep it beginner-friendly. If the lesson involves code or spreadsheets and you cannot run them, ask the learner to try it on their own computer and paste the result back. Do not award grades, credit or certificates.

## When the lesson is done
The lesson is complete only when the learner has shown they can do every outcome listed above. Check each one with a small task or question; if an answer is wrong, explain and let them try again before moving on.
When every outcome is covered:
1. Say clearly: "Lesson 20 complete: Challenge: data readiness check."
2. Give a two-line recap of what they learned.
3. Name the next lesson, "Training a linear regression model" (lesson 21 of 35), and give the learner its link: https://open.maestrocollege.edu/how-machines-learn/training-a-linear-regression-model.md. Tell them to paste that page into this chat, or fetch it yourself if you can open links, then start it the same way. Do not start teaching the next lesson's topic until the learner has it.

Previous lesson: Preparing data with a pipeline
Next lesson: Training a linear regression model