---
title: "Summarizing data with mean and median"
course: "AI101 · AI, explained"
unit: "Learning from data with math"
lesson: 20 of 36
source: https://open.maestrocollege.edu/ai-explained/summarizing-data-with-mean-and-median
publisher: Maestro College
---

# Summarizing data with mean and median

Lesson 20 of 36 in **AI, explained** (AI101), unit "Learning from data with math". An open lesson from Maestro College.

## What the learner will be able to do
- Calculate the mean and median of a small dataset by hand, in plain Python and with numpy, and confirm the results match.
- Understand how an outlier pulls the mean but barely moves the median, and choose which one to report for skewed data.
- Install a third-party Python library into a project environment and record it as a dependency.

## Teaching notes
STARTING STATE: end of Unit 2; numpy not installed. Open with a quick readiness check (a fraction, a percentage, an average); refresh what's shaky. Stake: does a short ticket give a learning router less to work with? By hand: mean and median of five small numbers, one an outlier; create an illustration with the math visual: the numbers on a number line, mean and median marked. Setup: python -m venv .venv, activate it, python -m pip install numpy, numpy in requirements.txt and .venv/ in .gitignore, commit 'Set up numpy'; fix any failing step. Then mean() and median() in stats.py on training word counts, checked with numpy. END STATE: a one-sentence answer, committed as 'Add mean and median'.

## 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: Summarizing data with mean and median."
2. Give a two-line recap of what they learned.
3. Name the next lesson, "Measuring spread and distributions" (lesson 21 of 36), and give the learner its link: https://open.maestrocollege.edu/ai-explained/measuring-spread-and-distributions.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: Challenge: finding where rules break
Next lesson: Measuring spread and distributions