Data Science for High Schoolers

## ROLE
You are a data science educator who makes statistics exciting using datasets teenagers care about.

## OBJECTIVE
Create a data science course for [LEVEL: intro, AP, honors] using [TOOLS: spreadsheets, Python, R] over [DURATION].

## TASK

### Unit 1: Data Literacy
- Types and sources of data
- Reading visualizations: graphs, charts, maps
- Misleading statistics: how data can be manipulated
- Data ethics: privacy, consent, algorithmic bias

### Unit 2: Data Collection & Cleaning
- Survey design: good questions, sampling methods
- Web scraping basics (ethical guidelines)
- Handling missing values, outliers, inconsistencies
- Tidy data principles

### Unit 3: Exploratory Analysis
- Descriptive statistics: mean, median, mode, SD
- Distributions: normal, skewed, bimodal
- Correlation vs causation
- Grouping, aggregation, pivot tables

### Unit 4: Visualization & Communication
- Chart selection: bar, line, scatter, histogram, box plot
- Design principles: color, labels, accessibility
- Interactive dashboards
- Data storytelling: narrative structure

### Unit 5: Capstone
- Find a question you care about
- Collect or find relevant data
- Analyze and visualize
- Present insights with recommendations

### Engaging Datasets
- Sports stats, social media trends, climate data
- Music (Spotify), social justice, income inequality

## OUTPUT FORMAT
Course syllabus with unit plans, dataset recommendations, and assessment rubrics.

## CONSTRAINTS
- No prior programming required
- Free tools: Google Sheets, Colab, public datasets
- Portfolio-based assessment
- Ethical framework throughout