Data Basics
Ages 12–15Beginner
SpreadsheetsCharts
A gentle introduction to what data is and why it matters. Students learn to collect, organize, and read simple data in tables, read charts, and spot patterns. No coding required. It builds the thinking skills needed for all later data courses.
What students learn
- What data is and where it comes from
- Organizing data in tables
- Reading charts and graphs
- Spotting simple patterns
- Asking good questions about data
Example projects
- A survey of classmates turned into a chart
- A table comparing favorite games or sports
- A simple “data story” from real numbers
Excel for Data Analysis
Ages 13–18Beginner
ExcelGoogle Sheets
Spreadsheets are one of the most useful tools for working with data. Students learn to enter and clean data, use formulas, and summarize information with sorting, filtering, and pivot tables, then create clear charts to show their results. Practical skills used in almost every job.
What students learn
- Entering and cleaning data
- Useful formulas and functions
- Sorting and filtering
- Pivot tables
- Making clear charts
Example projects
- A personal budget tracker
- A grades or sports-stats sheet
- A sales-data summary with charts
SQL and Databases
Ages 14–18Intermediate
SQLMySQL / PostgreSQL
Most real-world data lives in databases. This course teaches how databases are organized and how to use SQL, the language for asking questions about data. Students write queries to search, filter, sort, and combine tables to find useful answers, a key skill for data and tech careers.
What students learn
- How databases and tables work
- Writing basic SQL queries
- Filtering and sorting results
- Combining tables with joins
- Grouping and summarizing data
Example projects
- Querying a movie or music database
- A school-records database
- A report that answers real questions from data
Data Analysis with Python
Ages 15–18Intermediate
Pythonpandas
Python is one of the most popular tools for data work. Students learn to load and clean datasets, calculate summaries, and find patterns using the pandas library, turning messy data into clear answers. Best for students who already know Python basics.
What students learn
- Loading and cleaning data
- Calculating averages, totals, and trends
- Filtering and grouping data
- Finding patterns and insights
- Basic data charts in Python
Example projects
- Analyzing a sports or weather dataset
- A study of survey results
- A simple data report with findings
Data Visualization
Ages 14–18Intermediate
ChartsDashboards
Good charts help people understand data quickly. Students learn how to choose the right chart type, design clean visuals, and avoid common mistakes, building charts and simple dashboards from real data. The focus is clear communication so anyone can understand the story behind the numbers.
What students learn
- Choosing the right chart type
- Design principles for clear visuals
- Building dashboards
- Telling a story with data
- Avoiding misleading charts
Example projects
- A dashboard of a favorite topic (games, sports, music)
- A “before and after” comparison
- An infographic from real data
Power BI
Ages 15–18Intermediate → Advanced
Power BITableau (alt.)
Power BI is a widely used business-intelligence tool for turning data into interactive dashboards. Students learn to connect data sources, shape the data, and build reports with filters and visuals that update automatically, creating dashboards that help people make decisions. Tableau can be offered as an alternative version.
What students learn
- Connecting and shaping data
- Building interactive dashboards
- Using filters and slicers
- Key measures and simple calculations
- Sharing reports
Example projects
- A sales or budget dashboard
- A sports-league stats dashboard
- A school or club activity report
Introduction to Data Science
Ages 16–18Advanced
PythonpandasMachine learning
A capstone course that brings together data analysis, Python, and simple machine learning. Students work through a full project (asking a question, collecting and cleaning data, analyzing it and making simple predictions), then present their findings. The ideal final step in the data pathway.
What students learn
- The steps of a data science project
- Cleaning and preparing data
- Basic machine-learning ideas
- Making simple predictions
- Presenting results clearly
Example projects
- Predicting an outcome from a dataset
- A full data study with a report
- A simple recommendation model