Course overview
Duration: 8 weeks
Total hours: 24 hours (3 hours per week)
Level: Beginner to intermediate
Target group: Grades 7 to 10
Course content: Download the course flyer (PDF)
Students explore data analysis using Python, Jupyter Notebook, Microsoft Excel, Microsoft Power BI, and SQL with relational databases.
Course structure (8 weeks, 24 hours)
Week 1: Introduction to data and Excel basics
Topics
What is data? (structured vs unstructured)
Types of data (numeric, categorical, time-based)
Introduction to Microsoft Excel
Sorting, filtering, basic formulas (SUM, AVERAGE)
Activities
Analyze a class survey dataset
Create simple charts (bar, pie, line)
Mini project: Build a student performance tracker in Excel.
Week 2: Data collection and data cleaning
Topics
Data collection methods (survey, forms, observation)
Common data errors (missing, duplicates, incorrect format)
Data cleansing techniques
Introduction to data sharing ethics
Activities
Conduct a school data collection challenge
Clean a messy dataset in Excel
Mini project: Create a clean dataset report with before/after comparison.
Week 3: Introduction to Python and Jupyter
Topics
Basics of Python
Running code in Jupyter Notebook
Variables, lists, basic calculations
Reading simple datasets
Activities
Perform data calculations in Python
Generate summary statistics
Mini project: Build a simple data analyzer script.
Week 4: Databases and SQL fundamentals
Topics
What is a database?
Tables, rows, columns
SQL basics (SELECT, WHERE, COUNT, GROUP BY)
Introduction to OLTP vs OLAP systems
Activities
Query a sample school database
Compare transaction systems vs analytics systems
Mini project: Create SQL queries to answer business questions.
Week 5: Data pipelines and data flow
Topics
What is a data pipeline?
Data collection, storage, processing, analysis, reporting
Data integration basics
Data sharing practices
Activity: Draw a data pipeline diagram for a YouTube channel or school system.
Mini project: Design a data pipeline plan for a small business.
Week 6: Data visualization and dashboarding
Topics
Principles of good data visualization
Introduction to Microsoft Power BI
Creating charts and dashboards
KPIs (Key Performance Indicators)
Activities
Build an interactive dashboard
Compare good vs misleading graphs
Mini project: Create a sales or school performance dashboard.
Week 7: Data-driven insights and decision making
Topics
Turning data into insights
Identifying trends and patterns
Making data-driven decisions
Communicating findings effectively
Activities
Analyze a case study dataset
Present insights in small groups
Mini project: Create a data insight presentation.
Week 8: Final project and presentation
Capstone project: Data analysis challenge. Students choose one scenario:
School attendance analysis
Sports performance analysis
Small business sales analysis
Social media engagement analysis
Requirements
Data collection or provided dataset
Data cleaning
Basic SQL or Python analysis
Dashboard (Excel or Power BI)
Final report with insights and recommendations
Presentation (5 to 7 minutes) explaining
The problem
Data used
Analysis process
Key insights
Suggested decisions
Tools and technologies
Python
Jupyter Notebook
Microsoft Excel
Microsoft Power BI
SQL (MySQL / SQLite recommended)