Students engage in hands-on practice with techniques such as data mining, management, and analysis using various technologies. They also explore visualization through dashboards, presentations, knowledge management, and ethical considerations. The curriculum covers descriptive statistics, advanced use of data analysis tools, working with AI tools, and more.
By the program's conclusion, students will possess the skills to:
- Extract meaningful insights from data
- Effectively communicate data-driven findings and recommendations to business clients
The teaching materials are designed for Generation Z and Alpha, incorporating short videos, exercises, and solutions for self-learning.
Key Skills Developed
The learning focus encompasses critical thinking, independent and entrepreneurial thinking, self-learning, creativity, collaborative learning, planning, and process thinking.
The matriculation exam is computerized and includes application and analysis questions with open materials. The final project involves creating an analytical report using a dashboard to visualize data and recommendations.
Weekly Instruction Hours
Students dedicate 8-12 hours per week to data analysis instruction over three years of high school.
Assessment Methods
Assessment includes a data-oriented project with social impact, a computerized high school graduation exam with open internet sources and AI tools, and a Capstone Project utilizing BI tools to perform data analysis, extract insights, and present findings to decision-makers, all while incorporating artificial intelligence tools.
Teacher Training
Our teachers undergo a customized training program developed in collaboration with academic institutions. This program equips our data analysis instructors with key technical competencies and pedagogical techniques essential for delivering engaging, hands-on data literacy education to high school students.
Industry Mentorship for Real-World Connections
Data professionals from the industry are integrated into the classroom, serving as mentors. They facilitate connections between students and the real-world data landscape, providing technological expertise to complement teachers. .
