Career pathway
Computer Science, AI & Data Science
Investigate data, algorithms, machine learning, analytics, and intelligent systems using mathematical thinking. This is what a Grade 10 student actually learns on the CMP Technology pathway.
Pathway insight
Mathematics is the language of computing
Every algorithm, model, and dataset is built on statistics, probability, and logical reasoning. Students learn the maths that sits under the code.
From topic to intelligent system
Descriptive statistics, distributions, and probability become tools for data analysis, prediction, and evaluating machine-learning outputs, not abstract exercises.
Portfolio, not just tests
Each term ends with a portfolio task, a short data-driven investigation where the student uses the mathematics to draw and defend a conclusion from real data.
What students learn: Grade 10 Computer Science, Data Science & AI Course Planner
Four terms, each anchored to a real area of computing, data, and AI practice. Four sample topics from every term are shown below.
Term 1
Data Quality, Compute Metrics & Feature Distribution
Sample topics
- Model performance indicators
- CPU and GPU compute measures
- Memory and storage requirements
- Dataset size and feature-to-sample ratios
Term 2
Probability Networks, System Reliability & Algorithmic Drift
Sample topics
- Probability models in computing
- Software error events
- Runtime failures
- Cloud-service outages
Term 3
Binary Classification, Evaluation Matrices & Validation
Sample topics
- Automated classification
- Spam and malware detection
- Anomaly detection
- Responsible classification systems
Term 4
System Variation, Performance Forecasting & Computing Optimization
Sample topics
- Network latency
- Jitter and response variation
- System load
- Performance distributions