Data Science & Machine Learning Basics

Data Science is one of the highest-paying technical careers globally and increasingly important in Sri Lanka — from MAS analytics to LB Finance fraud detection to research at the University of Moratuwa. Our 14-week Data Science & Machine Learning Basics course gives Grade 10–13 students a strong, practical foundation in real data work.

Students learn the full data-science workflow: collecting and cleaning data, exploratory data analysis with pandas, data visualisation with matplotlib and seaborn, statistical thinking (means, medians, distributions, correlation, regression), and machine learning algorithms (linear and logistic regression, decision trees, random forests, k-means clustering) using scikit-learn. We cover overfitting, train/test splits, cross-validation and model evaluation properly — most beginner courses skip these.

Real Sri Lankan datasets: predicting GCE O/L pass rates from school-level statistics, analysing Colombo apartment price drivers, building a classifier for Sri Lankan weather forecasting from historical data, and a final capstone project where each student picks their own dataset (we provide a curated list of free Sri Lankan and global datasets) and produces a full data-science report.

This is our most analytical course — students should be comfortable with school maths (basic algebra, statistics, percentages) and have completed our Python Programming course or have equivalent Python experience. A laptop with 8 GB RAM. Highly recommended for students considering A/L Mathematics + Computer Science, engineering or business analytics careers.

Course Outline

• Weeks 1–2: pandas + data cleaning
• Weeks 3–4: Exploratory analysis + matplotlib
• Weeks 5–6: Statistical thinking + correlation
• Weeks 7–8: Linear + logistic regression
• Weeks 9–10: Decision trees + random forests
• Week 11: Clustering + unsupervised
• Week 12: Model evaluation done right
• Weeks 13–14: Capstone project + presentation