Introduction to Data Science

I taught a part of the Introduction to Data Science course to MS-level students.

During this class, I introduced supervised classification through decision trees, random forests, and boosting methods, using practical notebooks on weather and breast cancer datasets.

The course focused on model training, hyperparameter tuning, and evaluation with metrics such as accuracy, AUC, ROC curves, confusion matrices, and feature importance analysis.

Overall, the sessions helped students understand the difference between single models and ensemble methods, while developing a practical data science workflow from data preparation to interpretation.