Data Science Foundations

What you'll achieve

• Execute the complete data science workflow from data ingestion to model evaluation
• Engineer and scale features suitable for machine learning algorithms
• Train baseline predictive models using scikit-learn
• Evaluate model performance objectively using standard validation metrics

Curriculum

1. 1Data science lifecycle overview & Jupyter notebook workflows
2. 2Data preprocessing: Encoding categorical variables & feature scaling
3. 3Splitting datasets: Train, validation & test sets
4. 4Supervised learning basics: Linear Regression & Logistic Regression
5. 5Evaluation metrics: Accuracy, Precision, Recall, F1-Score & ROC-AUC
6. 6Cross-validation techniques & preventing overfitting
7. 7Unsupervised learning introduction: K-Means clustering
8. 8Project: End-to-end predictive data science project with full evaluation