Data Science Fundamentals — From Theory to Production Models
Master the mathematical, statistical, and algorithmic foundations that every production data scientist depends on. Covers probability, distributions, regression, classification, clustering, model evaluation, explainability, and end-to-end pipelines.
Course Lessons
Statistical Foundations Every Data Scientist Must Own
Probability Theory & Distributions for ML
Bayesian Thinking & Inference
Regression — Linear, Polynomial, Regularised
Classification Algorithms — From Logistic Regression to Gradient Boosting
Unsupervised Learning — Clustering & Dimensionality Reduction
Model Evaluation, Validation & Metrics That Matter
Bias-Variance Tradeoff, Overfitting & Regularisation
Feature Engineering & Selection for ML
Ensemble Methods — Bagging, Boosting & Stacking
Model Explainability — SHAP, LIME & Interpretable ML
End-to-End Data Science Pipeline — From Problem to Production