StanfordOnline: Statistical Learning with Python course Syllabus

Overview: This course provides a comprehensive introduction to statistical learning with Python, blending theoretical foundations with hands-on implementation. Designed by StanfordOnline, it spans approximately 8–12 weeks with a total time commitment of 60–80 hours. Learners will progress through core topics in machine learning with an emphasis on understanding model mechanics, evaluation, and practical application using Python. Each module combines conceptual depth with coding exercises to build a robust foundation for real-world data science tasks.

Module 1: Introduction to Statistical Learning

Estimated time: 10 hours

• Definition and scope of statistical learning
• Differences between statistical learning and traditional statistics
• Prediction vs. inference in modeling
• Real-world applications in data science and machine learning

Module 2: Linear Regression and Extensions

Estimated time: 15 hours

• Simple and multiple linear regression fundamentals
• Model interpretation and coefficient analysis
• Model diagnostics and residual analysis
• Polynomial regression and regularization techniques

Module 3: Classification Methods

Estimated time: 15 hours

• Logistic regression for binary classification
• Decision boundaries and classification rules
• Performance metrics: accuracy, precision, recall, and ROC curves
• Implementing classification models in Python

Module 4: Resampling and Model Evaluation

Estimated time: 12 hours

• Cross-validation techniques for model assessment
• Bootstrap methods for uncertainty estimation
• Understanding overfitting and underfitting
• Validation strategies for reliable model evaluation

Module 5: Tree-Based Methods and Ensemble Learning

Estimated time: 15 hours

• Decision trees: construction and interpretation
• Random forests and feature importance
• Boosting fundamentals: AdaBoost and gradient boosting
• Strengths and limitations of ensemble models

Module 6: Final Project

Estimated time: 15 hours

• Apply statistical learning methods to a real-world dataset
• Build and evaluate regression and classification models
• Interpret results with attention to model assumptions and trade-offs

Prerequisites

• Familiarity with basic statistics (e.g., mean, variance, correlation)
• Working knowledge of Python programming
• Basic understanding of linear algebra and calculus recommended

What You'll Be Able to Do After

• Understand core concepts of statistical learning and their data science applications
• Implement regression and classification models using Python
• Evaluate models using resampling and validation techniques
• Interpret machine learning models beyond black-box predictions
• Build a strong foundation for advanced machine learning and AI studies