Detailed Syllabus & Weekly Breakdown

Module 1: Data Science Math & Python (Weeks 1–4)

• Week 1: Python for Data Science
• Week 2: Linear Algebra Basics
• Week 3: Probability & Statistics
• Week 4: Data Preprocessing

Module 2: Supervised Learning (Weeks 5–8)

• Week 5: Linear Regression
• Week 6: Logistic Regression
• Week 7: Decision Trees & Random Forests
• Week 8: Support Vector Machines

Module 3: Unsupervised Learning (Weeks 9–12)

• Week 9: K-Means Clustering
• Week 10: Hierarchical Clustering
• Week 11: Principal Component Analysis (PCA)
• Week 12: Anomaly Detection

Module 4: Evaluation & Deployment (Weeks 13–16)

• Week 13: Cross-Validation & Metrics
• Week 14: Hyperparameter Tuning
• Week 15: Model Deployment Basics
• Week 16: Capstone ML Project

Learning Outcomes

• Core ML algorithms
• Math foundations
• Model optimization
• Deployment techniques

Assessment Weightage

Assessment Type	Weightage
Strategic Simulations	20%
Leadership Portfolio	20%
Quizzes & Presentations	20%
Final CXO Pitch (Capstone)	40%

The "WhiteCollar" Executive Perks

• CXO Mentorship: Direct access to industry leaders
• Investor Meets: Opportunity to pitch innovation ideas