Detailed Syllabus & Technical Roadmap

Module 1: Foundations of Data Science in Health (Weeks 1–3)

• Week 1: The AI-Life Science Intersection: Evolution from "Big Data" to "Agentic AI." Understanding the unique challenges of healthcare data (Small N, High Dimension).
• Week 2: Mathematics of Intelligence: Introduction to Linear Algebra and Statistics for ML; Understanding loss functions and optimization in a clinical context.
• Week 3: Python for AI Development: Scikit-learn and SciPy foundations; Automating data pre-processing for messy Electronic Health Records (EHR).
• Module Outcome: Prepare and clean healthcare datasets for model training while maintaining data privacy.

Module 2: Machine Learning for Diagnostics & Safety (Weeks 4–6)

• Week 4: Supervised Learning & Clinical Predictions: Building models for disease diagnosis (Classification) and patient length-of-stay (Regression).
• Week 5: Unsupervised Learning & Patient Stratification: Clustering algorithms for identifying "Sub-phenotypes" in rare diseases; Dimensionality reduction using PCA.
• Week 6: AI in Pharmacovigilance: Using Natural Language Processing (NLP) for automated signal detection from social media and clinical narratives.
• Module Outcome: Build and evaluate a predictive model using real-world healthcare datasets.

Module 3: Deep Learning & Generative AI in Pharma (Weeks 7–9)

• Week 7: Neural Networks & Medical Imaging: Convolutional Neural Networks (CNNs) for X-ray, MRI, and Histopathology analysis.
• Week 8: Generative AI & Protein Folding: How LLMs and Diffusion models are accelerating "De Novo" drug design and protein structure prediction (AlphaFold concepts).
• Week 9: Transformers in Medical Writing: Using Generative AI to assist in drafting Clinical Study Reports (CSRs) and Patient Narrative summaries.
• Module Outcome: Understand the deployment of Deep Learning models in drug discovery and medical imaging.

Module 4: AI Governance, Ethics & Deployment (Weeks 10–12)

• Week 10: AI Regulatory Frameworks: Navigating the FDA’s "Software as a Medical Device" (SaMD) guidelines and the EU AI Act’s "High-Risk" classifications.
• Week 11: Ethics & Bias in Health AI: Detecting and mitigating algorithmic bias in racial and gender-based health data; The "Black Box" problem and Explainable AI (XAI).
• Week 12: Capstone Project: Building and pitching an AI-based solution (e.g., an AI agent for Materiovigilance) to a mock Board of Directors.
• Module Outcome: Architect a compliant, ethical, and scalable AI strategy for a Life Sciences organization.

Learning Outcomes

• Technical Proficiency: Develop and deploy Machine Learning models using Python-based AI frameworks.
• Strategic Oversight: Evaluate AI vendors and lead AI-transformation projects in Pharma and MedTech.
• Regulatory Mastery: Ensure AI solutions meet global healthcare compliance standards (GxP/HIPAA/EU AI Act).
• Clinical Innovation: Apply Generative AI to accelerate drug development and automate safety reporting.

Assessment Weightage

Assessment Type	Weightage
Weekly AI Lab Challenges	30%
AI Ethics & Regulatory Paper	20%
Final Capstone: AI Product Launch	50%

The "WhiteCollar" AI Edge

In 2026, the question is no longer if AI will change Life Sciences, but how fast. This course ensures you are not just a user of AI tools, but a Creator of AI Strategy. By focusing on "Agentic AI" and regulatory compliance, we prepare you for high-level roles in Digital Health, Clinical Innovation, and AI-Ops

All above courses Include: Training Certification + Internship