The "Introduction to Agentic AI for Data Scientists" course provides a comprehensive foundation in autonomous AI systems that can reason, plan, and act independently to achieve complex goals. Tailored for beginner to experienced data scientists ready to evolve their practice, the course covers the fundamental shift from traditional predictive modeling to designing intelligent agent workflows, while also delving into cutting-edge topics such as the four agentic design patterns, multi-agent collaboration, and production deployment strategies. Through interactive case studies, real-world industry applications, and hands-on workflow analysis, learners gain practical insights that can be immediately applied to transform their data science operations.
- Build a clear understanding of agentic AI and how it differs from traditional and generative AI approaches.
- Apply Andrew Ng's four agentic design patterns to identify the right architecture for different automation scenarios.
- Map the evolving data scientist role from execution to orchestration and identify personal skill gaps to address.
- Learn from real-world implementations across industries to apply proven patterns to your own domain.
- Design responsible, human-centered agentic systems that avoid common deployment pitfalls.
- Module 1
The Agentic Evolution
Defines agentic AI and contrasts it with traditional and generative approaches. Covers market growth, business impact, and a hands-on workflow comparison exercise to identify automation opportunities.
- Module 2
Four Agentic Design Patterns
Andrew Ng's four patterns — Reflection, Tool Use, Planning, and Multi-Agent Collaboration — explained with practical demos. Covers when and why to apply each pattern in real scenarios.
- Module 3
The Evolving Data Science Role
The shift from execution to orchestration and what the Agentic Architect role looks like. Covers new technical tools including LangChain, AutoGen, and CrewAI alongside a personal skills gap analysis.
- Module 4
Real-World Applications
Workflow transformations across ETL, model selection, and EDA with industry use cases from finance, healthcare, e-commerce, and manufacturing. Includes analysis of production systems like AutoGPT and Salesforce Agentforce.
- Module 5
Challenges & Best Practices
Six lessons from McKinsey's 50+ agentic implementations covering ethics, privacy, bias, and transparency. Focuses on building trust and designing for effective human-agent collaboration.