Generative AI for Data Engineers Specialization Course Syllabus

Overview: This specialization is designed for data engineering professionals seeking to integrate generative AI into their workflows. Comprising three core courses and a final project, the program requires approximately 27 hours to complete and is structured to be flexible and self-paced. Learners will gain hands-on experience applying generative AI to real-world data engineering tasks such as data generation, pipeline development, and infrastructure design, culminating in a practical final project. No prior AI experience is required, making it accessible to beginners while still offering value to experienced practitioners.

Module 1: Generative AI: Introduction and Applications

Estimated time: 7 hours

• Understanding the fundamentals of generative AI
• Distinguishing generative AI from discriminative AI
• Exploring real-world use cases across industries
• Introduction to generative AI models and capabilities

Module 2: Generative AI: Prompt Engineering Basics

Estimated time: 7 hours

• Foundations of prompt engineering
• Zero-shot and few-shot prompting techniques
• Designing effective prompts for accuracy and relevance
• Tools and frameworks for prompt development

Module 3: Generative AI: Elevate Your Data Engineering Career

Estimated time: 13 hours

• Applying generative AI to data warehouse schema design
• Automating infrastructure setup with AI-generated code
• Enhancing ETL workflows using generative AI
• Generating and augmenting datasets for analytics

Module 4: Data Generation and Augmentation with Generative AI

Estimated time: 5 hours

• Techniques for synthetic data generation
• Data augmentation strategies for improved model performance
• Ensuring data quality and consistency

Module 5: AI-Powered Data Pipeline Development

Estimated time: 6 hours

• Building data pipelines using generative AI
• Querying databases with natural language prompts
• Optimizing pipeline workflows with AI assistance

Module 6: Final Project

Estimated time: 8 hours

• Design a data warehouse schema using generative AI
• Develop an automated ETL pipeline with AI-generated scripts
• Submit a comprehensive project report with implementation insights

Prerequisites

• Familiarity with basic data engineering concepts
• Understanding of databases and ETL processes
• Basic knowledge of cloud platforms (helpful but not required)

What You'll Be Able to Do After

• Understand the core principles of generative AI and its role in data engineering
• Apply prompt engineering techniques to guide AI models effectively
• Generate and augment data using generative AI tools
• Implement AI-driven solutions in data pipeline and infrastructure development
• Elevate your data engineering career with practical, hands-on AI integration skills