Foundations of Data and AI Engineering

1 Unit · Graduate

This course introduces modern data engineering concepts and practices with an emphasis on data pipeline orchestration, ETL (Extract, Transform, Load) processes, data ecosystems, Retrieval-Augmented Generation (RAG), multimodal data processing with Large Language Models (LLMs), and Generative AI for synthetic data. Students learn to design and implement scalable data pipelines, perform requirements analysis, and develop containerized microservices for automated batch and streaming workflows. They use modern technologies to build intelligent data systems that integrate structured and unstructured data. The course emphasizes hands-on experimentation, system optimization, and GenAI-assisted automation to prepare students for advanced data science and analytics applications. This is a case study and project-based course with a strong programming component. Recommended prior course: MSDS 431-DL Go and AI-Assisted Programming.