MSDS 432 Foundations of Data and AI Engineering

Course Description #

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. Students design and implement scalable data pipelines, perform requirements analysis, and develop containerized microservices for automated batch and streaming workflows. They build intelligent data systems that integrate structured and unstructured data as needed 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. Prerequisites: (1) MSDS 400-DL Math for Modelers and (2) MSDS 420-DL Database Systems or CIS 417 Database Systems Design..

Students benefit by taking the Go Learning Studio and MSDS 431 Go and AI-Assisted Programming before taking this course.

What is required of students? Students participate in weekly discussion forums and work on challenging programming assignments. They must also complete an term project, implementing a data pipeline for business intelligence. The figure below provides an example data pipeline employed in this course.

Go to Data Engineering Courses.