DSBA 6171: Data Engineering for AI

University of North Carolina, Charlotte

Data Engineering for AI equips practitioners with the skills to design the data foundations that make AI systems work in the real world. The course covers the full data lifecycle-ingestion, transformation, storage, orchestration, and governance-across structured and unstructured, text, image, and audio data. Students learn to build ETL/ELT pipelines, integrate batch and streaming sources, engineer features for ML, and implement vector/embedding stores for RAG-style applications. Emphasis is placed on data quality (validation, drift, bias), metadata and lineage, and treating prompts as structured inputs in AI workflows. A team capstone applies these skills to deploy a multimodal pipeline in a cloud environment and communicate results to stakeholders. A functional knowledge of Python and SQL is required for this course.