Syllabus: AINS6006 Big Data Management for AI Applications

Catalog Description

Designs data platforms, pipelines, lineage, cloud integration, and security for AI workflows.

Course Structure

Each week includes readings, a lecture/slide sequence, an executable lab, and an applied deliverable. Students maintain a reproducible project record and submit work through the LMS or GitHub workflow selected by the instructor.

Weekly Schedule

Week	Topic	Essential Question	Deliverable
1	Data architectures for AI	What architecture supports trustworthy AI workflows?	Lab notebook + assignment brief
2	Pipelines, orchestration, and quality	How do data pipelines fail, and how are failures detected?	Lab notebook + assignment brief
3	Storage, indexing, and retrieval	How do access patterns shape storage choices?	Lab notebook + assignment brief
4	Distributed processing and scale	When does scale require distributed computation?	Lab notebook + assignment brief
5	Metadata, lineage, and provenance	How do we preserve the story of data transformations?	Lab notebook + assignment brief
6	Cloud integration and cost control	How do cloud choices affect reliability and budget?	Lab notebook + assignment brief
7	Security and access governance	How should sensitive data be protected across AI workflows?	Lab notebook + assignment brief
8	AI data platform readiness review	What proves a data system can support production AI?	Lab notebook + assignment brief

Assessment

Component	Weight
Weekly labs and notebooks	30%
Applied assignments	35%
Participation and technical critique	15%
Final synthesis portfolio	20%

Graduate Expectations

Submissions must show technical reasoning, evidence awareness, clear limitations, and responsible use of AI assistance. Code and analysis should be reproducible enough for instructor review.