Syllabus: AINS6006 Big Data Management for AI Applications#
Course Identity#
Field |
Value |
|---|---|
Course |
AINS6006 Big Data Management for AI Applications |
Program |
Aurnova Master of Science in Artificial Intelligence |
Track |
Core |
Credits |
3 graduate credits |
Format |
8-week online graduate course with weekly instructor interaction |
Primary environment |
Google Colab for first-run labs; GitHub Codespaces for repository-based or instructor-guided work |
Catalog Description#
Designs data platforms, pipelines, lineage, cloud integration, and security for AI workflows.
Student Audience and Prerequisites#
Completion of the earlier core sequence or equivalent professional preparation is recommended. Students do not need to be computer science majors, but they should be willing to use guided notebooks and revise evidence-based artifacts. The course is designed for graduate students preparing to lead, evaluate, manage, or apply AI work in professional settings. Students are not expected to be computer science majors. Technical work is scaffolded so students can run a notebook, observe output, change one controlled variable, and explain the evidence in professional language.
Course-Level Learning Outcomes#
By the end of this course, students will be able to:
CO1: Analyze professional problems in Big Data Management for AI Applications and formulate AI use cases with explicit stakeholders, decision boundaries, data assumptions, and success criteria.
CO2: Execute or interpret reproducible notebook-based investigations that demonstrate core Big Data Management for AI Applications methods using guided Python/Colab workflows.
CO3: Evaluate model, workflow, or governance evidence for accuracy, validity, uncertainty, bias, security, privacy, and operational limitations appropriate to core MSAI contexts.
CO4: Produce professional artifacts for Big Data Management for AI Applications, including briefs, model cards, risk registers, evaluation memos, implementation plans, or executive recommendations.
CO5: Apply responsible AI, academic integrity, data stewardship, accessibility, and human-oversight expectations to course work and proposed deployments.
CO6: Communicate AI findings, limitations, tradeoffs, and next-step recommendations to technical and nontechnical stakeholders using clear graduate-level evidence.
Credit-Hour and Contact-Hour Rationale#
This 3-credit accelerated graduate course expects approximately 135 total student learning hours across the 8-week term. The course design allocates approximately 45 hours to instructor-led lecture, narrated content, guided lab demonstration, discussion, feedback, and synchronous or asynchronous interaction. Approximately 90 additional hours are allocated to reading, notebook practice, applied assignments, revision, and the final portfolio artifact. Each module contains enough slide and narration material for a 90-minute class session, plus labs and assignments that extend learning beyond direct instruction.
Weekly Schedule and Major Deliverables#
Week |
Module Topic |
Essential Question |
Primary Deliverable |
|---|---|---|---|
1 |
Data architectures for AI |
What architecture supports trustworthy AI workflows? |
Module 1 Assignment: Data architectures for AI |
2 |
Pipelines, orchestration, and quality |
How do data pipelines fail, and how are failures detected? |
Module 2 Assignment: Pipelines, orchestration, and quality |
3 |
Storage, indexing, and retrieval |
How do access patterns shape storage choices? |
Module 3 Assignment: Storage, indexing, and retrieval |
4 |
Distributed processing and scale |
When does scale require distributed computation? |
Module 4 Assignment: Distributed processing and scale |
5 |
Metadata, lineage, and provenance |
How do we preserve the story of data transformations? |
Module 5 Assignment: Metadata, lineage, and provenance |
6 |
Cloud integration and cost control |
How do cloud choices affect reliability and budget? |
Module 6 Assignment: Cloud integration and cost control |
7 |
Security and access governance |
How should sensitive data be protected across AI workflows? |
Module 7 Assignment: Security and access governance |
8 |
AI data platform readiness review |
What proves a data system can support production AI? |
Module 8 Assignment: AI data platform readiness review |
Assessment Plan#
For Populi implementation, Modules 1–3 and 5–7 supply the six module-applied-assignment scores; all eight labs supply the lab/reflection scores; the Module 4 assignment plus its Modules 1–3 synthesis is the mid-course brief; and the Module 8 data-platform readiness review is the final portfolio artifact. Modules 4 and 8 must not be counted again as generic module assignments.
Component |
Weight |
Evidence |
|---|---|---|
Module applied assignments |
35% |
Six professional artifacts from Modules 1–3 and 5–7, scored with module rubrics; Modules 4 and 8 are assessed in the signature categories below. |
Notebook labs and reflections |
20% |
Guided Colab/Codespaces labs with before-after interpretation and limitation statements. |
Mid-course synthesis brief |
15% |
Integrative memo or design artifact covering Modules 1-4. |
Final applied portfolio artifact |
20% |
Cumulative artifact demonstrating evidence, risk reasoning, and stakeholder communication. |
Participation and professional engagement |
10% |
Discussion, peer review, instructor check-ins, and revision responsiveness. |
Passing performance requires both cumulative course performance of 70% or higher and submission of the final applied portfolio artifact. Graduate-level performance requires accurate vocabulary, evidence-based reasoning, responsible limitation statements, and professional communication. Students may revise selected artifacts when the instructor determines that revision supports learning outcomes and academic integrity.
Grading Scale#
Grade |
Range |
Interpretation |
|---|---|---|
A |
93-100 |
Excellent graduate performance; evidence is rigorous, well communicated, and professionally defensible. |
A- |
90-92 |
Strong graduate performance with minor gaps in depth, precision, or integration. |
B+ |
87-89 |
Good performance; work is complete and evidence-based but not consistently advanced. |
B |
83-86 |
Satisfactory graduate performance; meets core expectations with some limitations. |
B- |
80-82 |
Marginal graduate performance; recurring gaps require attention. |
C |
70-79 |
Minimal passing performance; significant weaknesses in evidence, accuracy, or communication. |
F |
Below 70 |
Does not demonstrate required course outcomes. |
Required Readings, Standards, and Professional Resources#
The instructor may update editions, links, or equivalent institutional resources before the term begins. Readings combine scholarly foundations, professional standards, and applied guidance.
Before the course opens, the instructor or program designee verifies library access, link currency, accessible formats, and any approved substitutions. Populi identifies the authoritative assigned edition or link for the live offering; students should use that version rather than an older saved copy.
Module |
Topic |
Required Reading or Standard |
Applied Professional Resource |
|---|---|---|---|
1 |
Data architectures for AI |
Kleppmann, Designing Data-Intensive Applications, data models and storage. |
DAMA-DMBOK, data governance and quality sections. |
2 |
Pipelines, orchestration, and quality |
DAMA-DMBOK, data governance and quality sections. |
Apache Airflow or Prefect documentation on orchestration concepts. |
3 |
Storage, indexing, and retrieval |
Apache Airflow or Prefect documentation on orchestration concepts. |
Kleppmann, Designing Data-Intensive Applications, distributed data systems. |
4 |
Distributed processing and scale |
Kleppmann, Designing Data-Intensive Applications, distributed data systems. |
OpenLineage or DataHub documentation on metadata and lineage. |
5 |
Metadata, lineage, and provenance |
OpenLineage or DataHub documentation on metadata and lineage. |
Cloud provider architecture guidance for AI data platforms. |
6 |
Cloud integration and cost control |
Cloud provider architecture guidance for AI data platforms. |
NIST Cybersecurity Framework 2.0, Protect and Govern functions. |
7 |
Security and access governance |
NIST Cybersecurity Framework 2.0, Protect and Govern functions. |
NIST AI RMF 1.0, lifecycle evidence and data documentation. |
8 |
AI data platform readiness review |
NIST AI RMF 1.0, lifecycle evidence and data documentation. |
Kleppmann, Designing Data-Intensive Applications, data models and storage. |
Learning Technology and Beginner Support#
Students should start notebook labs in Google Colab unless the instructor specifies Codespaces. Colab lowers setup burden and supports students who are new to Python. Students should first run the notebook unchanged, then change one small value and explain what changed. Codespaces is used when an activity requires the full repository, multiple files, tests, or GitHub workflow evidence. The course assesses interpretation, evidence, limitations, and professional judgment; it does not assume that every student enters as a programmer.
Before launch, the instructor or program designee tests Colab and Codespaces access, equivalent activity paths, assistive-technology concerns, and the support escalation route. Populi provides the current accessibility, accommodation, and technical-support contacts for the live offering.
Academic Integrity and Permitted AI Assistance#
Students must submit their own analysis, decisions, explanations, and reflections. Use of generative AI tools is permitted for brainstorming, debugging, summarizing public documentation, and improving prose when allowed by the instructor, but students must disclose material AI assistance and remain responsible for factual accuracy, citations, code behavior, and final claims. Fabricated citations, hidden AI-generated submissions, unauthorized collaboration, or submission of work the student cannot explain may be treated as academic misconduct.
Accessibility and Accommodations#
Aurnova is expected to provide reasonable accommodations consistent with institutional policy and applicable law. Students who need accommodations should contact the designated institutional office and notify the instructor as early as possible. Course materials should be provided in accessible formats when feasible, including readable HTML pages, downloadable notebooks, text-based instructions, and alternatives for activities that require specific hardware or external accounts.
Data Privacy, Safety, and Responsible Use#
Unless explicitly approved by the instructor and institution, students must not upload confidential, regulated, proprietary, patient, student, financial, security-sensitive, or personally identifiable information into Colab, Codespaces, public repositories, or third-party AI tools. Labs use synthetic or public proxy data. Students must document data assumptions, privacy risks, and operational limits before recommending real-world use.
Late Work, Participation, and Instructor Interaction#
Students are expected to engage weekly with readings, lecture material, labs, and applied deliverables. Late work may receive reduced credit unless prior arrangements are approved or institutional policy applies. The instructor should provide regular substantive interaction through announcements, discussion, feedback, office hours or appointments, assignment feedback, and timely grading. Participation is evaluated by evidence of preparation, constructive discussion, peer feedback, and revision effort rather than by technical bravado.
Accreditation Evidence Location#
Aurnova maintains program-outcome mappings, assessment records, and continuous-improvement evidence separately from the public learner site. Those records are not learner coursework or a substitute for the syllabus, rubric, or Populi gradebook.