MSBAi: Design Principles & Constraints

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Purpose: This file defines the program design philosophy, guiding principles, and constraints for the MSBAi online degree. For specific course details, credit allocations, semester timelines, and instructor assignments, see program/CURRICULUM.md.

Version: 2.0 Last Updated: February 2026

Core Guiding Principles

1. AI-First, Not AI-Optional

• Every MSBAi course integrates practical AI tool usage
• AI is treated as a productivity accelerator, not theoretical topic
• Students graduate with hands-on LLM experience + prompt engineering skills
• “AI tools” (generic term: ChatGPT, Claude, Copilot, open-source models, etc.) are standard part of analytical workflow
• AI literacy → competency → expertise progression across all courses

2. Projects Are the Primary Learning Vehicle

• Integral to design: 2-3 major projects per 8-week course (not optional extras)
• Live project sessions: Separate, branded tutorial sessions dedicated to:
  • Hands-on project work (guided problem-solving)
  • Tool showcasing (Power BI, SQL, AWS, etc.)
  • AI-augmented analysis demos (how AI accelerates work)
  • Q&A and debugging support
  • Key differentiator from competitors
• Portfolio-driven: All projects contribute to GitHub portfolio
• Real data: Projects use public datasets, case studies, or simulated business problems
• Authentic assessment: No traditional exams; grading based on project quality

3. Python + Jupyter Notebooks as Universal Backbone

• Common computational environment: All students work in Jupyter (Colab or cloud-hosted)
• Reproducibility: Code + narrative + visualizations in single document
• Career asset: Portfolio of Jupyter notebooks demonstrates skills to employers
• Flexibility: Supplementary tools allowed (Power BI, AWS, R, etc.) but Python is primary
• GitHub integration: All project repos hosted on GitHub (version control habit-building)

4. Synchronous Live Sessions for High Engagement (Differentiator)

• Project tutorial sessions: Weekly guided project work (not lectures)
  • Real-time demonstration of tools + AI-assisted workflows
  • Student Q&A and live debugging
  • Showcase best practices + common pitfalls
• High-engagement discussions: Monthly or bi-weekly topics (current events, case studies)
  • Bring in practitioners, guest speakers, industry insights
  • Student-led discussions (builds communication skills)
• Office hours: 1-on-1 or small group mentoring
• Brand these distinctly: “Studio Sessions” (project-focused), “Analytics Conversations” (discussions), “Office Hours” (support)
• Async-first, sync-supplementary: All sessions recorded; attendance optional for flexibility

5. Modular, Flexible Design

• 8-week course format: Every course is self-contained within 8 weeks, enabling flexible scheduling
• No single “right path”: Students choose electives based on career goals
• Future stackable pathways: Certificate options (e.g., exit ramps and re-enrollment) are under development for future cohorts
• Coherent progression: Core → elective → capstone sequence has clear learning objectives and career outcomes

6. Affordability & Accessibility

• Target: 20% cheaper than peer programs (UT Austin, Penn State, Michigan)
• Flexible scheduling: 8-week courses allow working professionals to take 1-2 courses while employed
• Global accessibility: No time zone requirement; all async content available 24/7
• Diverse learners: Support for non-traditional backgrounds (career changers, underrepresented groups)

7. Applied Experiential Learning

• Real capstones: Research Park partnerships + corporate consulting projects (Recommended in Fall 4)
• Alternative capstone: Independent research with real business impact
• Portfolio + Project: Capstone split into portfolio curation (weeks 1-2) + new project (weeks 3-8)
• Public artifact: Final portfolio published on personal GitHub/website (career visibility)

8. Coherent Course Naming & Rubrics

• Naming convention: Use “MSBAi” prefix or thematic titles
  • Option A: MSBAi554, MSBAi513, etc. (emphasizes program cohesion)
  • Option B: Thematic names (Data Foundations, Data Storytelling, Predictive Analytics for Business, etc.)
• Consistent learning outcome structure: All courses use L-C-E (Literacy → Competency → Expertise)
• Clear prerequisites: First core courses have no prerequisites; electives assume core completion

Course details: See program/CURRICULUM.md

9. Version Control & Reproducibility

• GitHub as default: All student code lives on GitHub
• Best practice teaching: Students learn version control as part of course workflow
• Public portfolios: Final projects have public GitHub repos (career visibility)
• Reproducibility: README files, requirements.txt, documentation standards enforced

10. Alignment with Gies Campus AI Framework

• Four campus tracks: MSBAi courses contribute to official Campus AI curriculum
  • AI Basics/Fundamentals ← Core courses
  • AI & ML Technologies ← Electives
  • Agentic Systems & Workflows ← Advanced electives + capstone
  • Human-Centric AI ← Ethics woven throughout
• Credential pathway: MSBAi students can earn Campus AI certificates alongside MSBAi degree

Hard Constraints (Non-Negotiable)

Curricular Constraints

1. Total program: 36 credits (fixed)
2. 8-week format: All courses run 8 weeks (enables flexible, modular scheduling)
3. Fall 2026 launch: First courses must be ready Aug 2026
4. Python + Jupyter primary: All courses must use this as computational backbone
5. Project-based assessment: No traditional final exams; all courses graded on projects + participation
6. Statistics pre-requisites: Coursera Inferential Statistics (Duke) + Basic Statistics (Amsterdam) required before FIN 550
7. Team projects required: Every 8-week course must include at least one team project; 4-week courses are individual only
8. Oral defense required: Every course includes oral defense (20-30% for 8-week courses, 40-50% for capstone)

Course details: See program/CURRICULUM.md

Technology Constraints

1. AI tools: Generic term; no specific vendor lock-in (support multiple platforms)
2. Python version: 3.10+ (modern, stable)
3. Jupyter environment: Google Colab (free) + JupyterHub (paid, institutional option)
4. Cloud infrastructure: AWS Free Tier + student credits (reimbursable, optional)
5. LMS: Canvas (institutional standard)
6. GitHub: Required for all coursework (public portfolios)

Capstone Constraints (Fall 4)

1. Split structure:
   • Part 1 (Weeks 1-2): Portfolio curation + refinement
   • Part 2 (Weeks 3-8): New project (individual or small team)
2. Portfolio component:
   • Select 3-4 best projects from all courses
   • Write reflection narratives
   • Create public GitHub portfolio
   • Practice portfolio pitch (3 min)
3. New project component:
   • Option A: Real consulting project via Research Park (Recommended)
   • Option B: Independent research on self-selected business problem
   • AI assist allowed (documented)
4. Output: GitHub repo + Jupyter notebook + executive summary + video presentation

Soft Constraints (Flexible)

1. Course naming: Can use different rubric (554 vs. MSBAi554) as long as clear
2. Synchronous session timing: Flexible, accommodate global time zones (record all)
3. Capstone project type: Either real client OR independent, based on student preference
4. Elective flexibility: Industry tracks can evolve based on student demand
5. Tool choices: Supplementary tools flexible (R, Scala, different cloud platforms) as long as Python primary

Design Constraints from Context

From IBC Market Research

• Employer demand: AI/GenAI, applied learning, communication skills
• Student drivers: Flexibility, affordability, ROI, career outcomes
• Market gap: Cost, industry integration, emerging skills
• Differentiation needed: Not just “another MSBA” (MSBAi AI-first positioning)

From Undergrad AI Strategy

• L-C-E progression: All learning outcomes follow this model
• Campus AI alignment: Contribute to four campus tracks
• Responsible AI: Ethics + governance woven throughout
• AI literacy for all: Every student graduates AI-ready

From On-Campus MSBA Analysis

• Working professional focus: Adapted for asynchronous, 8-week format
• Hands-on labs: Preserved in cloud-native way
• Real data: Finance, business datasets maintained
• Project-based: Shifted from exams to continuous projects

Success Criteria

Must-Have

1. Every course has 2-3 major projects
2. All students use Python + Jupyter + GitHub
3. Weekly project-focused live sessions
4. First courses ready by Aug 2026
5. All 36 credits accounted for
6. Capstone split into portfolio + new project

Nice-to-Have

1. Real consulting capstones via Research Park
2. Industry-specific elective tracks
3. Modular 8-week format (future stackable pathways under development)
4. Guest speaker series (monthly)
5. Student peer learning communities

Red Flags (What Could Go Wrong)

1. Content development delays: Risk if Jupyter Book infrastructure not ready by March 1
2. Faculty availability: 5+ instructors needed; ensure course development time allocated
3. Cloud infrastructure costs: AWS + JupyterHub must be affordable (pass-through model)
4. Enrollment: Conservative first cohort (50-75 students); recruitment messaging must differentiate
5. Tool ecosystem changes: Keep “AI tools” generic to adapt to platform changes