Program-level details: See program/CURRICULUM.md
MSBAi Cohort Model: Student Experience & Synchronous Touchpoints
Version: 1.0 Last Updated: February 2026
Document Purpose
This document defines how MSBAi creates community, engagement, and support for online students despite asynchronous delivery. It covers:
- Cohort Structure: Size and composition
- Synchronous Sessions: Three distinct types (Studio, Conversation, Office Hours)
Part 1: Cohort Structure
Cohort Size & Composition
Target Cohort Size: 50-75 students per Fall intake
Rationale:
- Small enough for community (everyone knows each other)
- Large enough to sustain peer learning + discussions
- Manageable for 5-6 faculty + 3-5 TAs
- Economically viable (balance cost vs. revenue)
Part 2: Synchronous Session Types
Session 1: Project Studio (90 minutes, weekly)
Purpose: Hands-on project work with live instructor guidance, peer collaboration, AI-augmented analysis demos.
Format:
- First 30 min: Live coding walkthrough of project challenge (instructor demos approach)
- Next 45 min: Students work on projects (breakout rooms for pair programming)
- Last 15 min: Debrief + Q&A + preview next week
Instructor Role:
- Share screen + live-code a solution approach
- Demonstrate AI tools (Claude for analysis, ChatGPT for code generation)
- Monitor breakout rooms, answer questions
- Share best practices + common pitfalls
Attendance:
- Optional but strongly encouraged
- All sessions recorded + uploaded within 24 hours
- Asynchronous alternative: Pre-recorded walkthroughs + async Q&A on forum
Scheduling:
- Same time every week (time slots TBD based on cohort timezone distribution)
- Rotate time slots after midterm (accommodate students across zones)
- Record all sessions for async access
Tools:
- Zoom (video conferencing)
- Breakout rooms (pair programming)
- Shared Jupyter notebooks (live coding demo)
- GitHub (share solution code during session)
- Discord (chat during studio for quick questions)
Session 2: Analytics Conversation (60 minutes, bi-weekly)
Purpose: High-engagement discussions on current topics, guest speakers, case study analysis, emerging trends in data/AI/analytics.
Format Options:
Format A: Guest Speaker (Every Other Week)
- Industry practitioner talks about real project (30 min)
- Student Q&A (20 min)
- Discussion: How could you approach this problem? (10 min)
Format B: Case Study Discussion (Every Other Week)
- Assign case 1 week in advance
- Breakout rooms: 4-5 students per room discuss approach
- Share group answers (30 min of presentations)
- Instructor synthesis (20 min)
- Vote on best approach
Format C: Emerging Topics
- Current events in data/AI (recent news, tech announcements)
- Students analyze impact + implications
- Debate: Should we use this technology?
Guest Speaker Sources:
- MSBAi alumni (back to mentor + share experience)
- Industry practitioners (finance, healthcare, tech companies)
- AI/ML researchers (academia or corporate R&D)
- Thought leaders (Data Science Council, local business community)
Scheduling:
- Bi-weekly alternating with Project Studio
- Consistent day/time (e.g., Fri 2:00pm UTC)
- Recorded for async access (live Q&A only)
Session 3: Office Hours (30-60 minutes, weekly + drop-in)
Purpose: 1-on-1 or small-group mentoring, debugging, career advice, personal check-ins.
Format:
- Recurring office hours: Same time every week (10-15 min per student)
- Drop-in hours: Dedicated time for ad-hoc questions
- Slack/Discord: Asynchronous Q&A between sessions
Instructor Responsibilities:
- Code review + debugging help
- Conceptual questions (“I don’t understand logistic regression”)
- Project strategy (“Am I on the right track?”)
- Career conversations (“Should I pivot to data engineering?”)
- Mental health check-ins (“How are you coping with course load?”)
TA Responsibilities:
- Initial triage (route to instructor if needed)
- Troubleshooting technical setup (AWS, Jupyter, GitHub)
- DataCamp/course platform issues
- Peer mentoring (senior TA helps junior students)
Updated: February 2026