Program-level details: See program/curriculum.md
MSBAi Cohort Model: Student Experience & Synchronous Touchpoints
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: 35-50 career pivoters per Fall intake
Rationale:
- Small enough that everyone knows each other by name (critical for pivoter mutual support)
- Large enough to sustain peer learning, team formation, and diverse industry backgrounds
- Manageable for 4-5 faculty + 2-3 TAs
- Cohort of exclusively career pivoters creates shared identity and mutual accountability
- Economically viable with focused marketing and lower support costs per student
See program/target_profile.md for full student profile, admission criteria, and future cohort expansion plans.
Part 2: Synchronous Session Types
Session 0: Live Session (90 minutes, weekly, per course)
Purpose: Direct content delivery by lead faculty. Covers the week’s conceptual material with occasional guest speakers or case discussions at faculty discretion.
Format:
- Lead faculty teaches the week’s content live (70-75 min)
- Occasional guest speaker replaces or supplements faculty content (30-45 min talk + Q&A)
- Brief Q&A / preview next week
Instructor Role:
- Primary content delivery for the week
- Connect course material to real-world applications
- Introduce or host guest speakers as relevant
Attendance:
- Optional but strongly encouraged
- All sessions recorded + uploaded within 24 hours
Scheduling:
- Same time every week per course (time slots TBD based on cohort timezone distribution)
Session 1: Project Studio / Tutorial (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 Conversations (faculty-discretion, folded into Live Session or Project Studio)
Status (as of 2026-04-22): Analytics Conversations are not scheduled as a separate program-wide bi-weekly session. Faculty incorporate guest speakers, case discussions, and current-events conversations into the weekly Live Session or Project Studio at their discretion — no fixed cadence or format required.
Formats faculty may use (within Live Session or Studio):
Format A: Guest Speaker
- 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
- 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 who successfully pivoted into analytics roles
- Industry practitioners (finance, healthcare, tech companies)
- Hiring managers from analytics teams (what they look for in career changers)
- AI/ML researchers (academia or corporate R&D)
- Career coaches specializing in STEM/analytics transitions
Rationale: Preserves faculty flexibility and avoids adding a scheduling burden on students (who already attend Live + Studio weekly). Content still happens — just distributed rather than timetabled separately. See discussions/DECISIONS.md → “Weekly workload structure for 8-week, 4-credit courses” (2026-04-22).
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)
Session 4: Career Transition Circle (45 minutes, bi-weekly)
Purpose: Dedicated space for career pivoters to share job search progress, practice skills, and build professional networks for their transition into analytics.
Format:
- First 15 min: Career topic presentation (resume review, LinkedIn optimization, interview prep, networking strategy, salary negotiation)
- Next 20 min: Peer accountability check-ins (breakout rooms of 4-5, each shares progress on career transition milestones)
- Last 10 min: Open discussion + resource sharing
Guest Speakers:
- MSBAi alumni who successfully pivoted into analytics
- Hiring managers from analytics teams (what they look for in career changers)
- Career coaches specializing in STEM/analytics transitions
Scheduling:
- Bi-weekly at a consistent program-wide time (independent of course-level Live/Studio sessions)
- Recorded for async access
- Begins Spring 2027 (after students have completed first semester and begin thinking about career positioning)
Updated: February 2026