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MSBAi Capstone/Practicum

Program-level details: See program/curriculum.md

Status: Draft

Structure outlined (portfolio + applied project); pending detailed design with Vanitha. Part sequencing (portfolio-first vs project-first) is TBD — Vanitha to decide.

Learning Outcomes (L-C-E Framework)

Literacy:

• L1: Articulate the end-to-end analytics lifecycle from problem framing through deployment and stakeholder communication
• L2: Explain how different analytical methods (ML, BI, NLP, data engineering) apply to distinct business problems

Competency:

• C1: Polish analytical work into professional, portfolio-ready artifacts (clean repos, READMEs, reproducible code)
• C2: Integrate skills from at least 3 of the 6 core competency areas (Data Management, Communication, Predictive Modeling, Big Data, Business Intelligence, Advanced ML) in a single project
• C3: Document AI tool usage with appropriate attribution and governance considerations
• C4: Deliver an oral defense demonstrating individual mastery of project methodology and findings

Expertise:

• E1: Design and execute an independent analytical project addressing a real business problem
• E2: Frame analytical results as a career transition narrative — connecting prior domain experience with new analytical capabilities
• E3: Evaluate trade-offs in analytical approach selection and defend choices to a professional audience

Technology Stack

• IDE: VS Code with GitHub Copilot; Google Colab (browser alternative)
• Notebooks: Jupyter Notebooks (via Colab or VS Code)
• Version Control: GitHub (portfolio repos are the primary career artifact)
• BI Tool: Power BI (for dashboard components)
• AI Tools: Claude, ChatGPT, Copilot — all permitted with full disclosure
• Presentation: Zoom (oral defense), video recording tools

Overview

The capstone is the culminating experience of the MSBAi program. Students demonstrate both cumulative mastery and the ability to produce new independent work. The program concludes with a two-part capstone in the final semester. Students apply their analytics and AI skills to a comprehensive project and curate a professional portfolio highlighting their strongest work from across the program, preparing them to effectively communicate insights to employers.

Note: The order of the two parts below (Portfolio → Applied Project) reflects the current working design, but final sequencing is pending Vanitha’s decision. Do not communicate a specific order in external-facing materials until confirmed.

Faculty have discretion over project format, team structure, and assessment weight allocation within the ranges and guidelines below.

Part 1: Professional Portfolio (Weeks 1-4, Individual)

Students select and polish their 4 strongest projects from the 8 prior courses, transforming coursework into career-ready artifacts. Because each course requires project completion throughout the program, this phase focuses on refinement and presentation — not starting from scratch.

Requirement: 4 polished projects. Each must demonstrate skills from a different course.

Portfolio Checklist (suggested standards — faculty may adapt)

For each of the 4 projects, students should:

• Clean and organize the GitHub repository (clear file structure, no dead code)
• Write a professional README (problem statement, approach, results, how to reproduce)
• Ensure code is reproducible (requirements.txt or environment.yml, clear instructions)
• Add or refine visualizations for presentation quality
• Write a reflection narrative (1-2 pages): what was learned, what would be done differently, how AI tools were used
• Tag the polished version as a release in the repo

Career Transition Deliverables

• Write a career transition narrative (1 page): how your prior domain experience + MSBAi training creates unique analytical value for employers
• Update LinkedIn profile with portfolio links, analytics positioning, and MSBAi highlights
• Prepare a “pivoter pitch” (90 seconds): where I came from, what I learned, where I’m going — for networking and interviews

Portfolio Presentation

• Students deliver a portfolio pitch targeted at hiring managers in their desired analytics role (not a generic academic presentation)
• Faculty determine pitch format and length (suggested: 5-8 minutes per student)
• Portfolio is published on student’s GitHub profile (serves as primary career artifact)

Part 2: Applied Project (Weeks 5-8)

Students complete a new analytical project that integrates skills from across the program. Faculty decide the project format based on available partnerships, cohort size, and learning objectives.

Project Format (faculty decides)

If team-based, standard team size is 3 students (2 or 4 in exceptional circumstances). Faculty may use rotating project leads, peer evaluation, or other team accountability mechanisms at their discretion.

Competency Requirement

Every capstone project must demonstrate integration of skills from at least 3 of the 6 core competency areas:

Students identify which competency areas their project addresses in a brief Competency Mapping section of their final deliverable.

Expected Deliverables

Faculty determine exact deliverable requirements. The following are recommended:

• GitHub repository with documented, reproducible code
• Jupyter Notebook(s) or equivalent with analysis, visualizations, and narrative
• Executive summary (2-page memo for non-technical audience)
• AI usage documentation (tools used, where human judgment was applied)
• AI Governance & Risk section (ethical considerations, model limitations, risk assessment)
• Oral defense (see assessment section below)

Confidentiality

For client projects involving proprietary data or NDAs:

• Students honor all confidentiality agreements
• Portfolio versions may use anonymized data, sanitized outputs, or methodology-only summaries
• Faculty work with sponsors to define what can be shared publicly

Assessment Guidelines

Faculty allocate weights within these suggested ranges. The goal is flexibility while ensuring the capstone assesses both cumulative learning (portfolio) and new work (project).

AI Usage Levels (AIAS)

Suggested Weight Ranges

Guardrails:

• No single component should exceed 40%
• Oral defense must be at least 20% (program-wide policy for capstone)
• If team-based, include a mechanism for individual accountability (peer evaluation, individual Q&A, git commit review, or similar)

Oral Defense

The oral defense verifies individual understanding and develops professional presentation skills.

Recommended format:

• Presentation to faculty panel (and client sponsor, if applicable)
• Q&A where panel probes individual understanding
• Each student expected to answer questions on any part of the project
• Faculty determine length (suggested: 15-20 min presentation + 10 min Q&A for teams; 10-15 min total for individual projects)

Suggested assessment dimensions (from program oral defense rubric):

• Clarity of explanation
• Technical depth
• Response to questions
• Methodology justification
• AI usage awareness

Process Documentation (recommended)

Faculty are encouraged to include process documentation as a graded component:

• What approaches were tried and why
• How AI tools were used and how outputs were validated
• What was learned from failures or dead ends
• Iteration evidence (drafts, version history)

Peer Evaluation (if team-based)

When projects are team-based, faculty should include peer evaluation:

• Anonymous evaluation using a standardized rubric (contribution, reliability, communication)
• At minimum at project completion; midpoint check recommended
• Faculty review for outliers and adjust individual grades if needed

Suggested Timeline

Faculty adapt this timeline to their course structure:

Key Constraints

• AI tools are allowed and encouraged throughout, but must be documented
• Every project must include an “AI Governance & Risk” section
• Portfolio projects are always individual work
• Confidentiality agreements are honored for client projects
• The portfolio serves as the student’s primary career artifact from the program
• Projects must demonstrate skills from at least 3 core competency areas

Career Transition Integration

The capstone is the critical “bridge to employment” for career pivoters. Beyond technical deliverables:

• Career narrative: Students articulate how their prior domain experience + MSBAi training creates unique value for employers
• Portfolio pitch: Targeted at hiring managers in the student’s desired analytics role (not a generic academic presentation)
• Employer demo day: Final presentations open to hiring managers and industry partners (design details TBD)
• Career services coordination: Resume review, interview prep, and job search strategy integrated into Part 1 timeline