MSBAi: Master of Science in Business Analytics - Online
AI-First · Project-Based · Flexible · Affordable
Welcome to the MSBAi curriculum design documentation. This site contains program planning documents for the Master of Science in Business Analytics - Online degree at Gies College of Business, University of Illinois, launching Fall 2026.
Single source of truth: All program-level facts (credits, duration, course sequence, faculty) are maintained in program/curriculum.md. This page summarizes key facts — refer to curriculum.md for authoritative details.
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Core Planning Documents
| Document | Purpose | For Whom |
|---|---|---|
| Curriculum (Source of Truth) | Credits, semesters, course sequence, faculty | Everyone |
| AI-First Strategy | Strategic positioning, curriculum vision, pathways | Leadership, marketing |
| Design Principles | 10 principles + hard/soft constraints | Faculty, instructional designers |
Course Syllabi (8-week online format)
| Course | Type | Credits | Term | Syllabus |
|---|---|---|---|---|
| BADM 554 - Enterprise Database Management | Core | 4 | Fall 2026 (Wk 1-8) | BADM554 |
| BDI 513 - Data Storytelling | Core | 4 | Fall 2026 (Wk 5-12) | BDI513 |
| FIN 550 - Big Data Analytics in Finance (ML I) | Core | 4 | Fall 2026 (Wk 9-16) | FIN550 |
| BADM 558 - Big Data Infrastructure | Advanced | 4 | Spring 2027 (Wk 1-8) | BADM558 |
| Agentic AI for Analytics | Elective | 2 | Spring 2027 (Wk 5-8) | Agentic AI |
| Quantum Computing for Optimization | Elective | 2 | Spring 2027 (Wk 9-12) | Quantum |
| General Elective (any iMBA) | Elective | 4 | Spring 2027 (Wk 9-16) | Details |
| BADM 557 - Business Intelligence | Core | 4 | Summer 2027 (Wk 1-8) | BADM557 |
| BADM 576 - Data Science and Analytics (ML II) | Advanced | 4 | Fall 2027 (Wk 1-8) | BADM576 |
| Capstone | Capstone | 4 | Fall 2027 (Wk 9-16) | Capstone |
Program Design & Operations
| Document | Purpose | For Whom |
|---|---|---|
| Target Profile | Who we’re building for — career pivoters, admission criteria, positioning | Admissions, marketing, leadership |
| Cohort Model | Student experience, sync sessions, community | Faculty, instructional designers |
| Assessment Strategy | Assessment philosophy, rubrics, AIAS levels, feedback | Faculty, instructional designers |
| Standard Tools | VS Code, Copilot, Colab, Power BI, WRDS — what students use | Faculty, IT |
| Course Names | Canonical names, rename history, credit breakdown | Everyone |
| Competitive Analysis | Market research, competitors, positioning | Leadership, marketing |
| Curriculum Evaluation | Scorecard tracking curriculum design decisions | Leadership, faculty |
| Future Electives & Tracks | Specialization tracks, stackable pathways (ideation) | Leadership |
Program Differentiators
1. AI-First Analytics Curriculum The MSBAi program integrates AI and modern analytics tools throughout every course rather than treating AI as a standalone topic. Students learn to apply AI alongside analytics techniques to solve real business problems — from Week 1 through capstone.
2. Project-Based Learning with Real-World Application Each course centers on one major team project, scaffolded by weekly assignments (cases, labs, discussions, exercises) that build the skills students apply in their project. No traditional exams — assessment is 100% applied, with progressive milestones and oral defense in every course.
3. Professional Portfolio that Demonstrates Impact Throughout the program, students build a professional analytics portfolio showcasing their projects and technical capabilities. The portfolio provides a tangible way to demonstrate expertise to employers, highlighting both AI literacy and career readiness.
4. Two-Part Capstone (Project + Portfolio Storytelling) The program culminates in a two-part capstone: students first curate and present a professional portfolio highlighting their strongest work from across the program, then apply their analytics and AI skills to a comprehensive applied project. Graduates can not only perform advanced analytics but also effectively communicate insights and business impact to decision-makers.
5. Immersive Instructional Model with High-Touch Support Beyond asynchronous coursework, students participate in live project studios, analytics discussions, and office hours that provide opportunities for collaboration, mentorship, and real-time problem solving. Individualized guidance with the flexibility of recorded sessions.
Program At A Glance
| Duration | 15 months (3 semesters + 1 summer), starting Fall 2026 |
| Credits | 36 total — 16 core + 8 advanced + 8 elective + 4 capstone |
| Format | 8-week courses within 16-week semesters |
| Delivery | Async-first + weekly live studio sessions |
| Target Student | Career pivoters (age 25-40) entering analytics for the first time — full profile |
| Assessment | 100% applied — weekly assignments + one major team project per course. Oral defense in every course. AIAS levels on every assessment. No exams. |
| Tools | VS Code + Copilot, Python, Power BI, GitHub, Colab — full list |
Semester Sequence
| Semester | Credits | Courses | |———-|———|———| | Fall 2026 | 12 | Enterprise Database Management → Data Storytelling → Big Data Analytics in Finance (ML I) | | Spring 2027 | 12 | Big Data Infrastructure + Agentic AI + Quantum + General Elective | | Summer 2027 | 4 | Business Intelligence | | Fall 2027 | 8 | Data Science and Analytics (ML II) → Capstone |
Gies College of Business — University of Illinois Urbana-Champaign