MSBAi Curriculum
Build the technical and analytical skills needed to lead in the age of AI.
The MSBAi curriculum transforms working professionals into analytics practitioners who can work with AI. Over 15 months, you move from data foundations and machine learning through AI-powered analytics systems to a real-client practicum project, entirely online, while working full time. Every course ends with a project added to your professional portfolio. By graduation, you have a body of work employers can evaluate – not just a degree.
15 Months | 36 Credits | 100% Online | 8-Week Courses
AI-native
The MSBAi program is built around an AI-native, human-centered analytics curriculum that integrates artificial intelligence and modern analytics tools throughout the program rather than treating AI as a standalone topic.
Students learn to leverage and integrate AI with analytics techniques to amplify human insight and decision-making in solving real business problems.
Portfolio development
Every course in the MSBAi curriculum includes one major project that you develop across the 8-week term. Projects use real data, address real business problems, and are published to your GitHub portfolio.
From your first course to your practicum, you are building a professional record of technical and analytical work.
Fall Semester (12 Credits)
Students begin by developing foundational skills in data systems, communication, and predictive modeling – the three capabilities that underpin every analytics role.
BADM 554: Enterprise Database Management (4 credits)
You build the practical skills that every analytics role requires: SQL for querying databases, Python for data analysis, ETL pipelines for moving and transforming data, and cloud-ready workflows for working at scale. Projects use real business datasets. AI tools (GitHub Copilot, Claude) are introduced as coding accelerators from week one. Portfolio artifact: End-to-end data pipeline project.
BDI 513: Data Storytelling (4 credits)
Analytics only creates value when it drives decisions. This course develops your ability to explore data visually, identify the story in the numbers, and communicate insights to non-technical audiences. You work with real business and public datasets, using AI tools to accelerate exploration and refine visualizations. Portfolio artifact: Data storytelling case study with visualization dashboard.
FIN 550: Big Data Analytics in Finance (4 credits)
This is ML I. You learn to build, evaluate, and select predictive models – regression, classification, regularization, tree-based methods, and neural networks – using rich financial and business datasets. No finance background is required; the methods are universal, and financial data is among the richest available for learning ML in a business context. AI tools assist with code generation, model comparison, and result interpretation throughout. Portfolio artifact: Trading signal system – a complete predictive modeling pipeline from data to portfolio strategy.
Spring Semester (12 Credits)
In the second semester, students expand into big data infrastructure, AI-powered analytics systems, and emerging computational methods.
BADM 558: Big Data Infrastructures (4 credits)
You learn to design and build scalable data systems in the cloud. Using AWS, dbt, Apache Spark, Redshift, and Snowflake, you build data pipelines capable of handling large-scale analytics workloads. AI tools assist with pipeline design and cloud configuration.
Portfolio artifact: End-to-end cloud data pipeline, production-ready.
Quantum Cognition (2 credits)
This course examines why human judgment systematically departs from classical rationality — and why those departures matter for managers working alongside AI. Drawing on quantum probability theory as a modeling framework (no physics or advanced math required), you learn to diagnose order effects, conjunction fallacies, and ambiguity aversion in real organizational settings.
Quantum Computing for Better Business Decision Making (2 credits)
This course introduces quantum computing concepts and their emerging applications to optimization and decision problems in business. Using Python and Qiskit simulators, you explore how quantum methods may reshape complex analytical tasks. Portfolio artifact: Quantum optimization implementation on a business problem.
Agentic AI (4 credits)
You learn to build AI-powered analytics systems – not just use them. The course covers large language model fundamentals, retrieval-augmented generation (RAG), prompt engineering, and the design of agentic AI workflows that automate and augment analytics processes. This is where AI moves from tool to system. Portfolio artifact: Functioning AI-powered analytics workflow.
Summer Semester (4 Credits)
BADM 557: Business Intelligence (4 credits)
You develop the practical BI skills that translate data into business decisions: framing the right questions, analyzing data across functions, building dashboards, and communicating findings to leadership. AI tools are used throughout to accelerate analysis and sharpen recommendations.
Portfolio artifact: BI dashboard and business case analysis using real organizational data.
Final Fall Semester (8 Credits)
The final semester advances your ML expertise and culminates in the program practicum.
BADM 576: Data Science and Analytics (4 credits)
This is ML II. Building on FIN 550, you extend into advanced techniques: ensemble methods, unsupervised learning, natural language processing (including transformer models), time series analysis, and neural networks. The course also covers MLOps and LLMOps – the infrastructure for deploying and maintaining AI-powered analytics systems in production.
Portfolio artifact: Advanced ML project with deployment component.
Practicum (4 credits)
The practicum is the culminating experience of the MSBAi program and has two parts.
How courses are structured
Courses are designed for working professionals. Expect to spend 8-12 hours per week per course on coursework – lectures, case materials, project work, and weekly assignments – all available on your schedule. Each course also includes optional live sessions:
Weekly Project Studios (60 minutes): Live coding walkthroughs with your instructor, followed by collaborative project work in small groups.
Analytics Conversations (bi-weekly, 60 minutes): Guest speakers from industry, live case study debates, and discussions on emerging topics.
Office Hours (weekly): Drop-in sessions with instructors and teaching assistants for questions, debugging, and project feedback.
Sessions are optional – no attendance is required – but students who engage report stronger project outcomes and faster skill development.
Immersion opportunities
Expand your understanding of other cultures and the global economy by choosing to participate in a two-week immersion – either in-person or virtually. We partner with select universities and companies around the world to bring together a diverse group of learners, cultures, ideas, and solutions.
