MSBAi Program Requirements

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.

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.

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.

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.

The MSBAi Practicum is a culminating experience. Learners curate a professional portfolio of polished projects from across the program and complete an applied analytics project that integrates skills from multiple competency areas.

This course pairs two quantum frameworks for working alongside AI, each running four weeks. In the first half, you use the mathematics of quantum probability - no physics or quantum hardware required - to model why human judgement systematically departs from the classical rationality of AI systems, learning to diagnose order effects, framing effects, and conjunction fallacies, then applying an audit methodology to AI-assisted decisions in your own organization. In the second half, you learn quantum computing from the ground up - core concepts, the underlying math, and the algorithms that solve complex business problems like optimization and route planning - building and testing hands-on solutions every week. AI tools accelerate exploration throughout. 

Portfolio artifact: An executive-ready decision audit and a quantum optimization solution.

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.

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.

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.