{"id":19294,"date":"2026-01-20T16:24:54","date_gmt":"2026-01-20T21:24:54","guid":{"rendered":"https:\/\/www.bu.edu\/cds-faculty\/?page_id=19294"},"modified":"2026-05-06T11:53:04","modified_gmt":"2026-05-06T15:53:04","slug":"curriculum","status":"publish","type":"page","link":"https:\/\/www.bu.edu\/cds-faculty\/programs-admissions\/masters-in-enterprise-ai-online\/curriculum\/","title":{"rendered":"Curriculum"},"content":{"rendered":"

Request Information<\/a><\/span><\/h3>\n

Our Curriculum<\/h2>\n

Boston University\u2019s online MS in Enterprise AI is a 30-credit program designed to help working professionals build, deploy, and govern AI systems in enterprise environments.<\/p>\n

The program consists of 12 courses (30 credits total) delivered fully online and designed to be completed in four sequential semesters, plus two required 0-credit onboarding modules completed prior to the first semester. The format combines asynchronous coursework with scheduled live sessions.<\/p>\n

Across the curriculum, students build practical skills in machine learning foundations, data engineering for AI, LLM application development (prompting, RAG, and fine-tuning), MLOps, enterprise agents, AI governance, and AI strategy. A program-long \u201cEnterprise AI for Leaders\u201d sequence runs through all four semesters and guides students from problem selection to prototype and final presentation.<\/p>\n

Program Overview<\/h2>\n

Pre-Program (2 weeks) \u2014 EA 500 Program Orientation (0 credits)<\/h3>
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A short, required onboarding module that prepares students for the online learning environment, program structure, expectations, and support model. Students complete this module before Semester 1.<\/p>\n

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Pre-Program (2 weeks) \u2014 EA 501 Enterprise AI Bootcamp (0 credits)<\/h3>
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A required bootcamp that helps students ramp up on the technical foundations and tooling used across the program so they are ready to start coursework on day one.<\/p>\n

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Semester 1<\/h3>
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EA 810 Enterprise AI for Leaders 1<\/h4>\n

Launches the program-long applied sequence. Students begin scoping an enterprise AI problem, aligning on stakeholders, constraints, and success measures.<\/p>\n

DX 602 Programming Toolkit for Data Science<\/h4>\n

Builds proficiency in Python and essential tooling for data and AI work. Topics include environment setup, Git, and core Python data workflows (data structures, control flow, functions, and working with common packages used in data science).<\/p>\n

DX 603 Machine Learning Fundamentals<\/h4>\n

Introduces core machine learning concepts and methods used in enterprise settings and builds a foundation for later LLM and production AI coursework.<\/p>\n