• Module 0: Orientation. A short onboarding module that prepares students for the online learning environment, program structure, expectations, and support resources.
• Module A: Software Engineering & Data Science Bootcamp. A hands-on ramp-up covering professional development tools—Git, IDEs, debugging, testing, build systems, and cloud workflows—so students enter the core curriculum with a shared technical foundation. It also covers Python programming fundamentals for data science, including built-in types, NumPy, Pandas, Matplotlib, and introductory machine learning with scikit-learn.

• Module 1: Software Engineering Fundamentals. Foundational principles of modern software engineering: disciplined development practices, automated testing, CI/CD, debugging and observability, cloud deployment, and secure coding. Culminates in a mini-capstone integrating all skills.
• Module 2: Python Programming Toolkit. Python programming for data-driven work, spanning core data structures, NumPy, Pandas, Matplotlib, and introductory model building with scikit-learn. Students apply these tools in a hands-on final project.
• Module B: Data & Algorithms for Scalable Systems (Part 1). First half of the module applying algorithmic thinking to large datasets. Begins with a strong SQL foundation: retrieval, aggregation, joins, window functions, before extending to data structures, graph algorithms, and query optimization for performance at scale.

• Module 3: Machine Learning Fundamentals. Practical introduction to the full ML workflow: exploratory data analysis, regression, classification, decision trees, ensemble methods, neural networks, deep learning, transformers, and LLMs—with a focus on interpretability and responsible deployment.
• Module 4: Software Engineering at Scale. Explores architectural trade-offs between monolithic and microservice systems, API design, concurrency, distributed observability, cloud deployment strategies, and security in distributed environments.
• Module B: Data & Algorithms for Scalable Systems (Part 2). Second half of the module applying algorithmic thinking to large datasets. Begins with a strong SQL foundation: retrieval, aggregation, joins, window functions, before extending to data structures, graph algorithms, and query optimization for performance at scale.

• Module 5: AI/LLM-Aided Software Development. How AI and LLMs are transforming engineering practice. Students use tools like Copilot and Claude Code, evaluate AI-generated code for correctness and security, build AI agents for developer workflows, and assess long-term maintainability of AI-assisted codebases.
• Module 6: Human-Centric AI UX. Design and evaluation of human-AI interfaces: usability, trust, explainability, conversational and multimodal interaction, accessibility, human-in-the-loop workflows, and ethical case studies.
• Module C: Capstone Project: End-to-End AI Application at Scale (Part 1). Year-long team project designing and deploying a production-grade AI-enabled application. Part 1 covers proposal, design, scaffolding, and initial implementation milestones.

• Module 7: Data Design and Distribution at Scale; AI/ML Ops. Deep dive into scalable data architecture: NoSQL models, ETL and streaming pipelines, partitioning and replication strategies, CAP theorem, data lineage, ML Ops practices, and privacy-preserving data access.
• Module 8: Responsible and Ethical Data Science and AI. Ethical frameworks applied to data-driven systems: data fraud, privacy, algorithmic bias, surveillance, intellectual property, regulatory frameworks, and the role of generative AI in misinformation.
• Module C: Capstone Project: End-to-End AI Application at Scale (Part 2). Continuation of the capstone. Teams polish functionality, expand test coverage, address edge cases and scalability, incorporate faculty feedback, and deliver a final demo.

The software engineering master’s for AI is 30 credits (10 modules) delivered 100% online and designed for working professionals. The format combines asynchronous coursework with scheduled weekly live sessions and is designed to be completed in approximately two years, in addition to the required onboarding modules completed before Year 1.

Applicants typically need a bachelor’s degree in a related field such as Computer Science, Software Engineering, Data Science, or Engineering—or equivalent professional experience. Before core coursework begins, students complete the structured bootcamp module that ensures foundational skills in programming, tools, and data fundamentals.

Students study software engineering end-to-end, including fundamentals and at-scale architecture, Python for data science, machine learning and deep learning, AI/LLM-aided development, data design and distribution, ML Ops, responsible and ethical AI, human-centric AI interaction, and a year-long capstone integrating applied work across all modules.

The capstone for the Online master’s degree in software engineering for AI program spans both semesters of Year 2. Student teams design and deploy a production-grade, end-to-end AI-enabled application at scale—integrating scalable architecture, data pipelines, ML models, human-centered interfaces, and responsible AI practices. The project includes design proposals, implementation checkpoints, faculty feedback, and a final demo.

To apply to the online Master’s in Software Engineering for AI program, submit a resume, personal statement, and unofficial transcripts. GRE/GMAT scores and professional references are optional. There are multiple deadlines throughout the year.