Courses across five institutions.

Continuation of the AI sequence — applied work with LLMs, RAG patterns, fine-tuning, agent design, and evaluation. Hands-on Python against current tooling.

Foundational Linux administration covering the shell, file system, permissions, package management, and scripting. Lab-driven.

General education foundation — computing concepts, productivity applications, internet literacy, and the role of computing in modern work.

Introductory Python — syntax, control flow, data structures, file I/O, and applied scripting. Lab-driven with current tooling.

Hands-on introduction to networking and security fundamentals — TCP/IP, OSI model, common protocols, threat surface, and defensive practices.

Operating system internals, device management, drivers, and the hardware/software interface that underpins IT support and infrastructure work.

Systematic diagnosis and remediation across data-center systems — compute, storage, network, and power. Hands-on lab work against real fault scenarios.

Designing data-center infrastructure end to end — rack layout, power and cooling, networking topology, storage architecture, and capacity planning.

Advanced Linux administration — services, scripting, security hardening, networking, and automation against real workloads.

Survey of information security — threats, vulnerabilities, controls, cryptography, network defense, and policy. Foundation for the Cybersecurity pathway.

Cloud-native architectures for analytics workloads on AWS, Azure, and Google Cloud — from data ingest to ML serving.

Architectural patterns, design principles, and trade-off analysis for software-intensive systems.

Graduate seminar on managing IT projects — scope, schedule, risk, vendor and stakeholder management for technical work. Case-driven, with real artifacts.

Co-taught honors seminar applying LLMs, RAG, and agentic AI to interdisciplinary student projects.

Critical examination of how AI-mediated environments shape attention, agency, and well-being.

Foundational survey of IT — hardware, operating systems, networking, and end-user support concepts. Entry course for the IT pathway.

Continuation of the AI sequence — applied work with LLMs, RAG patterns, fine-tuning, agent design, and evaluation. Hands-on Python against current tooling.

Intermediate-to-advanced Python — data structures, OOP, concurrency, packaging, and applied projects against real APIs and datasets.