NUR500 Pre-MSN Orientation
College of Nursing Nursing Department
Course Description: pre-orientation course
Credit Hours: 0.00
Prerequisites: None
Corequisites: None
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AIS402 Monitoring, Reliability and AI Operations
College of Technology Applied AI Department
Course Description: This course addresses the post-deployment operation of AI-enabled systems — the work of keeping them reliable, accurate, and trustworthy once they are live. Students will monitor system performance, run data quality checks, and evaluate AI outputs while learning to recognize model drift and respond to operational incidents. Emphasis is placed on logging, alerting, and structured incident response as the backbone of dependable AI operations. By the end of the course, students will be able to build operational playbooks, diagnose and remediate performance and quality issues, and establish monitoring practices that sustain reliability and continuous improvement — preparing them to keep AI systems accountable and effective in real-world production environments.
Credit Hours: 3.00
Prerequisites: All of: AIS401
Corequisites: None
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SDC235 Foundations of Versioning and Workflows
College of Technology Software Development and Coding Department
Course Description: This course introduces students to the core practices of version control and collaborative software development. Through hands-on activities, students learn how to track changes, manage code history, create and merge branches, and resolve conflicts in multi-developer environments. Emphasis is placed on best practices for team workflows, including contribution management and peer review processes. Students will also explore the use of generative AI tools to support version control tasks, such as interpreting errors, composing commit messages, and improving workflow efficiency.
Credit Hours: 3.00
Prerequisites: All of: SDC120, SDC120L
Corequisites: None
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AIS101L Foundations of AI-Enabled Systems Lab
College of Technology Applied AI Department
Course Description: This lab provides hands-on application of the concepts introduced in the AIS101 course. Students evaluate AI-enabled system scenarios to determine use-case fit, identify required system components, and assess ethical and organizational risks. Students document data flows, human checkpoints, and governance controls through system architecture diagrams and communicate their findings through professional stakeholder briefs that include evidence-based go/no-go recommendations. Through these applied activities, students develop practical skills for evaluating AI-enabled systems and communicating responsible implementation recommendations in organizational environments.
Credit Hours: 1.00
Prerequisites: None
Corequisites: All of: AIS101
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AIS101 Foundations of AI-Enabled Systems
College of Technology Applied AI Department
Course Description: This course introduces artificial intelligence as an integrated component of organizational systems rather than as a standalone technology. Students explore AI system architectures, data flows, system lifecycles, human-in-the-loop design, and principles of responsible AI, including ethical considerations and governance. With an emphasis on operational literacy rather than advanced mathematics or programming, students learn to evaluate AI use cases for appropriate fit and communicate AI capabilities, limitations, decisions, and recommendations to both technical and non-technical stakeholders. The course provides a practical foundation for understanding how AI systems are designed, deployed, managed, and maintained in organizational environments while preparing students for continued study in AIS coursework.
Credit Hours: 3.00
Prerequisites: None
Corequisites: None
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SDC332L DevOps and CI/CD Using AI Tools Lab
College of Technology Applied AI Department
Course Description: This lab emphasizes projects where students design and configure CI/CD pipelines for multi-stage software delivery. Projects require automating builds, tests, and deployments across realistic development environments. Students integrate monitoring, rollback strategies, and security checks into their pipelines to implement functional CI/CD workflows that demonstrate production-ready DevOps practices.
Credit Hours: 1.00
Prerequisites: All of: SDC300
Corequisites: All of: SDC332
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SDC332 DevOps and CI/CD Using AI Tools
College of Technology Applied AI Department
Course Description: This course introduces students to the principles and practices of DevOps with a focus on continuous integration and continuous delivery (CI/CD). Students learn to streamline the software development lifecycle through version control integration, automated builds, test execution, and staged deployment workflows. Emphasis is placed on configuring pipelines using declarative tools such as YAML, ensuring quality through test automation, and enabling fast, reliable feedback loops. Generative AI tools are used to assist in pipeline configuration, troubleshoot errors, and optimize workflow steps, while students critically evaluate the output for correctness and maintainability.
Credit Hours: 3.00
Prerequisites: All of: SDC300
Corequisites: None
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SDC130L Database Systems and SQL Lab
College of Technology Software Development and Coding Department
Course Description: This lab centers on projects where students design and implement relational databases to support realistic applications, such as e-commerce or student record systems. Students create schemas, populate data, and build SQL queries that support reporting and decision-making. Performance optimization and query validation will be integral to the creation of database-driven projects that demonstrate schema design, query proficiency, and data integrity.
Credit Hours: 1.00
Prerequisites: All of: SDC100, SDC100L
Corequisites: All of: SDC130
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SDC130 Database Systems and SQL
College of Technology Software Development and Coding Department
Course Description: This course introduces students to the design, implementation, and management of relational database systems as essential tools in modern software development. Students will learn to model structured data, design efficient and scalable schemas, and write SQL queries for data manipulation and retrieval. Topics include entity-relationship modeling, schema design, and the use of normalization techniques to ensure data integrity.
Credit Hours: 3.00
Prerequisites: All of: CYB120, CYB120L OR All of: SDC100, SDC100L
Corequisites: None
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AIS301 Data Pipelines and ETL/ELT Operations
College of Technology Applied AI Department
Course Description: This course explores the design, implementation, and operation of the data pipelines that power modern analytics and AI systems. Students will build and manage workflows using ETL and ELT patterns, and apply core practices in scheduling, dependency management, error handling, logging, and documentation. Emphasis is placed on constructing pipelines that are reliable, repeatable, and auditable — the standards expected in professional data engineering environments. By the end of the course, students will be able to design a functioning data pipeline, diagnose and resolve failures, and document workflows for maintainability and compliance, preparing them to support data-driven decision-making across analytics and machine learning applications.
Credit Hours: 3.00
Prerequisites: All of: CYB310
Corequisites: None
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