DSAI-2110 Data Management for AI–Fa 3 hours Introduces students to the essential principles of data management in the context of artificial intelligence applications. Topics include data acquisition, cleaning, transformation, integration, and bias evaluation, with a focus on ethical data stewardship and responsible AI practices. Students will develop hands-on skills in data preparation for AI workflows, including prompt engineering, chatbot customization, and AI-driven data processing. Practical experience will be gained using modern data management tools. This course emphasizes critical thinking in data management and prepares students to apply these concepts in real-world AI environments. (Fee: $50) DSAI-3110 3 hours Foundations of Data Science and Machine Learning–Sp This courses introduces the principles of data science and surveys machine learning techniques. Literate programming using either R or Python will be introduced to train machine learning models, graph multidimensional data, and generate reports. Statistics and linear algebra concepts will be covered including probability distributions, correlation, matrix algebra, and eigenvectors/eigenvalues. Means of handling incomplete data will be discussed as well as data reduction techniques, such as principal component analysis, to improve the performance of trained models. Machine learning techniques surveyed include: k-nearest neighbors, naive Bayes, decision trees, rule-learning algorithms, regression methods, neural nets, and association rules. Focus is placed on properly applying these methods and testing the performance of developed models. Prerequisite: Previous programming course or experience with programming. Prerequisite/Corequisite: Statistics course satisfied by one of the following: BUS-2150 Statistics in Business, GMTH-2110 Statistics for the Natural Sciences, MATH-2520 Discrete Math and Probability Principles for Computer Science, MATH-3110 Probability and Statistics, MATH-3120 Theory of Probability. (Fee: $50) DSAI-3510 Neural Networks and Deep Learning–Fa 3 hours This course builds on foundations of machine learning while focusing on aspects of deep learning. Topics include supervised learning and reinforcement learning. Applications will cover signal processing, image classification, natural language processing, and robotics. Prerequisite: DSAI-3110 Foundations of Data Science and Machine Learning. (Fee: $50) DSAI-4880 1–3 hours Topics in Data Science and Artificial Intelligence–Fa,Sp Study of topics of interest related to data science, machine learning, and artificial intelligence. Possible topics include bioinformatics, adaptive agents, and generative AI. Prerequisite: DSAI-3110 Foundations of Data Science and Machine Learning. Repeatable (must cover different topics if repeated). (Fee: $50) DSAI-4900 1–3 hours Independent Study in Data Science and Artificial Intelligence–Fa,Sp Independent research in various branches of data science and artificial intelligence. Submission and approval of a research proposal must precede registration. Prerequisite: DSAI-3110 Foundations of Data Science and Machine Learning or permission of the instructor. Repeatable (must cover different topics if repeated). CY-3420 Cyber Defense–Fa 3 hours This course employs extensive hands-on labs in sandboxed environments to develop technical cybersecurity skills. Students will operate in a Linux command-line environment, read and write code, and navigate TPC/IP networks. Skills covered include operating system, software, web, and network security attacks and defenses; symmetric and public key cryptography; hashing; and public key infrastructure and certificates. Prerequisites: CY-1000 Introduction to Cybersecurity; CS-1220 Object Oriented Design Using C++. (Fee: $50) CY-4310 Cyber Operations–Sp 3 hours This course covers cyber operations and the best practices for securing a technology infrastructure. Topics include offensive cyber operations, cyber-related legal precedents and regulations, wired and wireless network security, intrusion detection and prevention systems, system hardening, and defense in-depth. This is a hands-on course with a heavy emphasis on virtual machinebased lab exercises. Prerequisites: CY-3420 Cyber Defense; EGCP-4310 Computer Networks. (Fee: $50) CY-4330 Software Security–Sp 3 hours A detailed look at issues involved in providing secure software systems. Students will study principles and practices of software development that result in software that is robust and secure from attack. Students will learn techniques for analyzing software to determine whether it contains weaknesses that are vulnerable to exploitation. Students will also explore reverse engineering of software to understand the design of an existing software component to determine its security and whether it could contain malware. Prerequisites: CY-3320 Linux Systems Programming; CY-3420 Cyber Defense. (Fee: $50) CY-4810 Secure Software Engineering I–Fa 3 hours The capstone experience for Cyber Operations majors. Introduction to secure software engineering principles focusing on requirement development, detailed design, risk analysis, project scheduling and management, quality assurance, and testing. Student teams meet regularly to develop a project management plan, a requirements document, and a detailed design. They begin implementation of their project that will be completed in CY-4820 Secure Software Engineering II. Prerequisite: CY-3420 Cyber Defense. Corequisite: CS-3410 Algorithms. Crosslisted with CS4810 Software Engineering I. (Fee: $50) CY-4820 Secure Software Engineering II–Sp 4 hours Continuation of CY-4810 Secure Software Engineering I. Student teams will complete the implementation, testing, and release of their capstone project, submit regular progress reports, prepare a final report, and make a formal project presentation. Prerequisite: CY-4810 Secure Software Engineering I. Crosslisted with CS-4820 Software Engineering II. (Fee: $50) Data Science/Artificial Intelligence (DSAI) DSAI-1000 Fundamentals of Generative AI–Fa, Sp 2 hours This course explores the foundational concepts and applications of generative AI, highlighting ethical, societal, and spiritual implications through a Christian worldview. Learners will practice using AI tools for real-world solutions while integrating faith and technical expertise. Focusing on content creation, data analysis, and decision-making, the course prepares students for leadership roles that prioritize Christ, ethics, love, justice, and human dignity. Credit/no credit. 2026–27 Undergraduate Academic Catalog Page 249 Course Descriptions CY-3420 – DSAI-4900
RkJQdWJsaXNoZXIy MTM4ODY=