Health, Fitness, and Wellness focuses on the importance and foundations of good health and physical fitness, particularly for children and adolescents. This course addresses health, nutrition, and fitness, and substance use and abuse.

Applied Probability and Statistics is designed to help students develop competence in the fundamental concepts of basic statistics including: introductory algebra and graphing; descriptive statistics; regression and correlation; and probability. Statistical data and probability are often used in everyday life, science, business, information technology, and educational settings to make informed decisions about the validity of studies and the effect of data on decisions. This course discusses what constitutes sound research design and how to appropriately model phenomena using statistical data. Additionally, the content covers simple probability calculations, based on events that occur in the business and IT industries. No prerequisites are required for this course.

Welcome to Composition: Successful Self-Expression! In this course, you will focus on four main topics: professional writing for a cross-cultural audience, narrowing research topics and questions, researching for content to support a topic, and referencing research sources. Each section includes learning opportunities through readings, videos, audio, and other relevant resources. Assessment activities with feedback also provide opportunities to check your learning, practice, and show how well you understand course content. Because the course is self-paced, you may move through the material as quickly or as slowly as you need to gain proficiency in the seven competencies that will be covered in the final assessment. If you have no prior knowledge or experience, you can expect to spend 30-40 hours on the course content. You will demonstrate competency through a performance assessment. There is no prerequisite for this course and there is no specific technical knowledge needed.

Discrete Mathematics II addresses abstract, discrete, computational methods used in computer science. In particular, this class introduces searching and sorting algorithms; big-O estimates; number theory and cryptography; recursion and induction; counting and advanced counting techniques; discrete probability; and modeling computation. This course emphasizes applications in computer science. Discrete Mathematics I is a prerequisite for this course.

Introduction to Systems Thinking and Applications provides learners with the skills required to engage in a holistic systems-based approach to analyzing complex problems and solutions. This course introduces the foundational concepts and principles of systems thinking and provides opportunities to use a systems thinking approach to analyze and evaluate real-world case studies. The course will culminate with using systems thinking to develop a solution to an authentic complex problem. This course has no prerequisites, but general education math (C955 or C957) is preferred. Because the course is self-paced, learners may move through the material as quickly or as slowly as needed, with the goal of demonstrating proficiency in the five competencies covered in the final assessment. If learners have no prior knowledge of this material, they can expect to spend 30 to 40 hours on the course content.

Welcome to Introduction to Communication: Connecting with Others! It may seem like common knowledge that communication skills are important, and that communicating with others is inescapable in our everyday lives. While this may appear simplistic, the study of communication is actually complex, dynamic, and multifaceted. Strong communication skills are invaluable to strengthening a multitude of aspects of life. Specifically, this course will focus on communication in the professional setting, and present material from multiple vantage points, including communicating with others in a variety of contexts, across situations, and with diverse populations. Upon completion, you will have a deeper understanding of both your own and others’ communication behaviors, and a toolbox of effective behaviors to enhance your experience in the workplace.

Ethical Engineering introduces learners to the ethical, professional, and societal responsibilities inherent in engineering practice. Learners examine the societal, economic, environmental, and global impacts of engineering decisions; interpret professional engineering codes of ethics; and apply ethical decision-making frameworks to real-world and historical engineering cases. Through case analysis and performance-based assessment, learners develop the judgment required to navigate ethical dilemmas in traditional and emerging engineering fields responsibly.

American Politics and the U.S. Constitution examines the evolution of representative government in the United States and the changing interpretations of the civil rights and civil liberties protected by the Constitution. This course will give candidates an understanding of the powers of the branches of the federal government, the continual tensions inherent in a federal system, the shifting relationship between state and federal governments, and the interactions between elected officials and the ever-changing electorate. This course will focus on such topics as the role of a free press in a democracy, the impact of changing demographics on American politics, and the debates over and expansion of civil rights. Upon completion of the course, candidates should be able to explain the basic functions of the federal government, describe the forces that shape American policy and politics, and be better prepared to participate in America’s civic institutions. This course has no prerequisite.

This course bridges foundational programming skills to advanced object-oriented concepts, empowering learners to design and implement robust, scalable applications. It emphasizes the core pillars of object-oriented programming such as inheritance, composition, encapsulation, abstraction, and polymorphism while introducing data structure fundamentals. Learners will define and extend class hierarchies, apply method overriding, work with polymorphic collections, and traverse data structures using iterative and recursive techniques. Through practical problem-solving, the course reinforces code reusability and equips learners to apply object-oriented principles for efficient, maintainable solutions.

Calculus I introduces the fundamental ideas of limits, derivatives, anti-derivatives, and basic differential equations as tools for modeling and solving real-world problems. Students explore how functions behave, how rates of change are measured and optimized, and how derivatives lead to applications in linear approximations and optimization. The basics of how accumulated quantities (antiderivatives) are computed using the Fundamental Theorem of Calculus are also introduced. Emphasis is placed on polynomial, exponential, logarithmic, and trigonometric functions, with applications drawn from science, engineering, economics, and technology. Students will focus on conceptual understanding, graphical interpretation, and applied problem-solving rather than formal proofs. By the end of the course, students will be able to analyze change, model systems, and apply calculus techniques to practical scenarios.

Applied Discrete Mathematics introduces the foundational mathematical structures that underpin modern technology. Topics include logic, Boolean algebra, sets, functions, relations, graphs, combinatorics, and modular arithmetic, with emphasis on their applications to technology concepts such as Boolean algebra and structures, password complexity, access control models, network topologies, and cryptography. Students will not focus on abstract proofs but will instead learn how discrete mathematics informs real-world technology structures and practices. By the end of the course, students will be able to apply mathematical reasoning to technology scenarios, analyze system vulnerabilities through discrete models, and explain the mathematical principles behind technological architectures and protocols.

Welcome to Web Development Foundations! In this course you will learn about web design and development using HTML and CSS. This course employs the zyBooks learning platform which contains all the necessary reading materials, knowledge checks, and hands-on activities to prepare you for the course assessment. For the best understanding of the course content, complete each chapter. There are no prerequisites. Competency will be demonstrated with a performance assessment.

This course advances learners’ understanding of calculus with a focus on integration techniques, sequences and series, differential equations, and alternative coordinate systems. Learners apply advanced integration and numerical approximation methods to model and solve engineering and computational problems, analyze infinite processes using sequences, series, and convergence tests, and represent dynamic systems through separable differential equations, parametric equations, and polar coordinates. Emphasizing real-world applications, the course highlights the role of calculus in optimization, numerical methods, and systematic problem-solving across engineering disciplines.

This course develops foundational linear algebra knowledge and applies it to engineering and computing problems. Learners use matrix methods to formulate and solve systems of linear equations, analyze vector spaces and linear transformations to represent and manipulate multidimensional data, and determine eigenvalues, eigenvectors, and matrix factorizations to evaluate system behavior and optimize solutions. Emphasis is placed on applying mathematical principles to model, analyze, and solve real world problems, including applications in data representation, optimization, and the mathematical foundations of machine learning. The course builds on prior coursework in calculus for engineering and programming.

This course enables learners to apply advanced mathematics, including calculus, linear algebra, probability, statistics, and optimization, to analyze and solve problems in artificial intelligence engineering. Learners use mathematical reasoning to model, analyze, and evaluate machine learning and deep learning systems, with emphasis on model training dynamics, generalization, stability, and performance. The course develops learners’ ability to analyze complex AI engineering problems by applying linear algebra techniques such as vector spaces and matrix factorizations, evaluate gradients, loss surfaces, and convergence behavior using multivariable calculus and optimization, and assess uncertainty and performance through probability, statistics, and information theory. Learners conduct computational experiments and data-driven analysis using Python, NumPy, and appropriate machine learning libraries to validate mathematical models and support engineering decisions. Throughout the course, learners demonstrate the ability to interpret results, draw justified conclusions, and communicate mathematical reasoning in the context of AI system design and evaluation.

Data Management Foundations offers an introduction in creating conceptual, logical and physical data models.  Students gain skills in creating databases and tables in SQL-enabled database management systems, as well as skills in normalizing databases. No prerequisites are required for this course.

Network and Security - Foundations introduces learners to the basic network systems and concepts related to networking technologies. Learners will gain skills in applying network security concepts for business continuity, data access, and confidentiality, and in identifying solutions for compliance with security guidance.

Version control is critical to maintaining software and enabling scalability solutions. A best practice for any programming project that requires multiple files uses version control. Version control enables teams to have collaborative workflows and enhances the software development lifecycle. This course introduces students to the basics of publishing, retrieving, branching, and cloning. There are no prerequisites for this course.

This course extends calculus into multiple dimensions and vector analysis, emphasizing mathematical tools essential for engineering applications. Learners apply vector operations in two and three dimensions and use multivariable differentiation techniques to model and solve optimization and engineering problems. The course also focuses on analyzing vector fields through line, surface, and volume integrals to model physical and computational systems. Applications include engineering systems, physics‑based modeling, and optimization problems relevant to artificial intelligence and related engineering fields.

This course is the first in a three-course C# sequence within the BSAIE program (AI Engineering with C#, Advanced C#, and C# .NET Backend Development). Building on students’ prior experience with Python and object-oriented programming, the course transitions learners to C# as a second programming language. Students design and develop functional C# applications that apply programming logic, incorporate user interface elements, manage basic data input and output, and integrate external libraries and services, including AI-enabled components. The course emphasizes applied software engineering practices, including code modularity, control flow, and the translation of object-oriented concepts from Python to C#. AI integration is introduced at a foundational, application level, focusing on incorporating existing AI services or libraries rather than developing or training AI models. This course is not beginner-focused and assumes prior coding experience. It prepares students for continued work in backend engineering with C# while also establishing foundational AI integration skills relevant to both the BSAIE and BSSWE programs.

This course is the third in a three-course C# sequence in the BSAIE program (AI Engineering with C#, Advanced C#, and C# .NET Backend Development) and focuses on the development of enterprise-ready backend services using ASP.NET. Learners extend their C# proficiency into professional software engineering by designing and implementing RESTful APIs that meet both functional and non-functional requirements, including scalability, security, and maintainability. The course emphasizes validation of service behavior through comprehensive automated unit testing that addresses typical usage scenarios and edge cases. Learners also configure .NET applications for deployment, incorporating logging, monitoring, and metrics to support operational observability and ongoing service reliability. Through applied backend engineering practices, learners build the skills necessary to deploy and operate production-grade services and prepare for integrating AI models into real-world systems that require scalable, secure, and observable backend infrastructure.

This course provides learners with a foundational understanding of artificial intelligence concepts and how they are applied through Microsoft Azure services. The course emphasizes AI workloads and responsible AI, machine learning, computer vision, natural language processing, and generative AI, providing a very fundamental introduction to the foundations of each and how to implement them through Azure services. This course is designed to prepare learners for Exam AI900 Azure AI Fundamentals Microsoft Certification.

This course is the second in a series of three C# courses in BSAIE (AI Engineering with C#>Advanced C#>C# .NET Backend Development). This course builds on prior exposure to programming logic and introductory C# applications, learners develop proficiency with advanced C# language features and core .NET libraries to build reliable, scalable, and maintainable software components. Learners apply asynchronous programming patterns, modern C# constructs, and standard libraries to design production ready components with clear boundaries and contracts that support integration scenarios such as packaging, serving, and consuming external services. The course emphasizes professional software engineering practices, including secure input handling, abuse resistant design, safe failure behaviors, and the implementation of a structured testing strategy with automated validation and regression considerations to support production readiness. These practices prepare learners for backend service design and deployment in the subsequent course.

This course provides a foundational orientation to the field of AI engineering, focusing on the essential interplay between programming, mathematics, and real-world applications. Learners apply core programming principles and foundational mathematical ideas that support algorithmic thinking and analyze how these foundations enable the design and development of AI systems. Students also describe how AI engineering differs from related domains, such as data science and academic research, with an emphasis on applied engineering practice. Through conceptual frameworks, applied examples, and introductory analysis, students begin to understand the skills, mindset, and responsibilities of AI engineers, including considerations related to scalability, reliability, and responsible use of AI technologies. The course establishes a strong foundation for success in the AI Engineering program by clarifying expectations for rigor and by highlighting the relevance of foundational competencies for building trustworthy, impactful, and real-world AI solutions.

This course examines computer systems and architectures through the lens of AI engineering, emphasizing how hardware and software decisions shape the feasibility, performance, cost, and reliability of modern AI workloads. Learners analyze the roles of CPUs, GPUs, TPUs, memory hierarchies, storage, interconnects, and distributed systems in enabling efficient training and inference. The course emphasizes architectural tradeoffs, profiling and bottleneck diagnosis, and resource management strategies that influence latency, throughput, scalability, and operational constraints. Through applied analysis and system design activities, learners translate functional and non-functional requirements into concrete compute, memory, bandwidth, and power constraints across cloud, embedded, and edge environments. Learners evaluate failure modes and safe degradation behaviors and apply infrastructure foundations, including standardized runtime environments and repeatable configuration concepts, to support defensible deployment decisions. By the end of the course, learners can design and justify system architectures aligned to real-world AI production constraints and organizational objectives. pipelines, apply word embeddings, and use sequence-based and transformer-based models to complete tasks such as classification, sentiment analysis, and question answering. The course emphasizes rigorous evaluation beyond accuracy, using task-appropriate metrics, structured error analysis, and stress testing under realistic input variation, to identify failure modes and reliability risks. Learners also assess bias, safety, and responsible-use concerns (including generative AI risks when applicable) and produce governance artifacts that document intended use, limitations, evaluation results, and monitoring expectations. Through applied engineering work, learners demonstrate judgment in selecting methods and designing solutions that meet technical, ethical, and operational constraints.

This course provides a foundational orientation to the field of AI engineering, focusing on the essential interplay between programming, mathematics, and real-world applications. Learners apply core programming principles and foundational mathematical ideas that support algorithmic thinking and analyze how these foundations enable the design and development of AI systems. Students also describe how AI engineering differs from related domains, such as data science and academic research, with an emphasis on applied engineering practice. Through conceptual frameworks, applied examples, and introductory analysis, students begin to understand the skills, mindset, and responsibilities of AI engineers, including considerations related to scalability, reliability, and responsible use of AI technologies. The course establishes a strong foundation for success in the AI Engineering program by clarifying expectations for rigor and by highlighting the relevance of foundational competencies for building trustworthy, impactful, and real-world AI solutions.

This course develops students’ ability to apply natural language processing (NLP) techniques to solve engineering problems involving human language data. Learners implement reproducible text preprocessing and tokenization pipelines, apply word embeddings, and use sequence-based and transformer-based models to complete tasks such as classification, sentiment analysis, and question answering. The course emphasizes rigorous evaluation beyond accuracy, using task-appropriate metrics, structured error analysis, and stress testing under realistic input variation, to identify failure modes and reliability risks. Learners also assess bias, safety, and responsible-use concerns (including generative AI risks when applicable) and produce governance artifacts that document intended use, limitations, evaluation results, and monitoring expectations. Through applied engineering work, learners demonstrate judgment in selecting methods and designing solutions that meet technical, ethical, and operational constraints.

This course prepares learners to design, implement, and deploy computer vision solutions in engineering contexts. Learners identify techniques for data selection, processing, structure, and generation for training computer vision models; apply CNN-based models for detection, classification, and segmentation; and recommend appropriate tools and deployment strategies for vision models given a business problem. The course builds on machine learning and deep learning foundations, narrowing the focus to vision-specific tasks with hands-on model development, optimization, and deployment.

This capstone course requires students to integrate and apply engineering knowledge from mathematics, programming, data engineering, machine learning, deep learning, and software engineering to design, validate, and deploy an AI‑enabled system that addresses a real‑world problem. Learners work through the full AI system lifecycle, including problem formulation, model development, testing, deployment, and monitoring. The course emphasizes ethical and responsible AI practices, systematic evaluation using appropriate metrics, and production deployment using industry‑standard MLOps tools such as cloud infrastructure, containerization, CI/CD pipelines, and monitoring frameworks. Through this culminating experience, learners demonstrate the ability to design and deploy AI solutions that meet technical, ethical, and operational constraints expected of entry‑level AI engineers.

Data Structures and Algorithms I covers the fundamentals of dynamic data structures, such as bags, lists, stacks, queues, trees, and hash tables with their associated algorithms. This course discusses object-oriented design and abstract data types as design paradigms. The course emphasizes problem-solving and techniques for designing efficient, maintainable software applications. Students will implement simple applications using the techniques learned.

Data Structures and Algorithms II explores the analysis and implementation of high-performance data structures and supporting algorithms, including graphs, hashing, self-adjusting data structures, set representations, and dynamic programming. The course also introduces students to NP-complete problems. The course discusses how to use Python techniques to implement software solutions for problems of memory management and data compression. This course has two prerequisites: Data Structures and Algorithms I and Discrete Math II.

Computer Architecture introduces students to concepts and characteristics of organization and architecture applied to modern computer systems, including performance, processor, memory, I/O, and multiprocessors to optimize system design, performance, and efficiency.

Big Data Foundations provides an in-depth introduction to big data concepts, terminology, and applications. You will learn the risks and challenges of working with extremely large data sets. The course introduces tools and techniques for working with big data. The course covers selection criteria for relational and non-relational data architectures and cloud-native data storage concepts. It also provides a historical perspective on the evolution of big data storage approaches. Data warehousing, data lakes, and data lakehouses are introduced, and design principles for each are explained. Learners design aspects of big data architecture and big data processing to address given business requirements.

Machine Learning presents the end-to-end process of investigating data through a machine learning lens. Topics covered include: supervised and unsupervised learning algorithms, features that best represent data, commonly-used machine learning algorithms, and methods for evaluating the performance of machine learning algorithms. 

Machine Learning DevOps focuses on the software engineering fundamentals needed to successfully streamline the deployment of data and machine learning models in a production-level environment. Students will build the DevOps skills required to automate the various aspects and stages of machine learning model building and monitoring over time.

This course lays the foundation for understanding terminology, principles, processes, and best practices of information security at local and global levels. It further provides an overview of basic security vulnerabilities and countermeasures for protecting information assets through planning and administrative controls within an organization. This course has no prerequisites.

Data and Information Governance provides learners with the knowledge that establishing rules of engagement, policies, procedures, and data stewardship is essential to exercising organizational control over—and extracting maximum value from—its data assets. Good data governance helps an organization lower costs, create efficiencies, and achieve its strategic goals and objectives. Data governance provides a framework for properly managing information across the entire data lifecycle and establishes strategies in support of disaster recovery and continuity of operations. This course will prepare IT professionals to assist their organization in the definition and implementation of best practices related to the planning and implementation of managed systems that meet business, technical, security, auditing, disaster recovery, and business continuity requirements.

General Chemistry I introduces foundational principles of chemistry, starting at the atomic level and expanding to the behavior of elements within the periodic table. This course explores how atoms bond to form molecules and proceeds into chemical reactions, acids and bases, solutions, and nuclear reactions. Students will gain a comprehensive understanding of stability and change in chemical processes. This course highlights the practical aspects of chemistry, providing insights into how chemical principles underpin everyday phenomena and contribute to our understanding of environmental processes.

General Chemistry I Lab focuses on developing foundational skills in scientific investigation in chemistry. It emphasizes the application of the scientific method to answer chemistry questions through hypothesis-driven experimentation. Students will learn to design, execute, and analyze chemistry experiments, ensuring adherence to rigorous scientific protocols and ethical standards. The course also covers essential aspects of scientific communication, including writing clear and structured scientific reports and effectively presenting experimental findings. Throughout the course, students will cultivate critical thinking skills necessary for interpreting data and drawing conclusions.

Software Engineering introduces the concepts of software engineering to students who have completed the core courses in programming and project management. The principles build on previously acquired concepts, switching the emphasis from programming simple routines to engineering robust and scalable software solutions. This course does not cover programming, but it provides an overview of software engineering processes and their challenging nature, focusing on the need for a disciplined approach to software engineering. A generic process framework provides the groundwork for formal process models. Prescriptive process models such as the Waterfall Model and Agile Development are included. This course also introduces the elements and phases of software engineering, including requirements engineering, design concepts, and software quality. There are no prerequisites for this course.