Online Associate of Applied Science in Business

This foundational course introduces the core concepts of programming, data structuring, and automation that underpin informed business decision-making — built for people who need to work with data, not necessarily become engineers. Learners start with the fundamental logic and syntax shared across common programming languages, then build practical proficiency in the two tools that do the most work in a modern business context: SQL for retrieving and filtering data from relational databases, and Python for cleaning, manipulating, and analyzing it. Along the way, they learn to differentiate the data structures and types used to store and analyze business information, so the skills connect to real decisions rather than abstract exercises.

From there, the course extends from writing code to working smarter with it. Learners identify the business processes and workflows best suited for optimization through low-code automation, and apply fundamental security and ethical practices for handling data and writing code responsibly — a non-negotiable in any environment where data carries real weight. The emphasis throughout is hands-on and applied: every concept lands as a query written, a script run, or a process mapped. By the end, learners have the working fluency to retrieve their own data, clean it, analyze it, and automate the repetitive parts — the practical foundation an AI Translator builds everything else on.

This course grounds innovation where it belongs — in real human needs and ethical creativity. Fulfilling the program's Humanities requirement, it explores the aesthetic and philosophical dimensions of design and invention, then puts them to practical use through the Design Thinking methodology. Learners work through the full arc — empathy, problem definition, ideation, prototyping, and testing — to develop solutions that are both technically feasible and genuinely centered on the people who'll use them. They begin with empathy and user research to frame authentic human-centered problems, then examine the aesthetic and functional aspects of design that shape how people interact with a product and experience it.

From there, the course builds the creative and critical muscles that separate real innovation from guesswork. Learners use divergent and convergent thinking to generate and sharpen ideas, develop low-fidelity prototypes and test plans to validate feasibility and user acceptance, and evaluate the ethical and societal implications of design choices across diverse user populations — because in a technology-driven world, who a solution serves and who it overlooks is a design decision in itself. The course closes on communication: presenting insights, validated prototypes, and the reasoning behind them to stakeholders. By the end, learners have a repeatable framework for developing products and strategies that hold up to both human and ethical scrutiny.

This course grounds the study of intelligence in core physical science principles — because every AI model, no matter how abstract it feels, runs on real hardware bound by real physical laws. Learners explore the relationship between the physical limits of computation, the energy demands of processing, and the biological systems that inspired modern machine learning in the first place. They start with the thermodynamics and hard physical limits of computation — how fundamental laws cap processing speed and what that means for system design — then examine how biological neural structures and brain functions became the architectural blueprint for artificial neural networks and deep learning. The result is a scientific lens on AI that most business-focused programs skip entirely.

From there, the course connects physical science to the decisions shaping AI's future. Learners evaluate the energy consumption, heat generation, and sustainability challenges of deploying large-scale models — an increasingly business-critical concern — and survey emerging physical technologies like quantum computing that stand to redefine what's computationally possible. They relate physical system science to the performance and scalability of interconnected intelligent devices (IoT), and close by weighing the physical, safety, and ethical consequences of increasingly autonomous systems operating in the real world. By the end, learners understand not just what AI can do, but the physical realities, costs, and limits that govern what it can do responsibly — context that sharpens every downstream design and strategy decision.

This course is the launchpad for an undergraduate degree built for the AI-shaped economy. It gives learners a strategic overview of the artificial intelligence landscape — defining the core terminology, tracing how the field evolved, and establishing why hybrid skillsets carry real business value. Rather than treating AI as a purely technical subject, the course centers the role of the AI Translator: the person who can sit in a strategy meeting and a Python notebook in the same afternoon, and who knows the difference between a technical problem domain and a strategic one. Learners build the vocabulary and judgment to frame an AI problem correctly before a single line of code is written.

Beyond the landscape, the course tackles the questions that decide whether an AI deployment succeeds or backfires. Learners examine the fundamental ethical dilemmas and governance frameworks that shape data usage and responsible deployment, developing the kind of decision-making employers expect from anyone directing AI work. The course also includes the essential scaffolding for academic success — strategies for managing time, resources, and self-directed learning across the program — and closes with a personal career roadmap that aligns individual competencies with target roles in the applied AI field. By the end, learners leave with both the foundation and the direction to move through the rest of the degree with intent.

This course provides a conceptual and applied understanding of the cloud computing services, architecture, and deployment models that power modern business and AI solutions. Learners start with the building blocks — core cloud architecture and the difference between IaaS, PaaS, and SaaS — then move quickly into the decisions that matter: how to read the non-functional requirements behind a business need, and how to evaluate a cloud solution against security, privacy, compliance, and data governance realities. Rather than memorizing one vendor's product menu, learners develop transferable judgment for assessing cloud-based options on their merits, building the kind of strategic literacy that holds up regardless of which platform an employer runs on.

From there, the course turns hands-on and outcome-focused. Learners practice accessing and applying cloud-based cognitive services and pre-trained AI models to solve straightforward problems, then learn to formulate a basic business case — cost justification included — for migrating a legacy application or service to the cloud. The course closes on the skill that often separates a technical contributor from a strategic one: communicating the technical and business benefits of cloud adoption clearly to management and stakeholders. By the end, learners can connect cloud capability to business value and defend that connection in front of the people who sign off on it.

This course teaches the conceptual and architectural thinking required to design and integrate complex digital solutions for modern business problems. Learners begin where every sound system starts — with the problem itself — mastering techniques for framing business challenges and gathering requirements before reaching for a solution. From there, the course moves into the core craft of architecture: selecting appropriate infrastructure across cloud, API, and platform options, choosing data architectures that fit the need, and designing conceptual solutions that pull multiple services, data sources, and external platforms into one cohesive functional model. Throughout, the emphasis is on bridging strategic business goals with technical feasibility, so design decisions are defensible on both fronts.

The second half turns architecture into something actionable and communicable. Learners evaluate architectural patterns, tools, and platforms against non-functional requirements such as cost, security, and scalability, then model the flow of data and logic using standardized documentation and diagramming techniques. They practice translating a conceptual design into technical requirements and user stories that an implementation team can actually build from, and close on the skill that defines a strong solutions architect: communicating the rationale and risk assessment behind a proposed architecture to technical and executive stakeholders alike. By the end, learners can carry a digital solution from a vague business problem all the way to a clear, justified design — and bring decision-makers along with them.

This course is a practical guide to rapidly building and deploying functional digital applications using low-code tools. It picks up where architectural design leaves off, assuming that foundation and shifting the focus entirely to hands-on implementation. Learners set up their platform and start building immediately — designing usable, accessible user interfaces and experiences, implementing core application features, and constructing the business logic and multi-step workflows that turn a static screen into a working tool. The emphasis throughout is on doing the work: every concept lands as something the learner builds, not something they read about.

From there, the course covers what real-world delivery actually demands. Learners execute API calls and integration routines to connect their applications to external data sources and services, then test functionality against defined business requirements and quality assurance standards. They develop the troubleshooting instincts that separate a finished build from a fragile one — diagnosing API connection errors, data flow problems, and runtime performance issues — before deploying a finalized application and generating the documentation needed for user enablement and ongoing maintenance. By the end, learners have moved a complete application from setup to deployment, with the practical fluency to do it again on the job.