Online Bachelor of Science in AI for 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.

This course is a practical guide to transforming complex data analysis into clear, actionable business communication. It moves beyond generating charts to the harder, higher-value skill: data storytelling — building a compelling narrative arc around analytical insights so they actually change what an organization does. Learners start with the questions most analysts skip, defining the audience, the business context, and the objective of a data-driven narrative before a single visualization is built. From there, they evaluate visualization types and tools against the structure of the data and the specific communication goal, and learn to design visuals that represent complex insights accurately and effectively for non-technical stakeholders — clarity and integrity treated as equal priorities.

The second half is where analysis becomes influence. Learners formulate narrative structures that frame their findings and lead to a clear, actionable conclusion, and develop a critical eye for the ways visualizations can mislead — whether by accident or design — so they can defend the integrity of their own work and challenge weak data when they see it. The course closes on delivery: communicating persuasive, data-driven recommendations through both oral and written presentations. By the end, learners can carry an insight from raw analysis all the way to a decision, with the storytelling skill to make sure it lands.

This course provides a comprehensive and strategic perspective on the Artificial Intelligence (AI) landscape, tailored specifically for managers seeking to drive business transformation. Learners will explore the diverse facets of AI technologies, ranging from foundational pattern analysis and predictive analytics to advanced neural networks and emerging large language models (LLMs). The course emphasizes the transition from technical understanding to tangible business value, guiding students in formulating robust strategies for adoption, initiative prioritization, and resource allocation.

A critical component of the curriculum is the focus on responsible innovation; students will learn to design governance frameworks to address the ethical implications and regulatory considerations inherent in AI development and deployment. Through the evaluation of strategic applications across the value chain, learners will develop the essential skills to lead cross-functional initiatives, communicate complex technical concepts to diverse stakeholders, and manage the successful implementation of AI solutions to ensure measurable organizational impact.

This course provides a comprehensive framework for product management in the era of Artificial Intelligence and large-scale Digital Transformation. Learners will bridge the gap between foundational product principles and complex, technology-driven execution, adapting Agile and Lean methodologies specifically for AI-powered development. The curriculum emphasizes a user-centric approach, guiding students through Design Thinking to create transformative solutions and utilizing low-code/no-code platforms for rapid prototyping and validation.

Beyond development mechanics, the course focuses on the strategic application of AI for decision-making. Students will learn to formulate data-driven strategies using AI-powered analytics and construct outcome-based roadmaps. Key modules also address the leadership challenges of managing product teams through organizational change and navigating the ethical implications of data and AI, ensuring learners are prepared to manage the entire product lifecycle responsibly and effectively.

This course offers a comprehensive strategic framework for leading operational transformation through the power of Artificial Intelligence. Learners will dive deep into how AI reshapes the entire operational landscape, from customer-facing functions and product management to core business processes and administrative support. The curriculum emphasizes the critical alignment of people, processes, and technology, guiding students in designing AI-augmented workflows, implementing intelligent automation, and leveraging low-code/no-code solutions to drive efficiency.

Beyond the technology, the course focuses on the managerial imperatives of transformation. Students will explore how to integrate AI with enabling technologies like cloud computing and IoT, while developing robust data strategies to support these initiatives. Key modules cover the essential skills of change management, helping leaders navigate the cultural shifts required for AI adoption. Learners will conclude by constructing agile transformation roadmaps and building data-driven business cases that quantify value, mitigate risk, and ensure ethical standards in AI-driven automation are met.

This course provides a data-driven examination of global logistics, focusing on how AI, predictive analytics, and automation optimize transportation networks and strengthen supply chain resilience. Learners begin with the networks themselves — applying predictive models and optimization techniques to global transportation and supply chains — then move into the operational core: using AI and automation to sharpen warehouse management, facility location, and route efficiency. From there, they learn to read modern supply chains the way a resilience-minded operator does, analyzing key risks and vulnerabilities through data-driven metrics and forecasting so disruption can be anticipated rather than absorbed.

The course then turns to the territory traditional logistics training tends to overlook. Learners apply best practices in reverse logistics and waste management to optimize asset recovery and product circularity, and evaluate the data architectures and digital systems that make supply chains visible, traceable, and governed ethically. It closes by zooming out: integrating data-informed logistics strategy and technological solutions into the organization's broader international strategic direction. By the end, learners can connect AI-driven logistics decisions to organizational outcomes — and defend the trade-offs behind them.

This course explores the strategic digital transformation of modern supply chains, focusing on how automation and AI technologies get planned, implemented, and governed across core functions. Learners start with the "why" — framing agility and resilience as the real value drivers behind transformation, not technology for its own sake — then move into the functional core. They apply predictive analytics and machine learning to demand planning, inventory, and procurement, and deploy Robotic Process Automation (RPA) and intelligent automation to optimize the repetitive, high-volume work in procurement, billing, and order management. Throughout, the focus stays on outcomes: which technologies earn their place, and where.

From there, the course builds the strategic and analytical judgment that separates an adopter from a leader. Learners evaluate the role of blockchain and other digital ledger technologies in strengthening visibility, traceability, and governance, then learn to analyze the cost reduction and return on investment behind automation initiatives — making the financial case as rigorously as the technical one. The course culminates in a comprehensive digital transformation roadmap for an end-to-end supply chain function, with every technology choice justified on value. By the end, learners can plan and defend a real transformation strategy, not just describe the tools.

This course provides a deep dive into the modern financial services landscape, focusing on FinTech innovation, digital assets, and the strategic application of AI and machine learning. Learners begin by analyzing the structural changes FinTech platforms have triggered in traditional financial services — the new competitive dynamics, the shifting power, the gaps. From there, they get specific about AI itself: evaluating both the efficacy and the real risks of machine learning models in lending, credit scoring, and algorithmic trading, where a model's blind spots carry direct financial and ethical consequences. The result is a clear-eyed view of AI in finance that holds the upside and the danger in the same frame.

The course then broadens into the wider digital finance ecosystem. Learners examine the foundational technology, use cases, and market impact of digital assets, blockchain, and decentralized finance (DeFi), and learn to integrate financial analysis with technological judgment to shape capital allocation and investment strategies. Compliance is treated as core, not afterthought: learners evaluate the regulatory and ethical challenges of data governance and digital asset management before the course culminates in designing an innovation strategy for a financial service or FinTech product — technology stack and market opportunity justified together. By the end, learners can think like both a financier and a technologist, which is exactly what the sector now demands.

This advanced course focuses on synthesizing human-centered design principles with technical feasibility to create compelling digital products. Learners begin where strong product work begins — pulling together insights from user research, design thinking, and strategic requirements into a coherent product vision and feature set. From there, they move into the craft itself: designing low- and high-fidelity wireframes and functional prototypes for technology solutions, including the increasingly common case of AI-driven user experiences. A dedicated focus on tool selection ensures learners can match design and prototyping tools to the complexity and technical constraints of a given project, rather than defaulting to whatever's familiar.

The second half is about rigor and accountability. Learners analyze user testing data and feedback to iterate their designs and optimize usability, and confront the UX challenges specific to AI systems — how to design for trust, how to build feedback loops that keep a model honest and a user confident. They determine and document the ethical, accessibility, and legal standards a product must meet before deployment, then learn to communicate a final design and its rationale to a leadership audience. By the end, learners can carry a product from strategic vision to a defensible, deployable design — and make the case for it in the room where decisions get made.

This course provides a strategic deep dive into designing and optimizing the customer experience (CX) using AI and automation. Learners start by mapping the end-to-end customer journey across omnichannel touchpoints, learning to spot friction and identify where automation creates genuine value rather than noise. From there, they evaluate the AI tools reshaping CX — virtual assistants, sentiment analysis engines, personalization systems — not as a shopping list, but against specific experience goals and strategic fit. The emphasis is on judgment: knowing which tool earns its place against which objective.

The course then moves from evaluation to design and accountability. Learners design intelligent, data-driven interaction flows that proactively predict customer needs and surface churn risk early, and apply sentiment analysis and natural language processing to extract meaningful insight from unstructured customer data. Because personalization and automation raise real stakes, learners integrate ethical standards and data governance directly into deployment rather than bolting them on later. The course culminates in a comprehensive AI-powered CX transformation roadmap, with every technology choice justified on organizational ROI and customer value. By the end, learners can lead a CX strategy that's both genuinely intelligent and genuinely trustworthy.

This course provides an advanced understanding of data architecture, modeling, and management in modern business systems. Learners master the principles of relational database design — conceptual and logical data models, normalization, schema design — alongside the query optimization techniques that keep data structures performant in both analytic and operational environments. The grounding is practical: these are the skills that determine whether a system runs smoothly or buckles under load, and learners build them through applied design work rather than abstract theory.

The course then scales up to the realities of modern data. Learners analyze non-relational databases and data lake architectures, evaluating storage technologies against data structure and application needs, and confront the modeling requirements and architectural challenges of scaling data for machine learning and Big Data platforms. Governance runs throughout: learners determine the security, access control, and data governance policies required to manage sensitive information responsibly. The course closes by connecting craft to strategy — integrating data modeling practice with business needs so the architecture serves organizational goals, not just technical ones. By the end, learners can design scalable, secure, business-aligned data architecture that holds up under real analytics and ML workloads.

This course provides an advanced, analytical examination of the legal and policy frameworks governing digital businesses and technology deployment. It moves beyond foundational contracts into the areas where modern technology actually creates exposure: data ownership, intellectual property, and regulatory compliance built around real frameworks like GDPR and CCPA. Learners analyze the legal implications of developing and deploying automated and AI-driven systems in a business context, then dig into the thorny questions of digital intellectual property — including the unsettled territory of rights over software, data, and generative AI content. The orientation is practical and strategic throughout: not law as abstraction, but law as a business risk to be managed.

From there, the course builds the judgment to turn regulation into strategy. Learners interpret global data privacy regulations and governance standards to formulate organizational compliance strategies, and examine the contractual and legal risks that come with cloud services, API usage, and third-party data reliance. A dedicated focus on algorithmic bias and data discrimination pushes learners to formulate governance policies that mitigate genuine legal and ethical risk — not just check a compliance box. Working through real legal case studies and precedents, learners learn to evaluate how law and governance actually apply in the digital economy. By the end, they can build governance and compliance strategies robust enough to stand behind a real technology initiative.

This course is the final, integrative experience of the degree — the point where technical skill, architectural thinking, and strategic judgment stop being separate courses and become a single, coherent piece of work. Learners execute a comprehensive project addressing a complex, real-world business challenge, beginning the way real initiatives begin: by synthesizing strategic analysis and organizational factors to define the problem clearly and justify the need for technology-driven change. From there, they design a comprehensive technical solution blueprint — data architecture, platform components, and all — then carry out the data modeling and machine learning execution needed to analyze outputs, derive actionable insight, and validate the solution's impact and ROI with evidence rather than assertion.

The back half of the project is where strategy and responsibility take over. Learners evaluate the ethical, legal, and security risks of their solution and formulate governance strategies that account for change management and employee impact — because a technically sound solution that ignores the human and legal realities isn't a real solution. They apply implementation planning techniques, resource allocation models, and change management principles to turn the design into an executable roadmap, then synthesize everything into professional, executive-level deliverables that communicate the strategic plan, technical justification, and financial rationale together. By the end, learners walk away with more than a grade: a portfolio-ready project that demonstrates they can take an AI-driven business challenge from problem definition all the way to a defensible, board-ready plan.