Online BAS in Managament & AI in Business Degree Program

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 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 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 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, 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 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 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 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 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.