Core Courses

DTSC 540 Introduction to Artificial Intelligence: Theory, Tools, and Applications
This course is a comprehensive introduction to the field of artificial intelligence (AI), tailored for graduate students with minimal prior background knowledge in AI or machine learning (ML).  The course focuses on foundational theories of AI, ethical and societal implications of AI technologies, and practical skills in using modern AI tools.  This is an interdisciplinary course appropriate for learners from all disciplines.

HUMA 550 AI for Everyday Innovation
This course equips individuals in non-technical roles with the strategic skills to leverage existing AI tools for process improvement. Along with an introduction to basic prompting, the focus is on human-centered planning, workflow design, and strategy. Students will learn to critically evaluate, compare, and select from diverse AI solutions, including generative AI, automation platforms, and AI-enhanced productivity apps. The curriculum emphasizes identifying innovation opportunities in daily work, planning pilot tests, and creating evaluation plans that measure success not just in productivity, but in holistic human outcomes such as reduced cognitive load, increased job satisfaction, and enhanced creativity.

DTSC 690: Ethical and Philosophical Issues in Data Science And Analytics
Students will explore contemporary ethical and philosophical issues in data science, analytics, and artificial intelligence. Students will engage with a wide range of interdisciplinary readings examining moral challenges and responsibilities inherent in the development and deployment of new data-driven technologies. Topics include societal and psychological impacts of AI, challenges of misinformation and algorithmic bias, the complexities of privacy and surveillance, and global implications of technological development.

HUMA 691 Human-Centered AI Application: The Capstone Studio
Students will complete a capstone project integrating their learning across courses. The final project will combine research, critical thinking and creative production or problem-solving in order to showcase successful human collaboration with large language models. Prerequisites: Students must have completed 15 credits to register.

Elective Courses

DTSC 555: SQL Essentials for Decision Makers
This course introduces students to the fundamental language of data: SQL. Designed specifically for non-technical professionals in healthcare, business, the humanities, and beyond, this course will use SQL to explore the principles of Boolean logic, set theory, and structured problem-solving.  Students will focus on how to retrieve, filter, and interpret information, as well as how to translate real-world business questions into structured queries. The course will cultivate a "data-fluent" mindset that allows students to interact directly with organizational data without relying on technical intermediaries. The goal is to serve as a foundational building block for any professional looking to strengthen their analytical reasoning and work more effectively with automated systems.

HUMA 560 AI for Teaching and Learning
This course explores the transformative potential of artificial intelligence within educational, training, and professional learning environments. Students will investigate the pedagogical and instructional design principles required to effectively integrate AI tools, with an emphasis on designing high-impact learning experiences that support diverse learner needs. The curriculum maintains a critical, human-centered perspective, assessing the impact of AI on educational policies, instructional workflows, and equitable learning outcomes. 

HUMA 620 Multimedia Production: AI and the Creative Process
This course investigates the collaborative relationship between human creative practice and artificial intelligence in the multimedia production process. Students navigate a comprehensive end-to-end workflow – from AI-assisted brainstorming to storyboarding to post-production – in order to design multimedia campaigns for the modern workplace. This iterative approach culminates in a multimedia project, such as interactive online experiences and short-form videos, tailored to the student’s specific professional goals.

HUMA 630 The Psychology of Intelligent Systems 
This interdisciplinary course invites students to explore the profound psychological questions raised by the rise of intelligent systems. As artificial intelligence becomes increasingly sophisticated and integrated into our daily lives, what does it mean to be human? Drawing on concepts from cognitive, social, and developmental psychology, students will examine the formation of digital identity, the psychology of human-AI interaction, and the emotional and ethical complexities of relationships with human-like machines. The course will also challenge traditional notions of consciousness, intelligence, and moral agency, asking whether machines can think, feel, or possess personhood.

HUMA 650 Automated Workflows
Recommended precursor: HUMA 550 AI for Everyday Innovation
This course focuses on designing and building integrated, end-to-end automated systems. Students will get hands-on experience using no-code/low-code platforms to connect applications and create streamlined, multi-step workflows. The curriculum balances this technical construction with the human side of implementation, focusing on human-in-the-loop systems design and the change management strategies needed for successful adoption.

HUMA 680 AI Media Literacy 
In a world saturated by AI-generated and algorithmically amplified content, this course equips students with advanced skills to critically analyze, evaluate, and navigate mediated information. The curriculum focuses on understanding the mechanisms of generative media creation, identifying algorithmic manipulation (e.g., deepfakes), and discerning the intent and source of digital information. Students will develop frameworks for responsible content creation and consumption to foster digital citizenship and resilience.

DTSC 545: Introduction to prompt engineering
In an era where generative AI plays an increasingly significant role in organizational strategy, this course equips students with the knowledge and skills to harness Large Language Models (LLMs) effectively. Students will learn foundational AI concepts, develop core prompting techniques, explore advanced strategies, and apply prompt engineering across various business fields. The course also emphasizes evaluation, ethics, governance, and integrating prompts with structured data and tools (e.g., JSON, SQL, Excel). Students complete a capstone prompt engineering project tailored to a real-world business challenge.

DTSC 600 Data Visualization
This course is designed to teach students the best practices in Data Visualization, the key trends in the industry, and how to become great storytellers with data. Students taking this class will learn the importance of using actionable dashboards that enable their organizations to make data-driven decisions. For this class students will use Tableau, one of the most used visual analytics platforms in the industry.

BUSA 610 AI for Growth
This course equips students to manage AI capabilities in their organization to accelerate customer-centric growth. Students will examine AI-driven tools and martech techniques that enable businesses to identify market gaps, personalize experiences, understand customer behavior, and foster innovation through growth loops, data flywheels, value creation networks, and ROI frameworks. Marketing tasks such as customer segmentation, predictive analytics, content creation, and product development will be explored through case studies, hands-on experiences, and other practical exercises culminating in a final AI-enabled growth plan. Students will learn to leverage AI for strategic growth while maintaining ethical standards and data privacy.

BUSA 640 AI for Efficiency
This course empowers students to manage AI initiatives in their organization to transform business processes and supply chain operations using cutting-edge AI technologies. Students will explore how AI technologies such as predictive analytics, robotic process automation (RPA) and generative AI can be applied to enhance efficiency, optimize workflows, reduce costs, streamline logistics, and strengthen competitive positioning. Through case studies and experiential projects, participants develop practical AI strategies tailored to real-world business contexts such as automated inventory and demand forecasting, HR and finance processes, predictive maintenance, and logistics optimization, among others. A final AI-enabled business efficiency plan will demonstrate strategic AI business competency.