Description: Social network analysis [SNA] refers to the study of connections among and between social units: people, events, organizations, communities, and other groups. This course provides an introduction to the primary tools used to visualize and analyze network data. The course covers network measurement, community detection, and simulation techniques.

Skill-based prerequisites: None

Description: You do not need to be a data scientist to lead in a data-driven world, but you do need the ability to frame problems, evaluate evidence, collaborate with technical experts, and make sound decisions under uncertainty. This course prepares students from diverse disciplines to become effective leaders, collaborators, and stakeholders in organizations increasingly shaped by data and artificial intelligence. Through a semester-long applied project, students will identify a real challenge in their own field and use data-informed decision-making practices, AI concepts, strategic frameworks, and communication techniques to develop and defend an actionable recommendation.

Skill-based prerequisites: None

Description: Anyone can be a scientist, regardless of age, background and where you came from. This can be done through citizen science – a voluntary public participation in the scientific process. Citizen science seeks to answer scientific questions and provide possible solutions to real-world environmental and societal problems. This course is designed for students to explore available citizen science data and learn how to manipulate and clean data for analyses and visualizations. At the end of the semester, students would be able to enhance their critical thinking and analytical abilities, mainly from hands-on data analytics training, class lectures, learning opportunities from known citizen science experts and presentation of results to a broader audience.

Skill-based prerequisites: None

Description: An introduction to exercise science, wearable technology and personal health analytics through immersive virtual reality (VR) experiences. Students will participate in VR-based exercise sessions while collecting physiological data. Emphasis is placed on collecting and interpreting personal data and communicating insights using basic data science tools.

Skill-based prerequisites: None

Description: An introduction for students of all disciplines to the fundamentals of artificial intelligence (AI), with a focus on large language models (LLMs) and generative AI in today’s world. Through interactive modules, ethical case studies, and hands-on experimentation, students will develop the skills needed to critically evaluate AI systems, design effective prompts, and apply AI tools responsibly in academic, professional, and societal contexts. The course will help students build awareness, develop strategies, and apply AI knowledge to real-world contexts. The course culminates in a capstone mini-project where students integrate their AI literacy into personalized applications.

Skill-based prerequisites: None

Description: An introduction to artificial intelligence, focusing on applications in data science workflows. Students will explore AI’s potential for enhancing data-driven insights, automating processes and solving real-world problems. Through interactive, no-code tools, students will gain foundational knowledge of AI concepts while engaging with practical applications.

Skill-based prerequisites: None

Description: Students will use publicly available data to develop empirical models for predicting the outcome of sporting events. Students will manage, visualize, interpret and communicate statistical and general data processes. Students will apply the R programming language to forecast sporting outcomes with applications to sports betting.

[Skill-based prerequisites]: Students should have a basic knowledge of a programming language, preferably R and basic knowledge of statistics

Description: Students will learn to use existing satellite products as data for analysis. Applications include finding, loading and summarizing satellite data for places such as counties or census tracts, using Python. Students will join satellite data summaries to social, health, or economic data to answer applied questions.

Skill-based prerequisites: basic familiarity with a programming language (e.g., Python or R)

Description: A hands-on introduction to conceptual foundations and practical tool development for applied text analytics and natural language processing. Students will learn how to transform unstructured text into structured data, extract and visualize meaningful patterns, and communicate insights effectively. Students will work with real-world datasets such as news articles, social media posts, and policy documents. Students will leverage Python to develop reproducible workflows for tasks including classification, clustering, and question answering over document collections.

Skill-based prerequisites: Basic familiarity with Python

Description: When can we trust an AI system, and how do we know? This course provides a hands-on investigation into how AI systems behave, where they fail, and how their reliability can be evaluated. Topics include hallucinations, bias, robustness, prompt sensitivity, evaluation methods, and adversarial techniques such as prompt injection and jailbreaking. Students will learn to test the limits of AI systems, design meaningful evaluations and guardrails, and communicate evidence-based assessments of their trustworthiness.

Skill-based prerequisites: Basic familiarity with Python

Description: Generative AI is becoming a practical skill across nearly every field, including business, science, engineering, statistics, healthcare, education, design, and the humanities. This course is an introduction to the practical use of modern generative AI systems such as ChatGPT, Claude, Gemini, and other large language models. Students will learn how LLMs work at a high level, how to communicate with them effectively through prompt engineering, how to connect them to external knowledge using Retrieval-Augmented Generation (RAG), and how to build simple AI agents that use tools and APIs.

Skill-based prerequisites: Basic Familiarity with Python

Description: An examination of how organizations adopt AI and how leaders can move from technical opportunity to sustained organizational value. Through alternating case-study and methods-focused weeks, students apply a structured framework to understand AI systems, diagnose organizational context, examine and redesign work, lead adoption, establish appropriate oversight, and evaluate outcomes. Emphasis is placed on sociotechnical inquiry, evidence-based diagnosis, disciplined analysis of workflows and organizational behavior, measurable value realization, and accountable recommendations about whether AI initiatives should be expanded, changed, constrained, or discontinued.

Skill-based prerequisites: Basic familiarity with AI concepts and organizational processes; no programming or machine-learning implementation experience required.

Description: An overview of the essential aspects of planning an applied AI industrial project so it is ready to begin development. Topics include software engineering fundamentals and applied AI principles, including non-generative and generative AI methods, for business and industry. Students will learn to conduct design workshops with business stakeholders to define project objectives, requirements, and business benefits, and will work through the phases of ideation, problem understanding, solution design, and project planning. A real-world industry project illustrates the concepts and serves as the basis for course assignments and the final project.

Skill-based prerequisites: basic knowledge of probability & statistics and Artificial Intelligence. General knowledge of the software development lifecycle, software design, and project management. Knowledge of a programming language, preferably Python.