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: Students will interact with local entrepreneurs and speakers from a variety of career pathways. Speakers will describe their career trajectories, with a focus on particular decision points, and discuss with students the types of information and data that were relevant and informative to the outcome. Using insights from the discussions, students will explore and utilize various generative AI tools, with a primary focus on language, images and other potential data sources (e.g., sound), to explore their own possible future careers and life decision points. Thematic course topics may include work, recreation, health, lifelong learning, creativity, social networks, strengths and values.

Skill-based prerequisites: None

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: An introduction to how AI is reshaping Cybersecurity. Students will learn how AI is helping attackers create better phishing emails, research on companies & organizations, and more sophisticated attacks. Students will also explore how AI is helping Cybersecurity defense with methods that allow for the analysis of massive amounts of data, vulnerabilities discovery, and enhanced protection. After completing this course students will understand the risk and benefits of AI in the realm of Cybersecurity.

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 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 apply AI tools to design strategic communication campaigns across diverse topics, such as mental health awareness, environmental sustainability, social justice, and more. Students will collaborate in teams to research various organizational contexts, analyze existing marketing and communication materials using AI, develop and implement data-driven communication strategies enhanced by AI-powered insights and technologies, and assess campaign effectiveness. The course emphasizes communication design principles, ethical AI use, interdisciplinary collaboration, and practical, real-world applications.

Skill-based prerequisites: None

Description: Supervised machine learning is a fundamental approach in AI which involves training models based on labeled and historical data. These models are used to understand hidden patterns in data associated to specific past events allowing them to predict future occurrences. Advanced machine learning models include gradient boosting, random forest, neural networks and support vector machines. Students will create projects using a low-code/no-code AI/ML framework. The course will cover the analytical lifecycle using SAS Viya, from data preparation to exploratory analysis, feature engineering, model development and deployment.

Skill-based prerequisites: Students should have familiarity with statistical and mathematical modeling.

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: 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 work with manufacturing data to detect quality defects and quantify their cost and time impacts. Course emphases include data visualization, trend analysis, and communication of analytical results to support quality improvement initiatives. Techniques from Lean Six Sigma, Kaizen, and poke-yoke may be applied.

[Skill-based prerequisites]: A general understanding of manufacturing systems and proficiency with Microsoft Excel.

Description: This course demystifies modern transformers and large language models (LLMs), at an application-focused level, to help students understand how LLMs work “under the hood,” what LLMs can and cannot do, and how to use LLMs responsibly in real projects. Students will learn the evolution from early neural networks to transformers, core transformer components (attention, embeddings, positional encoding), and practical LLM workflows including prompt engineering, fine-tuning concepts (including RLHF at a conceptual level), Retrieval-Augmented Generation (RAG), and LLM agents. The course culminates in a hands-on project building a small RAG + agent system for literature exploration across a large document collection.

Skill-based prerequisites: Linear algebra basics: vectors, matrices, dot products, basic probability/statistics, ability to read/write basic Python (e.g., functions, loops, data structures), familiarity with notebooks (e.g., Jupyter/Colab).

Description: This course equips students with the tools to answer the strategic question: “What is the effect of our actions?” through a project-based and a practical introduction to the concepts and statistical methods of causal inference. Students will learn to move beyond correlation to rigorously investigate causation, a critical skill for evidence-based decision-making in business, public policy, and healthcare. Students will explore the “gold standard” of Randomized Controlled Trials (A/B testing) and powerful quasi-experimental methods (like Difference-in-Differences and Regression Discontinuity) for when experiments are not feasible.

[Skill-based prerequisites]: Familiarity with introductory statistical concepts (e.g., probability distributions, measures of center and dispersion); Familiarity with programming (e.g., R or Python: data structures, loops, functions).

Description: An exploration of methods for evaluating and improving fairness, transparency and accountability in modern AI systems. Topics include algorithmic bias, disparate impact, explainability, model auditing and compliance with emerging governance frameworks. Students will engage with case studies from criminal justice, healthcare and public administration, and build hands-on skills by auditing machine learning pipelines for fairness and robustness.

Skill-based prerequisites: Basic knowledge of machine learning (e.g., training classifiers, evaluation metrics)

Description: An introduction to the R programming environment geared towards the biological sciences. Topics include installation & software setup, programming, and data exploration & analysis, with heavy focus on data visualization [graphics] within R. Students will learn analyses with a focus on shareability and reproducibility within the context of a project or mini projects. Students may bring their own data and use-case datasets will be available as well. Upon completion of the course, students will be prepared to extend their contextual programming skills and understanding into future work involving data science and AI pipelines with a greater understanding of the underlying processes. Although examples focus on biological contexts, learners from all fields are welcome to bring their background knowledge and extend their understanding.

[Skill-based prerequisites]: Familiarity with biology and biological research, or research/background knowledge in a field of interest.