Reading and research under the direction of a faculty member. Students must obtain approval from a faculty advisor to approve and direct the independent study. Final approval by the Director of Undergraduate Programs is also required.

Writing of a thesis of approximately 50 written pages undertaken in the spring semester of the fourth year by archaeology majors who have been accepted into the Interdisciplinary Archaeology Distinguished Majors Program.Prerequisite: acceptence into DMP program

Covers foundations and applications of NLP with a focus on the most popular form of unstructured data ¿ text. Convert source texts into structure-preserving analytical form and then apply information theory, NLP tools, and vector-based methods to explore language models, topic models, sentiment analyses, and GenAI techniques. Focus is on unsupervised methods to explore cognitive patterns in texts, with real-world examples and demonstrations.

Provides healthcare domain knowledge, healthcare data understanding, and data science methodologies to solve problems. Understand data types, models, and sources, including electronic health record data; health outcomes, quality, risk, and safety data; and unstructured data, such as clinical text data; biomedical sensor data; and biomedical image data. Querying with SQL, data visualization with Tableau, and analysis and prediction with Python.

This course looks into the past, present, and future of technologies that impact labor, with an eye to empowering students with knowledge about the social, economic, and political dimensions of the tools they use both inside and outside of work. The course covers labor history, whistleblowers, and hidden histories of common technologies that reorient common assumptions about what technologies can do, and what they have done in the past.

Provides a foundation in discrete mathematics, data structures, algorithmic design and implementation, computational complexity, parallel computing, and data integrity and consistency. Case studies and exercises will be drawn from real-world examples (e.g., bioinformatics, public health, marketing, and security).

Provides an in-depth exploration of probabilistic and statistical methods used to understand, quantify, and manage uncertainty. Learn foundational concepts in probability and statistics, simulation techniques, and modern approaches to parameter estimation, decision theory, and hypothesis testing. Topics include parametric and nonparametric methods, Bayesian and frequentist paradigms, and applications of uncertainty in real-world problems. 

This course will equip students with some of the most commonly used deep learning architectures. We will explore feed-forward networks, convolutional neural networks, UNETs, encoders-decoders, generative adversarial networks and transformers. We will also analyze tools of explainable AI. Focused on environmental applications, students will apply these techniques to real-world data, solving problems in prediction, pattern recognition, and data-driven insights. Solid background in probability, statistics, and in coding (preferably Python) is recommended for enrollment in this course.

Focuses on principles & theories of law related to healthcare delivery, management & administration. Examines the application of laws on healthcare liability prevention & the risks managers face. Explores legal & ethical issues in healthcare systems; and investigates the healthcare administrator as decision-maker, leader and moral agent. Evaluates situations with potential ethical/legal implications.

An intensive overview of cloud infrastructure and their role in data science. Topics will include storage as a service, ephemeral computing resources, auto-scaling, and event-driven workloads. Special attention will be paid to cloud-native design patterns, which are built assuming the unique functionality of cloud computing resources.