Learn core AI and data science concepts and skills (computing environments, visualization, machine learning, bias and fairness analysis.) Think like an AI driven Data Scientist through lectures, discussions, labs, & …
Will expose student to fundamental coding languages in data science. Python and R will be the primary focus of the course. Popular packages such as pandas and tidyverse will be …
This course will center on exposing students to contemporary pipelines for data analysis through a series of steadily escalating use cases. The course will begin with simple local database construction …
The course is designed to not only teach students tools necessary to visualize data but also effective techniques for explaining data driven results with an emphasis on communicating statistical output …
Explores principles and applications of data ethics within a broader social framework that prioritizes conversations about policy, regulatory frameworks, accountability, transparency, and governance models. Will discuss who is responsible for …
Explores principles and applications of data ethics within a broader social framework. Works to lay foundational knowledge for more advanced courses in the Value domain of the major. Will discuss …
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This course exposes students to foundational knowledge in each of the four high level domain areas of data science (Value, Design, Analytics, Systems). This includes an emphasis on ethical issues …
Exposes students to foundational knowledge in the area of analytics, especially as it relates to machine learning. The focus is on methods needed to prepare data for machine learning models, …
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Explore mathematical foundations of inferential and prediction frameworks, with emphasis on computation, used to learn from data. Frequentist, Bayesian, and Likelihood viewpoints are all considered. Topics: principles of estimation, optimality, …
The data science project course will allow students to take the knowledge gained in each of the four required courses and apply them to a data driven problem. Students will …
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Principles of interactivity in application and dashboard development using R, Python, and JavaScript programming languages. Design visually appealing and user-friendly interfaces, develop interactive applications for data visualization, and build dynamic …
Explainable artificial intelligence (XAI) is a subfield of machine learning that provides transparency for complex models to connect the technical meaning to social interpretation. Explore interpretability, transparency, and black-box machine …
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Study under the direction of a faculty member. Students must obtain approval on a syllabus from a faculty advisor to approve and direct the independent study. Student must be in …
Research under the supervision of faculty member. Student must obtain approval on the description and responsibilities of the research through a syllabus from a faculty advisor. Student must be in …
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 …
Train your own LLM for a custom task. Learn about the LLM lifecycle from architecture, to pre-training, to supervised finetuning, to deployment, to model editing/updating, including discussing LLM limitations. End …