Introduces fundamental concepts of computation, data structures, algorithms, & databases, focusing on their role in data science. Covers both theoretical studies & hands-on learning activities. Includes basic data structures, advanced …
Many problems in data science essentially boil down to some mathematical relationships that are to be solved numerically. But have you ever wondered how computers could do math? This graduate-level …
Covers the fundamental concepts of uncertainty in artificial intelligence (AI). Students will explore various techniques and models used to handle uncertainty in AI and machine learning systems, including Bayesian deep …
The purpose of this course is to develop the student's ability to define and solve public problems. Subsidiary objectives of the course are to help the student to integrate the …
Covers the fundamentals of probability and stochastic processes. Students will become conversant in the tools of probability, clearly describing and implementing concepts related to random variables, properties of probability, distributions, …
Explores the mathematical foundations of inferential and prediction frameworks commonly used to learn from data. Frequentist, Bayesian, Likelihood viewpoints are considered. Topics include: principles of estimation, optimality, bias, variance, consistency, …
This class explores the intersection between how we perform elections and how we craft public policy. We investigate two broad questions (1) the effect of policy on elections -- how …
Project oriented course that will research specific climate problems, proposing new solution to decision makers at local & state level. Course expands understanding of broad societal scope relevant to climate …
In this course students will learn how to create change in the public policy arena by understanding political actors, their interests, and the institutions they inhabit. Students will learn how …
Course uses expert entrepreneurs with decades of starting & running new ventures. Expert entrepreneurs learn to tackle the unpredictable, but also to embrace and leverage it to cocreate enduring new …
Introduction to regression modeling. Topics will be discussed first in the context of linear regression, and then revisited in the context of logistic regression, ordinal regression, proportional hazards regression, and …
Introduces physics-aware deep learning (PADL), an emerging approach that embeds physical laws into neural networks for accurate, efficient modeling. Topics include differential equations, physics-informed neural networks, neural operators, and PyTorch …
Fundamentals of data mining and machine learning within a common statistical framework. Topics include boosting, ensembles, Support Vector Machines, model-based clustering, forecasting, neural networks, recommender systems, market basket analysis, and …
Provides an exploration of foundational concepts in modern time series modeling and analysis. The course covers both classical statistical and signal processing methods and contemporary deep learning models.
Investigates a selected issue in public policy or leadership.
This course provides the opportunity to offer a new topic in the subject area of data science.
Investigates a selected issue in public policy or leadership.
Covers data pipeline: techniques to collect data, organize, query & apply the data, and generate products that describe the insights. Topics include Python environments, containers using Docker, data wrangling with …
Combines topics in data ethics, critical data studies, public policy, governance, and regulation. Address challenges by topic (Health, Education, Culture & Entertainment, Security & Defense, Cities, Environment, Labor). Research how …
This course will provide a solid foundation of insights into how Congress works, essential for aspiring public policy advocates. Topics investigated include historical precedents for policymaking, the process of Congressional …