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 data structures, searching, sorting, greedy algorithms, linear programming, & basics of databases. Will develop computational thinking skills and learn a variety of ways to represent & analyze real-world data.

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 data science course aims to cover fundamental topics of scientific computing, specifically selected and curated for data scientists, including numerical errors, root finding algorithms, numerical linear algebra, and numerical optimization.

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 learning, dropout as a Bayesian approximation, and decision theory. Will also cover applications of uncertainty in AI, such as computer vision, natural language processing,and autonomous systems.

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 analytical, political, and leadership skills they have learned in their other MPP courses and improve their ability to work in teams; and hone their written and oral presentation skills. Prerequisites: Graduate student in public policy

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, expectations, moments, transformations, model fit, sampling distributions, discrete and continuous time Markov chains, and Brownian motion.

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, sampling distributions, estimating equations, information, Bootstrap methods, ROC curves, shrinkage, and some large-sample theory, prediction optimality versus estimation optimality.

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 action. Students are exposed to federal, state & local policy challenges and opportunities as well as understanding how business & politics shape the policy landscape. Gain understanding of diverse climate-relevant career opportunities.

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 issues move through the policy process, at which points they are most amenable to influence, and how to create and use professional work products to influence them.

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 ventures. Students will grapple with the principles and process of effectual action and interaction. The course is designed to delve into effectual entrepreneurship ¿ philosophically, psychologically and practically.

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 random forests. Students will be required to fit the models (both MLE and Bayesian) and use the strategies discussed in class.