Through a step-by-step process students learn to conduct statistical analyses to examine, evaluate, and share relevant public safety related data. Students also learn how to make practical interpretations of the data and methods for decision-making.

Designed for capstone project teams to meet in groups with advisors and clients to advance work on their projects. Capstone course for MSDS Online students.

Comprehensive introduction to predictive modeling, a cornerstone of data science and machine learning. Learn the fundamental concepts, techniques, and tools used to build models while emphasizing both theoretical understanding and practical applications. The topics include we will cover are an in-depth analysis of linear models and different variants, their extension to generalized linear models, and an introduction to nonparametric regression.

Examines joint operations and incident command for complex events. Emphasis will be placed on command structure, continuity of operations, public safety response to community/public health emergencies, occupational health and safety, local systems and resources, inter-agency cooperation, and communications and technology support. Students will engage public safety response issues and apply their knowledge through scenario exercises.

This course covers fundamentals of data mining and machine learning within a common statistical framework. Topics include regression, classification, clustering, resampling, regularization, tree-based methods, ensembles, boosting, and Support Vector Machines. Coursework is conducted in the R programming language.

Focuses on the application of communication skills and principles in the context of public safety. Students will gain understanding and practice in engaging communities around such challenging issues as inequality and power; interactions in the aftermath of tragedy; officer fear and anger; historical, political, and economic divides; implicit biases and stereotype threat; and the importance of building coalitions across boundaries.

Bayesian inferential methods provide a foundation for machine learning under conditions of uncertainty. Bayesian machine learning techniques can help us to more effectively address the limits to our understanding of world problems. This class covers the major related techniques, including Bayesian inference, conjugate prior probabilities, naive Bayes classifiers, expectation maximization, Markov chain monte carlo, and variational inference. A course covering statistical techniques such as regression.

This course introduces first-year graduate students in the humanities and social sciences to the knowledge and skills fundamental to success in graduate school. Particular topics vary.

Students will develop a detailed understanding of the legal aspects of public employment law, and the short and long-term impact of recruiting and retaining talented employees. Emphasis will be placed on the means by which evidence-based strategies may be applied to determine the appropriate number of resources to deploy to normal and complex operations. Prereq: Admission to MPS Degree Program

A graduate-level course on deep learning fundamentals and applications with emphasis on their broad applicability to problems across a range of disciplines. Topics include regularization, optimization, convolutional networks, sequence modeling, generative learning, instance-based learning, and deep reinforcement learning. Students will complete several substantive programming assignments. A course covering statistical techniques such as regression.