This interdisciplinary course introduces students to advanced research methods for investigating issues in European Studies. Each student will develop a research proposal and paper on a specific disciplinary topic under the supervision of a faculty member in that discipline, with the requirement that the paper include significant insights from at least one other discipline.

Covers the practice of data science, including communication, exploratory data analysis, and visualization. Also covered are the selection of algorithms to suit the problem to be solved, user needs, and data. Case studies will explore the impact of data science across different domains.

This course examines the ethical issues arising around big data and provides frameworks, context, concepts, and theories to help students think through and deal with the issues as they encounter them in their professional lives.

Learn a theoretical & applied process to identify risks in every public safety agency job description. From this basis, students will gain skills & knowledge to design & update control measures to proactively prevent tragedies from occurring. Final project to develop an instrument to recognize, prioritize, mobilize & address identified public safety risks in community/agency. Prereq: MPS student or Instructor permission

Explores the Constitution as the ethical compass that guides the work of public safety professionals and cement a fundamental understanding of the U.S. Constitution and the subsequent 27 amendments. Students will develop a detailed understanding of both the powers and limitations that arise from the Bill of Rights, and closely examine the evolution of the rule of law that frames and guides their work.

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 is for MSDS 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.