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, how to evaluate the output of ML models and engineering features.

Moves deeper into current best practices around data engineering in industry. Topics will review basic data collection, ingestion, processing, and storage, moving beyond to data governance, security, pipeline orchestration, monitoring and maintenance, optimization, and documentation. Relies heavily on DevOps principles of automation, continuous improvement, and an understanding of the entire software/data lifecycle.

Engage with and train in the use of key concepts in machine learning and math: OLS estimator for regression; logistic regression & maximum likelihood estimator; multiple linear regression; principal components analysis & multiple correspondence analysis; neural networks; logarithms; probability distributions; integrals; multivariate optimization; matrix notation, eigen-math, and matrix decomposition; infinite power series & Taylor series.

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, bias, variance, consistency, sampling distributions, estimating equations, information, bootstrap methods, ROC curves, shrinkage, large sample theory, prediction optimality versus estimation optimality.

The course analyzes our global cultural condition from a dual historical perspective and follows a development stretching over the last 60 years, beginning with the period just after WW II and continuing to the present day. Of central concern will be the varieties of cultural expression across regions of the world and their relation to a rapidly changing social history, drawing upon events that occur during the semester.

Explores human aspects of cyber security -- including security training and awareness, cyber ethics, hacktivism, hacker culture -- with emphasis on human motivations and responses. Examines common human-centered attacks, such as phishing, social engineering, and other psychological manipulation.

This course examines the problems and issues confronting American national security policymakers and the factors that influence the policies that emerge. Types of questions we will address include: What threatens those interests? How should the U.S. defend those interests? What kind of military should we build? Should the U.S. enter alliances with other countries? The course is organized along an historical time line.

Develops strong writing competencies for technical fields, including communication of complex information to a variety of audiences through various print and online media. Teaches students to write, organize, edit, and design information with clarity and accuracy. Covers organizing, managing, communicating, and facilitating technical information. Topics include conciseness, simplicity, information arrangement, presentation, and readability.

Social entrepreneurship is an approach to creating system-level change through the application of entrepreneurial thinking to social ventures, non-profit organizations, government institutions, and NGOs to create economic, environmental, and social value for multiple stakeholders. In this course you will be introduced to a range of entrepreneurial approaches aimed at solving social problems - from the non-profit to the for-profit.

Provides an overview of the laws governing healthcare institutions and the ethical dilemmas facing healthcare managers and providers; reviews ethical principles utilized to examine health care issues. Evaluates the procedures followed by healthcare organizations in making legal and ethical decisions; addresses such contemporary issues as cloning, euthanasia, and organ donation. Prerequisite: Admission to BPHM or BIS program.