This course is intended for participants in the Undergraduate Student Opportunities in Academic Research (USOAR) program.

An introduction to concepts innovators use to solve problems and create value by addressing unmet needs. Learn how to identify and evaluate opportunities and use proven entrepreneurial frameworks to create new products and businesses for companies of all sizes. Through class activities, projects, and presentations you will learn how storytelling, teamwork, and leadership skills are essential for starting, funding, and building your business.

The course explores government contracting, how the government procures products and services, and opportunities created through government regulation. Pre-requisite: STS 1500 or ENGR 1020 or ENGR 2595-Engineering Foundations II.

Theories and cases studies concerning social, cultural and historical aspects of business, trade, finance, organizations, property systems, regulation and work. How are economic institutions and systems of exchange shaped by social and cultural contexts that they affect in turn? What alternative ways of organizing commerce are suggested by world comparative and historical study?

This is the foundation course for students admitted to the Global Studies-Security and Justice track of Global Studies.

Provides a global perspective on cyber security and the impact of cyber threats. Addresses a variety of topics that are all part of the cyber ecosystem, to include current threat trends, defense in-depth techniques, attack case studies, risk management, disaster recovery, security policy, and awareness training. Examines current best practices, compliance requirements, and evolving security architectures.

Provides overview of cyber security (CS) policies, laws, regulations, and ethical considerations that can be used to help protect and improve an organization's security posture. Explores related topics including ethics, CS policy requirements and controls, compliance, and implementation issues. Examines modern CS regulations and frameworks, and the various policies and procedures that may be used in an organization's security strategy.

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.