An Independent Study in Archaeology. Subject to be determined by student and instructor.

The data science project course will allow students to take the knowledge gained in each of the four required courses and apply them to a data driven problem. Students will work in groups and can either choose a project provided by SDS faculty or can propose a project for approval. Upon completion of the course students will be required to present their results and publish project content to an open forum.

Principles of interactivity in application and dashboard development using R, Python, and JavaScript programming languages. Design visually appealing and user-friendly interfaces, develop interactive applications for data visualization, and build dynamic dashboards for effective data communication with end-users. Covers theoretical concepts and hands-on implementation to provide a comprehensive understanding of the full design process.

In this course, students will learn how to trace the "causal chains" from such actions/inactions to various ecosystem, social, and economic outcomes and to measure and value those outcomes. We will consider the philosophical/ethical underpinnings of the Ecosystem Services framework, use computer mapping and other software tools for evaluation, and review current applications of the framework by private and public sector entities.

Explores mobile device architecture and cyber threats. Examines the security strengths and weaknesses of mobile devices and platforms, as well as corporate mobile security policies and procedures. Topics include mobile device operating system software architecture, mobile application architecture, mobile device and application vulnerability assessment testing, and mobile device forensic analysis.

Critique models and adapt them to a variety of data sets. Gain a deeper understanding of core ML concepts. Build towards neural networks (latent index models, more complex linear models with non-linear transformations of the data). Compare new methods to kNN, clustering, linear models from ML1 to discuss performance differences as complex and predictive power increases. How mathematical concepts are present in the models presented.

Principles of interactivity in application and dashboard development using R, Python, and JavaScript programming languages. Design visually appealing and user-friendly interfaces, develop interactive applications for data visualization, and build dynamic dashboards for effective data communication with end-users. Covers theoretical concepts and hands-on implementation to provide a comprehensive understanding of the full design process.

Explainable artificial intelligence (XAI) is a subfield of machine learning that provides transparency for complex models to connect the technical meaning to social interpretation. Explore interpretability, transparency, and black-box machine learning methods. Covers definitions, decision support, trust, and ethical considerations, and the latest advances in creating reliable and transparent AI models.

Explores the tools and techniques used to assess the security posture of a target system. Topics include footprinting, reconnaissance, vulnerability discovery, and malware. Covers vulnerability discovery in a variety of systems, including web applications, mobile platforms, and cloud computing. Aligns with the EC-Council ANSI accredited Certified Ethical Hacker exam 312-50.

The practicum uses problem-based learning to develop relevant facts and sound arguments surrounding local, national and global sustainability challenges. Working with live case studies in the U.S. and abroad, we will follow the steps from problem formation, through model building, data collection, and qualitative and quantitative analysis, and finally on to technical and advocacy communications grounded in our facts.