Master of Science in Cloud Computing

Overview of cybersecurity and privacy, including cryptography, authentication, malware, viruses, network security, anonymity, privacy and online privacy, risk management; common cyberattacks and techniques for detection and defense; policy and legal perspectives for managing cybersecurity missions supporting private sector and government; cyber technologies as applied to the stability of global information and communications infrastructure; government cybersecurity policies. (3 credits)

Cloud application design guidelines and software patterns. Survey of cloud services for scalable secure cloud applications. Trade-offs in cloud application design, container vs virtual machine deployments, and monolithic vs microservice. (3 credits).

In this course, the students will learn the basic skills that are essential to becoming a successful researcher. The objective of the course is to teach research skills in a systematic fashion, early in a student’s graduate program. Lecture topics will include research methodology, experimental design, professional ethics and academic integrity, and oral and written presentation techniques. Students will be required to perform a literature survey (on a topic in their own research area), construct a research proposal that includes an experimental design, and write a paper summary in the style of a formal scientific paper. (3 credits)

This course covers a wide range of research topics related to big data and cloud computing, including data centers, virtualization, hardware, and software architecture, as well as system-level issues on performance, energy efficiency, reliability, scalability and security. (3 credits)

The course provides a comprehensive guide to security concerns and best practices for cloud computing and cloud services. Topics discussed include cloud computing architectures, risk issues and legal topics, data security, internal and external clouds, information security frameworks and operational guidelines. (3 credits)

Applied and practical data analytics. High-level theory, with primary focus on practical application of a broad set of statistical techniques needed to support an empirical foundation for Computing and IT. Introduction to data analytics introduces you to the basics of data science and data analytics for handling of massive databases. The course covers concepts data mining for big data analytics and introduces you to the practicalities of map-reduce while adopting the big data management life cycle. (3 credits)

This course emphasizes learning algorithms and theory including concept, decision tree, neural network, computational, Bayesian, evolutionary, and reinforcement learning. The course will give the student the basic ideas and intuition behind modern machine learning methods as well as a bit more formal understanding of how, why, and when they work. The underlying theme in the course is statistical inference as it provides the foundation for most of the methods covered. (3 credits)

Problems in managing projects; project management as planning, organizing, directing, and monitoring; project and corporate organizations; duties and responsibilities; the project plan; schedule, cost, earned value and situation analysis; leadership; team building; conflict management; meetings, presentations, and proposals. (3 credits)

Maintaining compliance in the cloud. Theory, methodology, and procedures related to cloud computing; proper audit procedures for discovering system vulnerabilities; documenting findings according to the standards of compliance-based auditing. (3 credits)

Migrating traditional IT services to a cloud-based environment. Technical and business considerations necessary to develop an effective cloud migration strategy for an organization. Decision analysis framework to prioritize migration applications. (3 credits)

(Prerequisite: Must be taken in last term and all other courses completed) Thesis. (6-0) Credit 6 semester hours. A candidate for the Master of Science in Electrical Engineering is required to perform a study, a design of investigation, under the direction of a faculty advisory committee. A written thesis is required to be presented, defended orally and submitted to the faculty advisory committee for approval. (6 credits)