This course provides a broad introduction to the exploration and management of large datasets being generated and used in the modern world. First, practical techniques used in exploratory data analysis and mining are introduced; topics include data preparation, visualization, statistics for understanding data, and grouping and prediction techniques. Second, approaches used to store, retrieve, and manage data in the real world are presented; topics include traditional database systems, query languages, and data integrity and quality. Case studies will examine issues in data capture, organization, storage, retrieval, visualization, and analysis in diverse settings such as urban crime, drug research, census data, social networking, and space exploration. Big data exploration and management projects, a term paper and a presentation are required. Sufficient background in database systems and statistics is recommended.

This course examines policies, methods and mechanisms for securing enterprise and personal data and ensuring data privacy. Topics include data integrity and confidentiality; access control models; secure database architectures; secure transaction processing; information flow, aggregation, and inference controls; auditing; securing data in contemporary (relational, XML and other NO SQL) database systems; data privacy; and legal and ethical issues in data protection. Programming projects are required.

This course covers concepts, principles, and practices of secure coding. It explores secure development and engineering techniques, practical defensive programming techniques, and modern tools for delivering secure programs. The role of software verification and validation, including testing and formal methods, is stressed. The course also focuses on the design of modern secure programming languages and the role of the compiler in generating defensive code. The societal need for secure programming is also highlighted. Note: Programming projects, presentations, and a research report will be required.

An introduction to the study of distributed systems. The course covers distributed system architectures such as client-server and peer-to-peer, distributed system design issues such as communication, fault tolerance, coordination, and deadlock, distributed system middleware such as remote method invocation (RMI) and tuple space, and the theory of distributed algorithms such as logical clocks and leader election. Students will also learn about ethical and legal concerns in computing and research. Programming projects are required.