Introduces basic principles and techniques of building secure information systems. Covers network security, web security, privacy, and basic cryptographic tools (symmetric and public key cryptography, encryption, key exchange, hashing and signatures). Broader social, legal and political aspects of security addressed.

This course is intended as the first to take for students interested in the aspects of computer science related to data analysis and data management. It specifically serves as a preparation including, but not limited, to the courses CS460, CS506, CS542 and CS565. Course topics will cover data collection, cleaning and visualization. Data modeling and basics of data bases. Mathematical foundations of data science including linear algebra, (multivariate) calculus and convex optimization. Topics in data mining, such as similarity and distance functions, clustering, ranking, networks. Introduction to machine learning. Prediction methods, e.g. regression and common measures.

Basic Algorithms to guarantee confidentiality and authenticity of data. Definitions and proofs of security for practical constructions. Topics include perfectly secure encryption, pseudorandom generators, RSA and Elgamal encryption, Diffie-Hellman key agreement, RSA signatures, secret sharing, block and stream ciphers.

Introduces basic principles and techniques of building secure information systems. Covers network security, web security, privacy, and basic cryptographic tools (symmetric and public key cryptography, encryption, key exchange, hashing and signatures). Broader social, legal and political aspects of security addressed.

CS108 is a programming-based introduction to computer science for students not majoring in computer science. The course develops basic computer programming skills and algorithmic thinking, with an emphasis on developing interactive, database-driven applications for the World Wide Web (i.e. Facebook).

Basic Algorithms to guarantee confidentiality and authenticity of data. Definitions and proofs of security for practical constructions. Topics include perfectly secure encryption, pseudorandom generators, RSA and Elgamal encryption, Diffie-Hellman key agreement, RSA signatures, secret sharing, block and stream ciphers.

Introduces basic principles and techniques of building secure information systems. Covers network security, web security, privacy, and basic cryptographic tools (symmetric and public key cryptography, encryption, key exchange, hashing and signatures). Broader social, legal and political aspects of security addressed.

This course is intended as the first to take for students interested in the aspects of computer science related to data analysis and data management. It specifically serves as a preparation including, but not limited, to the courses CS460, CS506, CS542 and CS565. Course topics will cover data collection, cleaning and visualization. Data modeling and basics of data bases. Mathematical foundations of data science including linear algebra, (multivariate) calculus and convex optimization. Topics in data mining, such as similarity and distance functions, clustering, ranking, networks. Introduction to machine learning. Prediction methods, e.g. regression and common measures.

Basic Algorithms to guarantee confidentiality and authenticity of data. Definitions and proofs of security for practical constructions. Topics include perfectly secure encryption, pseudorandom generators, RSA and Elgamal encryption, Diffie-Hellman key agreement, RSA signatures, secret sharing, block and stream ciphers.