These majors require a series of lower-division courses, and prerequisites constrain the order in which they can be taken. Junior-level transfer students who must complete a significant part of this sequence may find that it will take longer than two years at UCI to complete their degree.
It begins with the review of integers and a thorough coverage of the fundamentals of finite group theory followed by the RSA and ElGamal ciphers. Primitive roots in cyclic groups and the discrete log problem are discussed.
Baby-step Giant-step and the Index Calculus probabilistic algorithms to compute discrete logs in cyclic groups are presented. Pollard's Rho, Pollard's and Quadratic Sieve factorization algorithms are presented.
The course ends with the coverage of some oblivious transfer protocols and zero-knowledge proofs. There are numerous programming assignments in the course. MET CSor instructor's consent. It begins with the coverage of commutative rings, finite fields, rings of polynomials, and finding of the greatest common divisor in the ring of polynomials.
Irreducible polynomials are discussed. The course continues with the introduction of message integrity and message authentication. Finally, entity authentication and key management issues are discussed.
MET CS ; or instructor's consent. MET CS Agile and Advanced Software Engineering Methods Builds on previous design and programming courses and introduces students to the technological, social, and pragmatic aspects of developing open source software through direct involvement in an open source project.
Students learn to use the tools, techniques, and strategies of open source developers. They become familiar with the open source movement, its philosophy, history and licensing issues. This is a project-based laboratory course. Students are directly involved with and integrated into an open source project.
They are expected to be active participants in the project and contribute to the project in various ways.
First, Agile methods are based on the need for flexibility while applications are being built. Agile methods constitute a radical departure from pre-existing methods: They rely on newly developed technologies such as test-driven development, XUnit, and refactoring.
A second form is the emergence of open-source development. This course teaches the architectural and operational implications of open source development and explores its relationship with agile methods.
The course will also discuss aspect-oriented programming, the decomposition of applications into onshore and offshore components, design for security, and formal methods.
This option is available to Master of Science in Computer Science candidates who have completed at least seven courses toward their degree and have a GPA of 3. Students are responsible for finding a thesis advisor and a principal reader within the department.
The advisor must be a full-time faculty member; the principal reader may be part-time faculty with a PhD unless waived by department.
Students majoring in Computer Science may elect a thesis option.These information services are provided by Johns Hopkins to assist in accomplishing its business and mission. The accuracy and integrity of the data being recorded by this means is of vital importance for institutional systems. Computer Science at a Glance.
Computer science degrees can be found at every educational level from associate degrees to PhDs. Most programs combine the study of theory with the application of computer science. Apache/ (Scientific Linux) Server at ashio-midori.com Port Statistics is a branch of mathematics dealing with data collection, organization, analysis, interpretation and presentation.
In applying statistics to, for example, a scientific, industrial, or social problem, it is conventional to begin with a statistical population or a statistical model process to be studied. Populations can be diverse topics such as "all people living in a country" or. Master of Science in Computer Science Degree through BU MET in Boston.
Graduate computer science degree program with software engineering, database management and other courses intended for computer professionals and others seeking career advancement. Machine Learning vs. Statistics The Texas Death Match of Data Science | August 10th, Throughout its history, Machine Learning (ML) has coexisted with Statistics uneasily, like an ex-boyfriend accidentally seated with the groom’s family at a wedding reception: both uncertain where to lead the conversation, but painfully aware of the potential for awkwardness.