Computer Science

This course examines outline of object-oriented programming, use of C + + standard library, classes and objects, inheritance and derivation, polymorphism and combination, preliminary design patterns and new features of C + + programming language.

This course offers a study of programming technology. Topics include: Object Oriented Programming (OOP); classes and objects, the creation and destruction of objects, and dynamic memory; inheritance; polymorphism and dynamic binding; exceptions; input and output.

This is a special studies course with projects arranged between the student and faculty member. The specific topics of study vary each term and are described on a special study project form for each student. The number of units varies with the student’s project, contact hours, and method of assessment, as defined on the student’s special study project form.

This course reviews key theoretical and practical aspects of Information Security Management. The course content covers higher-level technical and theoretical issues as well as management issues such as organizational, planning, certification, auditing, and governance.  The course introduces students to a topical field of business and IT concern via varied learning styles and in-depth consideration of current issues, standards, and scenarios.

This course examines common data structures and their implementation, including stack, queue, string, binary tree, tree and graph; common retrieval, indexing and sorting methods, including linear table, hash table, inverted file, B-tree and other common retrieval and indexing technologies, insertion sorting, shell sorting, heap sorting, quick sorting, cardinal sorting and other common sorting algorithms and their time and space overhead; common algorithms and their implementation, including divide and conquer, recursion, backtracking, greedy method.

This class addresses topics from network structure and growth to the spread of epidemics. The course studies diverse algorithmic techniques and mathematical models that are used to analyze such large networks, and give an in-depth description of the theoretical results that underlie them. Some topics are random graphs, giant components, power laws, percolation, spreading phenomena, community detection, basic algorithms for network science, lower bounds and advanced algorithms for polynomial-time problems, sampling algorithms, streaming algorithms, sublinear algorithms, and graph partitioning algorithms.

The course assumes basic skills in algorithms and mathematics: familiarity with basic graph algorithms (shortest paths, flows), and basic understanding of NP-completeness. Work with basic probabilities and some integrals in included.