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Completed Computer Science Courses:
Undergraduate Courses -- Graduate Courses
Intro to Programming: Introduction to the design and implementation of computer software with an emphasis on abstraction and program organization.
Programming Structures and Abstratcions: Application of software engineering principles with examples from central areas of computer science. Use of abstract data types such as stacks, queues, lists, and trees. File processing. Introduction to ethics in computer science, including philosophical ethics theories. Discussion of intellectual property rights and privacy.
Data Structures: Representation and analysis of widely used data structures and associated algorithms. Design of software systems. Written communications required in some projects. Discussion of ethical issues including computer crime, abuse, and hacker ethics.
Computer Organization: Introduction to the organization and structuring of the major components of computers and the transfer of information among these components for control and data processing.
Discrete Mathematics: Introduction to the mathematical foundation of computer science. Topics include combinatorics, logic, relations, functions, computational complexity, recurrences, and graph theory.
Operating Systems: An introduction to the major concept areas and techniques of designing and implementing operating systems. Class projects require the design of medium-scale software systems.
Theory of Computation: Introduction to abstract machine theory and formal language theory. Topics include turing machines, finite/pushdown automata, deterministic versus nondeterministic computations, context-free grammars, and mathematical properties of these systems.
Graphical User Interfaces: An introduction to human-computer interaction, and graphical user interfaces. Topics include: pinciples of human-computer interaction, human cognitive abilities, interface analysis and design, window systems, social implications of computing, and some 2D graphics. Current interface programming tools will be described and used. Oral presentations are required for some assignments.
Dynamic Data Assimilation: Exploration of general data assimilation theory. Derivation and application of multiple types of variational assimilation methods, filtering methods, reduced rank filters, etc..
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