300
Examines the basic concepts of programming languages: programming language processors, elementary and structured data types, subprograms, sequence control, data control, storage management, syntax and translation, and programming environments. The student will also study three different programming languages and write a short project in each.
Data science is an interdisciplinary field which blends mathematics, computer science, and various domain-specific fields (such as bioinformatics). The goal is to extract usable information from large sets of data. This course will be an introduction to data science using R, Python or a similar language. Emphasis will be on exploratory data analysis, visualization, model fitting, classification, and prediction.
Computer
Science
Elective
Studies the use of simulated evolution in computer science and biology. Primary emphasis will be on the use of evolutionary and genetic algorithms as tools for solving combinatorial optimization problems (including problems arising in bioinformatics). Secondary emphasis will be placed on construction of computer models designed to illuminate aspects of evolutionary theory (e.g. the computer evolution of strategies for playing the prisoner's dilemma as a model for the evolution of altruistic traits).
Covers the fundamental algorithms used in both symmetric key and public key cryptography. Algorithms include AES, Diffie-Hellman, RSA, elliptic curve cryptography, as well as cryptographical hash algorithms. Both mathematical foundations and computer implementations will be discussed during the course.
Is designed to teach computer science and computer information science majors the skills necessary to learn computer science on their own and communicate their knowledge to others in oral and written form. All students will attend presentations made by senior computer science students. Students will be required to write a short, independently-researched paper and present it to the other students in the junior seminar.
Will introduce concepts of algorithm analysis, strategies, time and resource complexity and basic computability.
Is a seminar in information resource management covering such topics as office automation, networks, distributed data processing, data integrity, and decision support systems.
Junior standing or permission of instructor
Provide an understanding of fundamental software architecture concepts, standards, drivers, styles, and design specification tenets including design patterns. Develop an understanding of UML method notations and tools to document architectures, and use of tradeoff methodologies (e.g., QAW, ATAM) to evaluate an architecture. Present tenets of technical and ethical debt. Throughout the course students will implement a core set of software design patterns using language of choice.