Computer Science Minor

Overview of the software development process. Includes requirements, design, construction, and testing of software. Software project planning. Analysis, architecture, and design of software systems using UML. Evaluating designs. Implementing designs using appropriate data structures, frameworks, and APIs. Prerequisite: EGR 120 or 121. (3 units; Fall/Spring/Summer)

Fundamental data structures for implementation and analysis. Techniques for solving problems by programming. Analysis for complexity and performance trade-offs. Topics include object oriented design, debugging, abstract data types (ADTs), recursion, big-O notation, interfaces, inheritance, and encapsulation, linked lists, stacks, queues, hash tables, heap, trees, searching, sorting. Prerequisite: EGR 222. (3 units; Fall/Spring)

Introduction to computer science. Covers problem solving methods and algorithm development; modern programming methodologies; and fundamentals of high-level block structured language using Python. (3 units; Fall/Spring)

Introduction to computer science. Covers problem solving methods and algorithm development; modern programming methodologies; and fundamentals of high-level block structured language using C++. Prerequisite: EGR 181 or MAT 115. (3 units; Fall, Spring, & Online)

Introduces the foundations of discrete mathematics as they apply to computer science, focusing on providing a solid theoretical foundation for further work. Topics include functions, relations, sets, simple proof techniques, Boolean algebra, propositional logic, digital logic, elementary number theory, and the fundamentals of counting. (3.0 units; Fall)

A study of various methods of proofs and mathematical structures covering logic, universal and existential quantifiers, sets, functions, and selected topics in discrete mathematics, linear algebra, abstract algebra, and real analysis. This is a bridge course to abstract mathematics and should be taken prior to or concurrently with courses in linear or abstract algebra, and real or complex analysis. Prerequisite: MAT 245. (3 units, Fall/Spring)

Algorithms are the heart of any computer software; they define the procedure for accomplishing a data-intensive task. This course establishes terms and methods for talking about algorithms, examines some of the basic algorithms for sorting, searching and other fundamental tasks, and explores more advanced algorithms in bioinformatics and other fields. Prerequisite: EGR 227. (3 units; Fall/Spring)

Introduces students to the organization and architecture of computer systems, beginning with the standard von Neumann model and then moving forward to more recent architectural concepts. Introduction to assembly language programming. Prerequisite: EGR 120 or 121. Pre- or Co- Requisite: EGR 225. (3 units; Fall)

This course explores issues related to the production and the analysis of imagery and visual representations. Computer graphics are introduced, from basic concepts through rendering and animation. Visualization of data is presented. Computer Vision and Image Processing is presented both mathematically and practically, with an emphasis on creating image manipulation programs in a high-level language. Prerequisite: EGR 227. (3 units; Spring)

Introduction to concepts and considerations of modern compilers and programming languages. Language translation, types and declaration, and abstraction mechanisms are studied. Both functional and object-oriented programming paradigms are explored through examination of several programming languages. Automata, languages and grammar, language processing, computability and complexity theory will be examined in detail. Important topics will be explored using a combination of conceptual work and coding exercises. Prerequisites: EGR 225 and 227. (3 units; Fall)

Artificial Intelligence (AI) is concerned with the design and analysis of autonomous agents that perceive their environment and make rational decisions. This course introduces the basic definitions and issues in Artificial Intelligence and Machine Learning. Students will create reasoning systems in software and explore their capabilities in dealing with new knowledge. Ethical issues in AI will be presented and examined from a Christian perspective. Prerequisite: EGR 227 (3 units; Spring)

This course covers introductory machine learning topics including supervised and unsupervised learning, linear and logistic regression, support vector machines, neural networks (MLPs, CNNs, RNNs, GANs) and more. Coursework includes instruction and programming assignments in algorithmic implementations and high-level library usage. Students also apply machine learning techniques to a unique research project. Prerequisites: EGR 120 or 121, and one of the following: EGR 305, MAT 353, STA 144, 310, or one additional approved statistic course. (3 units; Fall)

The course commences with an examination of the knowledge discovery process. In particular, the introduction equips students with the strategic thinking skills essential to focus on cutting edge data mining techniques that can be applied in a wide variety of settings, e.g., business, engineering, health care, science, etc. Traditional topics include data mining algorithms and implementation issues, advantages and disadvantages of data mining, and examples of knowledge engineering. Current topics such as ubiquitous, distributed, and spatiotemporal geographic data mining will also be explored. This is a practical hands-on course that culminates in a real-world project implemented via open source tools. Prerequisites: EGR 120 or 121, and one of the following: EGR 305, MAT 353, STA 144, 310, or one additional approved statistics course. (3 units; Fall)

Natural language is ubiquitous, e.g., humans speak and write to communicate, to transfer information, and to document knowledge. Natural Language Processing (NLP) is an integral component in countless information systems requiring advanced manipulation of natural language. In this class, students will be introduced to NLP starting with the concept of understanding words in context and the need for natural language processing in the business environment. The discussion continues with a detailed study of words and is a foundational framework supporting phonetics and speech synthesis. Subsequent topics include concepts of how words are grouped together to form unique grammatical units. The last part of the course, explores solving real-world NLP problems and deals with two key areas: corpus building, feature engineering, and application development. Course material is presented via theory-based lectures, group discussion, and practical labs-a culminating research project will be individually crafted. Prerequisites: EGR 120 or 121, and one of the following: EGR 305, MAT 353, STA 144, 310, or one additional approved statistics course. (3 units; Spring)

This course provides an introduction to the field of Security in computing. Topics include common security standards and policies, cryptography and information security, access controls, attacks and countermeasures, and computer forensics. Prerequisite: EGR 222. (3 units; Spring)

This course provides an in-depth look into the fundamentals of packet-switched network traffic analysis at the network layer and above as applied to problems in traffic engineering, economics, security, etc. The course will explore the design and integration of analytic tools and techniques into the fabric of the network including: spatial and temporal anomaly detection, origin-destination matrix estimation, application mix determination, deep-packet inspection, fingerprinting, intrusion detection and insider threat mitigation. Finally, the course covers active defense and offensive methods reliant on traffic analysis. Contains a lab component. Prerequisite: CSC 431. (3 units; Spring)

This course provides an in-depth look to define the nature and scope of cyber security incident handling services, including intrusion/incident detection, damage control, service continuity, forensic analysis, service/data restoration, and incident reporting. Material covers policy, planning, operations, and technology issues involved in related cyber incident handling plans; i.e., Business Continuity, Disaster Recovery, and Continuity of Operations. Specific incident types addressed include, natural disasters, denial of service, malicious code, malicious misuse of hardware and firmware, unauthorized access, data compromise and inappropriate use, including insider attacks. Emphasis is given to the detection and analysis of infiltration and exfiltration techniques employed during cyber attacks, thus enabling the incident handler to detect low noise attacks, and to deconstruct particularly insidious attacks. Contains a lab component. Prerequisite: CSC 431 (3 units; Fall)

This course provides an advanced course that focuses on key principles of a constructive approach to secure systems. A brief review of operating systems and computer architecture is provided. Major topics include threat characterization and subversion; confinement; fundamental abstractions, principles, and mechanisms, such as reduced complexity, hierarchical relationships, least privilege, hardware protection, resource management and virtualization, software security, secure system composition, mutual suspicion, synchronization, covert and side-channel analysis, secure metadata, secure operational states, usability, and life cycle assurance. Current developments will include advances in security hardware, components, and systems. This course has heavy reliance on software development and implementation. Prerequisite: CSC 431. (3 units; Spring)

Overview of current database technologies with an emphasis on relational database technology. Introduction to database design, entity relationship diagraming, structured query language, and stored procedures. Prerequisite: EGR 120 or 121. (3 units; Fall)

Focus on Programming real-time applications on an embedded platform running a real-time operating system (RTOS). Consideration will be given to cross-compiled software development, embedded system debugging, multitasking, real-time scheduling, inter-task communication, software design for deterministic execution time, software performance analysis and optimization, device drivers. Prerequisites: EGR 222 and 329. (3 units; Spring)