Skip to Content

Computer Science

Computer Science Courses

COMP 025. Computers and Information Processing. 4 Units.

This introductory information technology course focuses on computer architecture, networking, internet technologies and the integration of productivity software. Lectures, readings, hands-on projects and lab assignments give a variety of learning experiences. Specific topics include computer architecture, digital data, networking, file management, spreadsheets, database systems and presentation applications. Students are exposed to JavaScript and Visual Basic scripting. Particular emphasis is placed on HTML programming and creating an interactive student website for homework and lab linking throughout the semester. Prerequisite: Fundamental Math Skills requirement. (GE3B)

COMP 041. Great Ideas in Computing. 4 Units.

This course is a broad introduction to the field of computing. The concepts that are the foundation of computing are presented and placed in historical context. Discussion topics include the ways of thinking and working that make computing effective, and the future of the field. Example topics include number representation, architecture of computing systems, intelligent computing systems, and the use of computing in art and games. Prerequisite: Fundamental Math Skills requirement. (GE3C)

COMP 047. Discrete Math for Computer Science. 4 Units.

This course is designed to develop skills in deductive reasoning and to apply concepts of discrete mathematics to computer science. Topics include logic, deductive reasoning, mathematical induction, set theory, functions, recurrence relations, combinatorics and probability, graphs, trees, and Boolean Algebra. Prerequisite: Fundamental Math Skills requirement. (Spring, every year). (GE3B)

COMP 051. Introduction to Computer Science. 4 Units.

The course emphasizes program design and problem solving techniques that use a high-level programming language. The course introduces basic concepts such as assignment, control flow, iteration, and basic data structures in addition to a supervised lab. Credit for this course is not given if a student has credit for COMP 061. Prerequisite: Fundamental Math Skills requirement. (GE3B)

COMP 053. Data Structures. 4 Units.

The course continues the development of program design and problem solving techniques. Topics include development of fundamental data structures and their associated algorithms as well as array-based algorithms, recursion, lists, generics, dynamic memory, binary trees, and associative structures. Prerequisite: COMP 051 or COMP 061 with a "C-" or better.

COMP 055. Application Development. 4 Units.

This course develops the skills and techniques required for the creation of contemporary software applications. Contemporary software applications are complex systems that involve the interaction of multiple subsystems that require teams of developers working together for extended periods of time. Topics include teamwork and communication skills, current development methodologies, analysis and design documentation and the use of libraries. This course is intended to prepare students to transition to upper division courses. Prerequisites: Completion of all Fundamental Skills and COMP 053 with a "C-" or better. (Fall, every year).

COMP 061. Introduction to Programming for Data Science. 4 Units.

This course introduces programming concepts and program design using topics in data science as examples. Basic concepts such as assignment, control flow, iteration, and simple as well as object-oriented data types and structures are developed. The course includes a supervised lab. Credit for this course is not given if student has credit for COMP 051. Prerequisite: Fundamental Math Skills requirement. (GE3B)

COMP 093. Special Topics. 3 or 4 Units.

COMP 127. Web Applications. 4 Units.

The World-Wide Web consists of client-server applications operating over the Internet. This course introduces the skills and techniques for designing and developing web applications. Topics include: client-server architectures, web servers and web browsers, server-side programming, client-side programming, form processing, state management and multimedia. Prerequisites: Completion of all Fundamental Skills and COMP 053 with a "C-" or better or permission of instructor. (Fall, even years).

COMP 129. Software Engineering. 4 Units.

Students gain practical experience in dealing with medium to large scale software systems. Students learn how current analysis and design methodologies are used to develop the abstractions necessary to understand large systems. Students also learn how such methodologies and abstractions are used to communicate with coworkers and clients about the analysis and design. Because communication is an essential skill in large system development, students are expected to produce documents and presentations of professional quality and depth. Prerequisites: Completion of all Fundamental Skills and COMP 055 with a "C-" or better. (Spring, odd years).

COMP 135. Human-Computer Interface Design. 3 Units.

Human-Computer Interface (HCI) Design focuses on the relationship between humans and computers or other physical devices. This course helps students develop an understanding of the common problems in designing these interfaces and presents a set of design techniques to ensure that designs are both useful and useable. Prerequisite: Completion of all Fundamental Skills. Junior standing. (Spring, odd years).

COMP 137. Parallel Computing. 3 Units.

Parallel computing is a science which solves a large problem by giving small parts of the problem to many computers to solve and then combining the solutions for the parts into a solution for the problem. This course introduces architectures and implementation techniques to support parallel computation. Students are expected to design and implement an original parallel application as a term project. Prerequisite: Completion of all Fundamental Skills and COMP 053 with a "C-" or better. Corequisite: ECPE 170. (Spring, even years).

COMP 141. Programming Languages. 4 Units.

Topics in evaluation, design, and development of programming languages. Topics include type systems, variables and scope, functions, parameter passing, data hiding and abstractions, recursion, memory allocation, grammars and parsing, compiler architecture, programming paradigms, and comparison of programming languages and environments. Prerequisites: Completion of Fundamental Skills and COMP 053 with a "C-" or better. (Spring, every year).

COMP 147. Computing Theory. 4 Units.

Students study automata, formal languages and computability. Topics include finite state automata, regular languages, pushdown automata, context-free languages, Turing machines; decidability, reducibility, and time complexity that includes NP-completeness and intractability. Prerequisites: Completion of all Fundamental Skills; COMP 047 or ECPE 071 or MATH 074 with a "C-" or better. (Fall, every year).

COMP 151. Artificial Intelligence. 3 Units.

Students study fundamental concepts, techniques and tools used in Artificial Intelligence. Topics include knowledge representation, search techniques, machine learning and problem solving strategies. Also listed as ECPE 151. Prerequisites: Completion of all Fundamental Skills and COMP 053 with a “C-“ or better. (Fall, odd years).

COMP 153. Computer Graphics. 3 Units.

An introduction to two and three dimensional computer graphics. Basic representations and mathematical concepts, object modeling, viewing, lighting and shading. Programming using OpenGL and other computer graphics applications. Also listed as ECPE 153. Prerequisites: Completion of all Fundamental Skills and COMP 053 with a "C-" or better. (Fall, every year).

COMP 155. Computer Simulation. 4 Units.

This course explores digital simulation, in which a model of a system is executed on a computer. The course focuses on modeling methodologies, mathematical techniques for implementing models, and statistical techniques for analyzing the results of simulations. Students develop simulations using both simulation development toolkits and general-purpose programming languages. Also listed as EMGT 155. Prerequisites: Completion of all Fundamental Skills; MATH 037 or MATH 039; MATH 045 or MATH 051, COMP 051 or ENGR 019 with a "C-" or better. (Fall, even years).

COMP 157. Design and Analysis of Algorithms. 4 Units.

Topics for this course include complexity analysis, algorithms for searching, sorting, pattern matching, combinatorial problems, optimization problems, backtracking, algorithms related to number theory, graph algorithms, and the limitations of algorithm power. Prerequisites: Completion of all Fundamental Skills; COMP 047 or MATH 074; COMP 053; MATH 045 or MATH 051 with a "C-" or better. (Fall, every year).

COMP 159. Computer Game Technologies. 4 Units.

This course surveys the technologies and processes used for modern video game development. Course topics include software engineering, media creation and management, hardware interfaces, user interaction, 3D mathematics and common algorithms and data structures to support graphics, physics and artificial intelligence. Prerequisite: Completion of all Fundamental Skills and COMP 055 with a "C-" or better. (Fall, odd years).

COMP 162. Data Analytics Programming. 4 Units.

This course develops programming skills for computational data analysis. The course emphasizes programming for statistical analysis, machine learning and predictive modeling. Other topics include programming packages for handling, preparation, and manipulation of data, as well as visualization tools for exploration and presentation of data and results. The course emphasizes hands-on data and analysis using a variety of real-world data sets and analytical objectives. Prerequisites: Completion of all Fundamental Skills; COMP 051 or COMP 061.

COMP 163. Database Management Systems. 4 Units.

A database management system (DBMS) is a computer application designed for the efficient and effective storage, access and update of large volumes of data. This course looks at such systems from two perspectives. The user-center perspective focuses on how a DBMS is used to build support for a data intensive application. This perspective includes examination of common data models, query languages and design techniques. The system implementation perspective focuses on the policies, algorithms and data structures used to design and implement a DBMS. Prerequisites: Completion of all Fundamental Skills and COMP 053 with a "C-" or better. Corequisite: COMP 047 or MATH 074. (Spring, even years).

COMP 173. Operating Systems. 4 Units.

Students are introduced to the fundamental concepts of modern operating systems. Topics include an overview of the computer hardware that supports the operating system, process management, threads, and CPU scheduling. Students also study process synchronization that uses primitive and high-level languages, virtual memory management, file systems, system protection, and distributed systems. Prerequisites: Completion of all Fundamental Skills; COMP 053 and ECPE 170 with a "C-" or better or permission of instructor. (Fall, every year).

COMP 175. System Administration and Security. 3 Units.

Students are introduced to an operating system from an administrator's standpoint. Topics include installation with the proper allocation of disk resources, maintaining the operating system and various subsystems, security issues that include server hardening, host firewalls and network security issues. Students also study account administration in a networked environment, change management and intrusion detection. Prerequisites: Completion of all fundamental skills and familiarity with console-based operating systems commands. Junior standing. (Fall, every year).

COMP 177. Computer Networking. 4 Units.

Topics examined in this course include computer networks and the internet, LAN and WAN architectures, and packet switched networks and routing. Students learn about the 7-layer OSI model and internet protocol stack, socket programming and client/server systems, wireless and security. The course includes a laboratory. Also listed as ECPE 177. Prerequisites: Completion of all Fundamental Skills; COMP 053 and ECPE 170 with a "C-" or better. Junior or Senior standing. (Fall, every year).

COMP 178. Computer Network Security. 3 Units.

This course is an examination of the pervasive security threats related to the Internet, data communications and networking. Topics include TCP/IP protocols, authentication, encryption, malware, cybercrime, and social engineering. Emphasis is on computer and network attack methods, their detection, prevention and analysis, and the integration of the tools and techniques employed in this effort. Includes lab. Prerequisites: Completion of all Fundamental Skills and ECPE 170 or COMP 175 with a “C-“ or better. (Spring, every year).

COMP 187. Internship in Computer Science. 1-4 Units.

This internship course offers cooperative employment in a professional computer science environment. The internship requires satisfactory completion of the work assignment and written reports. Prerequisites: Completion of all Fundamental Skills; COMP 055 and ENGR 025 with a "C-" or better. Grading is Pass/No Credit only.

COMP 191. Independent Study. 1-4 Units.

Students create student-initiated projects that cover topics not available in regularly scheduled courses. A written proposal that outlines the project and norms for evaluation must be approved by the department chairperson.

COMP 195. CS Senior Project. 4 Units.

In this course, students synthesize their cumulative computer science knowledge through the development of a computer application. Students will establish design objectives and criteria, analyze solution alternatives and evaluate design performance. Students will then implement, test and evaluate the system. Results will include analysis and design documents, the implemented system, test reports and a presentation and demonstration of the project. Prerequisites: Completion of all Fundamental Skills, Senior Standing, COMP 055 with a “C-“ or better.

COMP 197. Undergraduate Research. 1-4 Units.

Students conduct supervised research that contributes to current active topics in Computer Science. Topics may be selected by the student, related to faculty research, or provided by industrial sponsors. Permission of Undergraduate Research Coordinator.

Students who complete the Bachelor of Science in Computer Science will have acquired the ability to:

  1. Analyze a complex computing problem and to apply principles of computing and other relevant disciplines to identify solutions.
  2. Design, implement, and evaluate a computing-based solution to meet a given set of computing requirements in the context of the program’s discipline.
  3. Communicate effectively in a variety of professional contexts.
  4. Recognize professional responsibilities and make informed judgments in computing practice based on legal and ethical principles.
  5. Function effectively as a member or leader of a team engaged in activities appropriate to the program’s discipline.
  6. Apply computer science theory and software development fundamentals to produce computing-based solutions.

Computer Science Faculty

Michael Doherty, Associate Dean and Associate Professor of Computer Science, 1998, BS Electrical Engineering, University of Florida, 1983; MS Computer Science, University of Rhode Island, 1992; PhD Computer Science, University of Colorado at Boulder, 1998. Computer animation, simulation, video game technology, database management systems, programming languages, computer science education.

Daniel Cliburn, Chair and Professor of Computer Science, 2006, BS Computer Science and Mathematics, Illinois College, 1997; MS Computer Science, University of Kansas, 1999; PhD Computer Science, University of Kansas, 2001. Computer graphics, video game design, virtual reality, computer science education.

Sepehr Amir-Mohammadian, Assistant Professor of Computer Science, 2017, BE Information Technology, Amirkabir University of Technology, 2009; ME Information Technology, Amirkabir University of Technology, 2011; PhD Computer Science, University of Vermont, 2017. Cybersecurity, programming languages, software security assurance.

Michael L. Canniff, Lecturer, 2003, BS Computer Science, University of Minnesota, 1985; MS Computer Science, Syracuse University, 1990. Enterprise Resource Planning systems, database management systems, cloud computing, accounting information systems, data analytics.

Cathy Carlson, Lecturer, 2008, BS, University of the Pacific, 2004; MBA, University of the Pacific, 2005. Business application training and implementation; lead tracking, generation and analytic software; website design.

Jinzhu Gao, Professor of Computer Science, 2008, BS Computer Science and Engineering, Huazhong University of Science and Technology, 1995; MS Mechanical Engineering, Huazhong University of Science and Technology, 1998; PhD Computer and Information Science, The Ohio State University, 2004. Intelligent data visual analytics, data-intensive computing, web and mobile development.

Emma Hayes, Associate Professor of Computer Science, 2007, BS Computer Science, University of Southern California, 2003; PhD Computer Science, University of Southern California, 2007. Artificial Intelligence, multi-agent systems, machine learning, agent-based simulation, computer science education.

Osvaldo Jimenez, Assistant Professor of Computer Science, 2013, BS Computer Science, Stanford University, 2002; MA Learning, Design, & Technology, Stanford University, 2003; PhD Learning Sciences & Technology Design, Stanford University, 2013. Educational games, video game development, software engineering, human-computer interaction, computer science education.

Chadi El Kari, Assistant Professor of Computer Science, 2014, BS Computer Engineering, Université Saint Joseph 2002; MS Computer Science and Engineering, University of Connecticut, 2005; PhD Computer Science and Engineering, University of Connecticut, 2011. Distributed systems, cloud computing, cryptography, combinatorial optimization.

Dana Nehoran, Lecturer, 2017, BS Computer Science, The Technion – Israel Institute of Technology; MS Analytics, University of San Francisco. Higher education analytics, decision sciences, predictive analytics, natural language processing, machine learning.

Jeffrey Shafer, Associate Professor, 2010, BS, Computer Engineering, University of Dayton, 2002; MS, Electrical Engineering, University of Dayton, 2004; PhD, Electrical and Computer Engineering, Rice University, 2010; Computer architecture, Network systems architecture, Data-intensive computing, Cloud computing, Virtualization.

Shon D. Vick, Lecturer, 2017, BA Economics/Mathematics, Rutgers University, 1980; MS Computer Science, Johns Hopkins University, 1988. Applications of intelligent systems in science/engineering, adaptive learning systems in education, and intelligent health.

Afsoon Yousefi-Zowj, Lecturer, 2018, AS Computer Science, Technical Collage of Shiraz, 2006; BE Computer Engineering, Shariaty Technical University, 2008; MS Computer Networks, Amirkabir University of Technology, 2012; MS Computer Science, University of Vermont, 2016; Data analytics, machine learning, sensor networks, mobile development.