The projected schedule is subject to change at any time due to unplanned changes in instructor availability and/or course demand. If you use this information for schedule and graduation planning, account for this risk by having backup plans and by taking required courses as soon as you can.
Fall Quarter
- CS 101
- CS 209
- CS 210
- CS 211
- CS 300
- CS 331
- CS 481
- CS 310
- CS 460
- Additional CS Elective(s)
Winter Quarter
- CS 101
- CS 209
- CS 210
- CS 211
- CS 300
- CS 320
- CS 351
- CS 360
- CS 482
- Additional CS Elective(s)
- MATH 301
Spring Quarter
- CS 101
- CS 209
- CS 210
- CS 211
- CS 212
- CS 250
- MATH 270
- CS 310 (sometimes)
- CS 401
- CS 410
- CS 483
- CS 495
- Additional CS Elective(s)
Summer Quarter
- CS 210
- CS 211
No upper-division CS courses (300- and 400-level) are offered in summer quarter
Note:
- A few CS elective courses are offered each regular quarter (Fall, Winter, Spring), including at least one data science elective. The program typically surveys students to determine which electives to offer, and each course is usually available only once per year.
- Calculus (MATH& 151, 152, 153) and Linear Algebra (MATH 208) are offered every quarter, including summer. MATH 301 is only offered in Winter quarter, and MATH 270 is only offered in Spring quarter.
Course Descriptions
Details for additional CS courses can be found on the college catalog or the course catalog section on ctcLink (Class Search > Class Information > Course Catalog).
CS 101: Technology and Computer Science 5 cr.
Introduces concepts of computer science through development of fluency in modern technology, while offering students an opportunity to increase skills in a variety of information systems. Computer lab work includes operation of computers on networks, programming fundamentals, logical reasoning, web searching, multimedia applications, basic spreadsheets, and database manipulation. Prerequisite: MATH 98 with a C or better, or placement into MATH 99 or higher.
CS 209: Introduction to Computer Programming 5 cr.
This course builds the foundation for core concepts in computer programming for students with no prior programming experience. Students learn how to develop programs in a modern programming language with emphasis on computer science fundamentals and problem solving. Students learn current industry standards for testing and debugging different solutions for scientific and technical problems. Prerequisite: MATH 141 (or higher), or placement by assessment in MATH 142 or above, or entry code.
CS 210: Fundamentals of Computer Science I 5 cr.
This course introduces core concepts in computer science, focusing on programming and problem-solving skills. Students learn to design and implement algorithms while exploring object-oriented programming (OOP) principles such as encapsulation, polymorphism, and inheritance. They gain hands-on experience with Java classes, 2-D and multi-dimensional arrays, interfaces, and the Java Collections Framework, including Lists, Sets, Maps, and ArrayList. The course also emphasizes unit testing. Recommended: CS 209 or prior programming experience. See the CS2xx self Placement test on the website: https://www.bellevuecollege.edu/cs/
CS 211: Fundamentals of Computer Science II 5 cr.
This course focuses on advanced concepts in algorithm efficiency, runtime analysis, and data structures. Topics include recursion, exceptions, search and sorting algorithms, stacks, queues, linked lists, trees, priority queues, and hashing. Students will implement efficient solutions using industry-standard APIs, analyze performance using Big-O notation, and develop a deeper understanding of algorithmic design and problem-solving techniques.
CS 212: C++ Data Structures 5 cr.
Completes one year sequence with data structures using C++, including lists, hash tables, stacks, queues, trees, and graphs. Contrasts the implementations of such data structures in different languages, specifically the differences between pointers versus references, templates versus generics, dynamic versus static memory allocation, multiple inheritance, and destructors. Prerequisite: CS 211.
CS 250: Management Information Systems 5 cr.
Provides basic concepts of information technology in modern business. Topics include data warehouses, decision support systems, electronic commerce, systems development, and risk management. Labs introduce intermediate spreadsheet and database applications in a networked environment. Requires experience with computer databases.
CS 300: Data Structures 5 cr.
This course is an introduction to the fundamental concept of data structures. It explains how to organize and store data efficiently using data structures and how to select appropriate data structures. The course further focuses on understanding the fundamental algorithms and analyzing the time and space complexity of these algorithms.
CS 320: Programming Languages 5 cr.
This course is an introduction to the design and implementation of programming languages. The course explores organization and structure of programming languages, run?time behavior and requirements of programs, and programming language specification. The course teaches the programming models underlying different programming paradigms such as functional, logic, scripting and object-oriented languages. Prerequisites: CS 300 with a C or better, and admission to BC CS program, or instructor’s permission.
CS 331: Database Systems 5 cr.
The course covers the fundamental concepts of database systems. It teaches students the internals of database systems including data model, database design, relational model, relational algebra, SQL, indexing, concurrency control, query processing, transaction management and recovery. This course also aims to teach the new directions involving NoSQL persistence models.
CS 351: Computer Architecture I 5 cr.
This course introduces the functional components of modern computer systems (processor, memory, Input/Output, etc.), characteristics and performance of these components. The course also addresses the interactions among hardware and software components. This course further allows students to develop programming skills while learning computer architecture with assembly programming assignments.
CS 360: Operating Systems 5 cr.
This class introduces the design and implementation of modern, process oriented operating systems, as well as systems programming basics. Primary topics include operating system structure, processes, threads, synchronization, memory management, virtual memory, file systems, I/O subsystem and device management. Prerequisite: CS 351 with a C or better and admission to BS CS program, or instructor’s permission.
CS 401: Algorithms 5 cr.
This course teaches the concepts and skills required to design, implement and analyze algorithms for constructing efficient computer programs. The course covers elementary data structures, searching, sorting, graph and string algorithms, and algorithm design principles such as dynamic programming, greedy, divide-and-conquer paradigms. The emphasis is on applications and scientific performance analysis of algorithms. Prerequisites: CS 300 with a C or better, MATH 301 and admission to BC CS program, or instructor’s permission.
CS 410: Software Engineering 5 cr.
The course teaches the fundamental concepts and principles of software engineering, its tools and techniques, and methods for building reliable software systems. This course introduces all phases of the lifecycle of a software system, including requirements elicitation and analysis, design, implementation, integration, testing, verification and validation, deployment, and maintenance.
CS 481: Senior Capstone I 3 cr.
This course focuses on literature review, requirement specification, project management, initial design and prototyping of the three-quarter long computer science project. Students work in teams and are given milestones. The course includes lectures, reading assignments and guest speakers on development process, team working, report writing and emerging trends in computer science. Prerequisites: CS 410 with a C or better and admission to BS CS program, or instructor’s permission.
CS 482: Senior Capstone II 4 cr.
This course is the second in a sequence of three senior level capstone courses. This course focuses on detailed design, test plan and implementation of the project. The course includes lectures, reading assignments and guest speakers on development process, test plan, ethics, legal issues, and emerging trends in computer science. Prerequisites: CS 481 with a C or better and admission to BS CS program, or instructor’s permission.
CS 483: Senior Capstone III 3 cr.
This course is the third in a sequence of three senior level capstone courses. This course focuses on implementation, test and presentation of the project. The course includes lectures, reading assignments and guest speakers on poster design, innovation and entrepreneurship, presentation skills and emerging trends in computer science.
CS 310: Python for Data Science 5 cr.
This course covers the basics of the Python language, and then quickly moves to topics related to data manipulations and analysis. Subjects covered are the most common libraries used in data pre-processing and visualizations, using a coding environment that allows to mix code, text, and visualizations elements. Prerequisite: Admission to BS CS program, or instructor’s permission.
CS 460: Machine Learning 5 cr.
This course is an introduction to the fundamentals and applications of machine learning. The course provides students with the opportunity to have theoretical knowledge and practical experience on basic concepts of machine learning with programming assignments. The course focuses on fundamentals, not on providing mastery of specific commercially available tools. Prerequisites: CS 300 with a C or better, MATH 208, MATH 270 and admission to BS CS program, or instructor’s permission.
CS 495: Data Science Project Practicum 5 cr.
This course focuses on applying technological methodologies and theories to real-world scenarios within the realm of Data Science. Emphasizing hands-on experience, problem-solving, critical analysis, and the application of industry-standard practices, alongside collaborative teamwork within the context of Data Science applications. Additionally, students have the option to apply for course credit equivalent to an internship, offering practical industry exposure.
Details for additional CS courses can be found on the college catalog or the course catalog section on ctcLink (Class Search > Class Information > Course Catalog).
MATH 270: Probability and Statistical Models 5 cr.
Provides a rigorous introduction to the fundamental principles of probability with emphasis on applications to data-driven problem solving. Starting from an axiomatic definition of probability, students learn how to work with both discrete and continuous random variables and apply these concepts to practical situations. Topics include: conditional probability, Bayes¿ theorem; Bernoulli, binomial, geometric, Poisson, uniform (discrete and continuous), normal, and exponential distributions; the law of large numbers; the central limit theorem and its applications; confidence intervals; and the Z-test. A portion of coursework will include techniques and examples in the Python programming language. Recommended: MATH&153, CS 310, or familiarity with Python
MATH 301: Discrete Mathematics 5 cr.
This class introduces basic discrete structures in mathematics, computer science and engineering fields. Topics include elementary logic, set theory, mathematical proof, relations, combinatorics, induction, recursion, sequence and recurrence, trees, graph theory.
Details for additional MATH courses can be found on the college catalog or the course catalog section on ctcLink (Class Search > Class Information > Course Catalog).
Last Updated May 26, 2026