UC Santa Barbara General Catalog

(1) FRANKLIN

Overviews the potential of, and opportunities available from, the field of computer science. Topics include an overview of how computers work and the interesting ways in which computers can be applied to solve important and high-impact technological, social, and cutting-edge research problems.

(4) EGECIOGLU
Prerequisite: Math 4A with a grade of C or better.

Introduction to the central concepts of computer science as they apply to a wide variety of human endeavors, including natural and physical sciences. Topics that interrelate central ideas from algorithms, performance and complexity, data structures, concurrency, languages and abstractions will be studied.

(4) KOC

An introduction to computational thinking, computing, data management, and problem solving using computers, for non-majors. Topics include coding basics, representing code and data using a computer, and applications of computing that are important to society.

(4) STAFF

Enrollment Comments: Not open for credit to students who have completed Computer Science 8, Computer Science 16, Engineering 3, ECE 3, or equivalent (including AP CS A with a score of 4 or higher).

Introduction to data science methods and Python programming language for students with little to no experience in the subjects. Topics include foundational programming concepts, problem-solving strategies and code design, and data science concepts as table operations, exploratory data analysis, basic probability.

(4) STAFF
Prerequisite: Computer Science 5A or Computer Science 8 or ECE 3 with a grade of C or better.

Students explore the data science lifecycle, including question formulation, data collection and cleaning, exploratory data analysis and visualization, statistical inference and prediction, and decision-making. The course focus is on transforming and analyzing data; machine learning methods including regression, classification and clustering; principles behind data visualizations; concepts of measurement error and prediction; and techniques for scalable data processing.

(4) MIRZA

Enrollment Comments: Not open for credit to students who have completed Computer Science 16 or Engineering 3 or ECE 3.

Introduction to computer program development for students with little to no programming experience. Basic programming concepts, variables and expressions, data and control structures, algorithms, debugging, program design, and documentation.

(4) R WANG
Prerequisite: Computer Science 8 or Engineering 3 with a grade of C or better.

Intermediate topics in Computer Science using the Python programming language. Topics include object oriented programming, runtime analysis, data structures, and software testing methodologies.

(1) STAFF
Prerequisite: Knowledge of at least one programming language.

Enrollment Comments: Different sections may be repeated. Sections not always offered.

A self-paced course to allow a student who already possesses a working knowledge of at least one programming language an opportunity to learn other languages of interest. Each section studies a different programming language.

(4) MIRZA, KHARITONOVA
Prerequisite: Mathematics 3A or 2A with a grade of C or better (may be taken concurrently), CS 8 or Engineering 3 or ECE 3 with a grade of C or better, or significant prior programming experience.

Repeat Comments: Legal repeat of CMPSC 10.

Fundamental building blocks for solving problems using computers. Topics include basic computer organization and programming constructs: memory CPU, binary arithmetic, variables, expressions, statements, conditionals, iteration, functions, parameters, recursion, primitive and composite data types, and basic operating system and debugging tools.

(4) COSTANZO
Prerequisite: Computer Science 16 with a grade of C or better; and Mathematics 3B or 2B with a grade of C or better (may be taken concurrently).

Enrollment Comments: Not open for credit to students who have completed Computer Science 20.

Repeat Comments: This is a legal repeat of CMPSC 24A.

Intermediate building blocks for solving problems using computers. Topics include intermediate object-oriented programming, data structures, object-oriented design, algorithms for manipulating these data structures and their run-time analyses. Data structures introduced include stacks, queues, lists, trees, and sets.

(4) WANG, RICHERT
Prerequisite: Computer Science 16 with a grade of C or better; and Mathematics 3B or 2B with a grade of C or better (may be taken concurrently).

Enrollment Comments: Not open for credit to students who have completed Computer Science 20.

Repeat Comments: This is a legal repeat of CS 24.

Intermediate building blocks for solving problems using computers. Topics include data structures, object-oriented design and development, algorithms for manipulating these data structures and their runtime analyses. Data structures introduced include stacks, queues, lists, trees, and sets.

(4) HOLLERER
Prerequisite: Computer Science 24 with a grade of C or better

Repeat Comments: Computer Science 32 is a legal repeat for Computer Science 60

Advanced topics in object-oriented computing. Topics include encapsulation, data hiding, inheritance, polymorphism, compilation, linking and loading, memory management, and debugging; recent advances in design and development tools, practices, libraries, and operating system support.

(5) SU, VAN DAM
Prerequisite: Computer Science 16 with a grade of C or better and Mathematics 4A with a grade of C or better.

Introduction to the theoretical underpinnings of computer science. Topics include propositional predicate logic, set theory, functions and relations, counting, mathematical induction and recursion (generating functions).

(4) ZHENG, FRANKLIN
Prerequisite: Computer Science 16 with a grade of C or better; and Mathematics 3C or 4A with a grade of C or better.

Enrollment Comments: Not open for credit to students who have completed ECE 15 or ECE 15B or Computer Science 30.

Repeat Comments: Course counts as a legal repeat of CMPSC 30.

Assembly language programming and advanced computer organization; Digital logic design topics including gates, combinational circuits, flip-flops, and the design and analysis of sequential circuits.

(4) STAFF
Prerequisite: Consent of instructor.

Enrollment Comments: May be repeated for credit to a maximum of 32 units provided letter designations are different.

Provides for the study of topics of current interest in computer science: A. Foundations; B. Software Systems; C. Programming Languages and Software Engineering; D. Information Management; E. Architecture; F. Networking; G. Security; H. Scientific Computing; I. Intelligent and Interactive Systems; N. General.

(2-4) STAFF
Prerequisite: Consent of instructor.

Enrollment Comments: May be repeated for up to 12 units total. Does not apply toward any CS major degree requirements.

Research opportunities for undergraduate students. Students will be expected to give regular oral presentations, actively participate in a weekly seminar, and prepare at least one written report on their research.

(1-4) STAFF

Enrollment Comments: Must have a minimum 3.0 grade point average. May be repeated. Students are limited to 5 units per quarter and 30 units total in all 99/198/199 courses combined.

Independent studies in computer science for advanced students.

Designed to train outstanding undergraduates for learning assistant positions in CS courses. Lecture/discussion surveys current research and best practices in CS pedagogy including student development theories, different pedagogical techniques, and methods for assessing learning. Students gain experience working one-on-one with students, fostering positive learning environments, and providing feedback on student work. Students who successfully complete this course will earn units by serving as an apprentice undergraduate learning assistant.

Defining a CS research problem, finding and reading technical papers, oral communication, technical writing, and independent learning. Course participants work in teams as they apprentice with a CS research group to propose an original research problem and write a research proposal.

Introduction to the numerical algorithms that form the foundations of data science, machine learning, and computational science and engineering. Matrix computation, linear equation systems, eigenvalue and singular value decompositions, numerical optimization. The informed use of mathematical software environments and libraries, such as python/numpy/scipy.

Data structures and applications with proofs of correctness and analysis. Hash tables, priority queues (heaps); balanced search trees. Graph traversal techniques and their applications.

Design and analysis of computer algorithms. Correctness proofs and solution of recurrence relations. Design techniques; divide and conquer, greedy strategies, dynamic programming. Applications of techniques to problems from several disciplines. NP - completeness.

Formal languages; finite automata and regular expressions; properties of regular languages; pushdown automata and context-free grammars; properties of context-free languages; introduction to Turing machines and computability.

Fundamentals of high performance computing and parallel algorithm design for numerical computation. Topics include parallel architectures and clusters, parallel programming with message-passing libraries and threads, program parallelization methodologies, parallel performance evaluation and optimization, parallel numerical algorithms and applications with different performance tradeoffs.

Team-based project development. Topics include software engineering and professional development practices, interface design, advanced library support; techniques for team oriented design and development, testing and test driven development, and software reliability and robustness. Students present and demonstrate final projects.

Issues in interfacing computing systems and software to practical I/O interfaces. Rapid response, real-time events and management of tasks, threads, and scheduling required for efficient design of embedded software and systems is discussed. Techniques for highly constrained systems.

Introduction to the architecture of computer systems. Topics include: central processing units, memory systems, channels and controllers, peripheral devices, interrupt systems, software versus hardware tradeoffs.

Advanced application programming using a high-level, virtual-machine-based language. Topics include generic programming, exception handling, automatic memory management, and application development, management, and maintenance tools, third-party library use, version control, software testing, issue tracking, code review, and working with legacy code.

Study of the structure of compilers. Topics include: lexical analysis; syntax analysis including LL and LR parsers; type checking; run-time environments; intermediate code generation; and compiler-construction tools.

Concepts of programming languages: scopes, parameter passing, storage management; control flow, exception handling; encapsulation and modularization mechanisms; reusability through genericity and inheritance; type systems; programming paradigms (imperative, object-oriented, functional, and others). Emerging programming languages and their development infrastructures.