This course introduces students to application of software engineering techniques to the design and development of large programs; data abstraction techniques and structures and associated algorithms. It covers object-oriented programming paradigm, focusing on the definition and use of classes along with the fundamentals of object-oriented design. Other topics include an overview of programming language principles, simple analysis of algorithms, basic data structures, searching and sorting techniques. This is the second course in a sequence of courses that is compliant with the standards of the Association for Computing Machinery (ACM). Prerequisites: CS V11 or equivalent. Transfer credit: CSU; UC.

In this course, students receive instruction in academic reading and writing, including writing processes, effective use of language, analytical thinking, and the foundations of academic research. This course takes a process-based approach to developing academic writing and critical reading skills. Students practice generating ideas, drafting, and revising to communicate effectively across audiences and purposes. Working with both low-stakes assignments and formal academic genres such as synthesis-driven arguments, literature reviews, and analytical essays, students develop analytical and collaborative skills through active engagement in the writing process. Students transform writing between scholarly and popular modes while integrating diverse sources as evidence. Through structured peer review and instructor feedback, students develop reading and revision strategies for writing across disciplines. The course includes multiple formal writing assignments totaling 5000 words. Notes: Prior to Common Course Numbering, this course was known as ENGL V01A. Prerequisites: Placement as determined by the college's multiple measures assessment process. Transfer credit: CSU; UC. Formerly: ENGL 1A.

This course is an introduction to statistical thinking and processes, including methods and concepts for discovery and decision-making using data. Topics include descriptive statistics; probability and sampling distributions; statistical inference; correlation and linear regression; analysis of variance, chi-squared, and t-tests; and application of technology for statistical analysis including the interpretation of the relevance of the statistical findings. Students apply methods and processes to applications using data from a broad range of disciplines. Successful completion of this course fulfills the mathematics competency requirement for the AA/AS degree. Notes: Prior to Common Course Numbering, this course was known as MATH V44. Prerequisites: Placement as determined by the college's multiple measures assessment process or completion of a course taught at or above the level of intermediate algebra. Transfer credit: CSU; UC. Formerly: MATH V44; Math 44.