This course is offered by the Oxnard College Cisco Networking Academy. It covers foundation-level computer networking including cabling, network topologies, local area networks (LANs), Ethernet, switching, routing, IPv4 and IPv6 addressing, TCP/IP protocols, and the Open Systems Interconnection (OSI) Model. The fundamentals of wireless networking and network security are also covered in this course. Through hands-on labs, students will develop practical networking skills and learn to troubleshoot common network issues. This is one of the essential courses to take on the pathway toward preparation for the Cisco CCNA certification and is an appropriate first course to take in the CNIT program. Field trips may be required. Transfer credit: CSU. Formerly: ENGT R120.

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. This course covers descriptive and inferential statistics for students of social sciences, science, education, business, and engineering. Included are discussions of graphing and interpreting graphs, measures of the center and variation, binomial and normal probability, hypothesis testing using a variety of standard tests. This course uses technology to analyze data sets. Credit will not be awarded for both the honors and regular versions of a course. Credit will be awarded only for the first course completed with a grade of "C" or better or "P". Prerequisites: Course taught at the level of intermediate algebra or placement as determined by the college’s multiple measures assessment process. Transfer credit: CSU; UC. Notes: Prior to Common Course Numbering, this course was known as MATH R105.

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. This course covers descriptive and inferential statistics for students of social sciences, science, education, business, and engineering. Included are discussions of graphing and interpreting graphs, measures of the center and variation, binomial and normal probability, hypothesis testing using a variety of standard tests. This course uses technology to analyze data sets. Credit will not be awarded for both the honors and regular versions of a course. Credit will be awarded only for the first course completed with a grade of "C" or better or "P". Prerequisites: Course taught at the level of intermediate algebra or placement as determined by the college’s multiple measures assessment process. Transfer credit: CSU; UC. Notes: Prior to Common Course Numbering, this course was known as MATH R105.

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. This course covers descriptive and inferential statistics for students of social sciences, science, education, business, and engineering. Included are discussions of graphing and interpreting graphs, measures of the center and variation, binomial and normal probability, hypothesis testing using a variety of standard tests. This course uses technology to analyze data sets. Credit will not be awarded for both the honors and regular versions of a course. Credit will be awarded only for the first course completed with a grade of "C" or better or "P". Prerequisites: Course taught at the level of intermediate algebra or placement as determined by the college’s multiple measures assessment process. Transfer credit: CSU; UC. Notes: Prior to Common Course Numbering, this course was known as MATH R105.