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2023-24 Catalog [ARCHIVED CATALOG]
Courses
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Quarterly Credit Classes are available online, where you may filter class offerings by subject, time, day, or whether they are held on campus, online or are hybrid classes.
& = Common Course Identifier
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Cultural and Ethnic Studies |
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Cybersecurity |
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Dance An asterisk (*) indicates a performance class. Use of performance classes in the distribution area of the Arts & Science transfer degree is limited to 5 credits.
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Data Analytics |
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DATA 410 - Multivariate Analysis 5 CR
Introduce various statistical methods for analyzing more than one outcome variable and understanding the relationships between variables. Topics include a variety of multivariate models such as MANOVA, discriminant functions, canonical correlation, and cluster analysis. The focus will be on real world examples from a variety of sources and using statistical software.
Recommended: DATA 460 or DA 460. Prerequisite(s): MATH 342 with a C or better and DATA 333 or ISIT 333 with a C or better or permission of the instructor.
Course Outcomes - Identify the common multivariate analysis methods, and their advantages and limitations.
- Evaluate the relevant aspects of a real-world data set and choose an appropriate type of multivariate analysis method
- Formulate, fit, and apply models using
Find out when this course is offered
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DATA 420 - Predictive Analytics 5 CR
Previously DA 420. Students will study the process of formulating business objectives, data selection, preparation, and partition to successfully design, build, evaluate, and implement predictive models for a variety of practical business applications. Topics include a variety of predictive models such as classification, decision trees, machine learning, supervised and unsupervised learning.
Recommended: DATA 460 or DA 460. Prerequisite(s): MATH 342 with a C or better and DATA 333 or ISIT 333 with a grade of C or better, or permission of the instructor.
Course Outcomes - Identify the common predictive analytics techniques, and their advantages and limitations.
- Identify common predictive models and classifiers and their applications.
- Evaluate the relevant aspects of a real-world data set and choose an appropriate
Find out when this course is offered
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Database Administration |
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Diagnostic Ultrasound |
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Digital Media Arts |
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