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Course Criteria
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1.00 - 3.00 Credits
Involves a special project where there is a demonstrated need which cannot be met through enrollment in a regularly scheduled course. Also could include special projects of unusual merit in furthering a student's professional goals. Student(s) must be able to sustain framework for developing and enhancing student abilities to do lucid thinking. Requires approval of instructor, division dean, and curriculum committee.
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0.50 Credits
This capstone course for students majoring in the sciences, mathematics, or engineering is intended to broaden their scientific horizons, acquaint them with various educational and career opportunities in their fields, and actively prepare them for transfer to a four-year college or university. Repeatable for credit.
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1.00 - 3.00 Credits
This course is designed to give students an in-depth learning experience in a mathematics related topic. It may include reading assignments, computation (by hand and/or with a calculator/ computer), meetings, group discussions, group work, and excursions to pertinent sites.
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1.00 - 6.00 Credits
An opportunity for majors to apply knowledge and techniques learned in the classroom to an actual job experience. Classroom instruction must precede the experience, or the student must be registered for courses at the same time the student is enrolled in the work experience.
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1.00 - 6.00 Credits
An opportunity for majors to apply knowledge and techniques learned in the classroom to an actual job experience. Classroom instruction must precede the experience, or the student must be registered for courses at the same time the student is enrolled in the work experience.
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1.00 - 6.00 Credits
An opportunity for majors to apply knowledge and techniques learned in the classroom to an actual job experience. Classroom instruction must precede the experience, or the student must be registered for courses at the same time the student is enrolled in the work experience.
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3.00 Credits
This is a first course in statistics for STEM majors. Topics will include probability, discrete and continuous distributions, descriptive statistics, and statistical inference (confidence intervals and hypothesis testing, including linear regression and one-way ANOVA). Proficiency with integral calculus is required.
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3.00 Credits
Students will get an introduction to Python programming, data analysis tools, and the necessary statistics to acquire, clean, analyze, explore, and visualize data real-life data sets. Using statistics, students will learn to make data-driven inferences and decisions, and to communicate those results effectively.
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3.00 Credits
sStudents will learn to efficiently find structures and patterns in large data sets. Topics will include acquiring data sets and cleaning messy and noisy raw data sets into structured and abstract forms; applying scalable and probabilistic algorithms to these well-structured abstract data sets; and, formally modeling and analyzing the error inherent in these methods. Students will consider data representations and trade-offs between accuracy and scalability.
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3.00 Credits
This course in discrete mathematics covers Boolean algebra, logic and proof, sets and relations, functions, induction, recursion, enumerative combinatorics, elements of number theory, and graph theory.
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