1001. Introduction to Academics in the Sciences (1 s.h.) F.

(Formerly: SCI TEC 0051.)

Preparation for the academic requirements of science curricula, and development of the skills necessary to succeed in college. A guided exploration of potential career opportunitites. There will be guest lecturers, including representatives from each of the departments in the College of Science and Technology.

3385. Diamond Peer Teachers - Internship I (1 s.h.) F.

Prerequisite: Permission of instructor required.

The Diamond Peer Teachers Program provides students with a mentored university-level teaching experience in their major. Course requirements include participation in the three-day pre-semester Teaching Institute and regular participation in the Peer Teachers support group throughout the semester. Peer Teachers provide supplemental instruction in first- and second-year courses, promote student engagement, and model successful study habits and academic preparedness for students with whom they work. For Diamond Peer Teachers only.

3386. Diamond Peer Teachers - Internship II (1 s.h.) S.

(Formerly: SCI TEC 0386.)

Prerequisite: Permission of instructor required.

The Diamond Peer Teachers Program provides students with a mentored university-level teaching experience in their major. Course requirements include participation in the three-day pre-semester Teaching Institute and regular participation in the Peer Teachers support group throughout the semester. Peer Teachers provide supplemental instruction in first- and second-year courses, promote student engagement, and model successful study habits and academic preparedness for students with whom they work. For Diamond Peer Teachers only.

4351. Paradigms of Scientific Knowledge: Knowledge Discovery from Scientific Data (3 s.h.)

Prerequisite: A one-semester programming course (Matlab, C, Java, Basic).

The goal of this course is to teach science students the necessary skills for knowledge discovery from large collections of data. The course is designed to introduce students to various data mining algorithms and illustrate how they can be applied to real-life knowledge discovery problems in sciences. Cas studies will be discussed that illustrate application of data mining systems in biology, chemistry, and physics. One emphasis of the course will be in teaching students how to use various data mining software to solve various knowledge discovery problems. Through this course, students will develop abilities in problem solving and critical thinking, both of which are necessary for the practice of data mining. This will be accomplished by demonstrating the practical applications of each presented algorithm, by emphasizing each algorithm's limitations, and by assigning research-like course projects.