Students enrolled in this course attend weekly pedagogy sessions led by the instructors and facilitate a weekly PLTL session (1 hour) for a pre-assigned group of 8 to 10 students from CHEM 111 or CHEM 112. This course trains students to become effective leaders for Peer-Led Team Learning (PLTL) groups in the large enrollment general chemistry courses. The weekly pedagogy sessions will equip students with collaborative learning strategies, group facilitation techniques, leadership skills, and communication skills.

Students enrolled in this course attend weekly pedagogy sessions led by the instructors and facilitate a weekly PLTL session (1 hour) for a pre-assigned group of 8 to 10 students from CHEM 111 or CHEM 112. This course trains students to become effective leaders for Peer-Led Team Learning (PLTL) groups in the large enrollment general chemistry courses. The weekly pedagogy sessions will equip students with collaborative learning strategies, group facilitation techniques, leadership skills, and communication skills.

Students enrolled in this course attend weekly pedagogy sessions led by the instructors and facilitate a weekly PLTL session (1 hour) for a pre-assigned group of 8 to 10 students from CHEM 111 or CHEM 112. This course trains students to become effective leaders for Peer-Led Team Learning (PLTL) groups in the large enrollment general chemistry courses. The weekly pedagogy sessions will equip students with collaborative learning strategies, group facilitation techniques, leadership skills, and communication skills.

Description not available at this time

The goal of this course is to introduce fundamentals of probability theory, statistical inference tools and their application to biostatistics and health data science. The course is intended for first-year graduate students in Biostatistics MS program and students who are interested in learning probability and statistical inference.

R has emerged as a preferred programming language in data science. This course covers an introduction to topics in R programming to develop powerful, robust, and reusable data science tools. Main topics include importing of data, data wrangling, visualization, and reporting.

This course is an introductory workshop in 16mm single-camera filmmaking, linear editing, and film projection intended for students interested in pursuing further creative production and coursework in film, especially toward completion of the Major or Certificate in Film Studies. Because the skills utilized in analog filmmaking can build upon but are in part discrete from those learned in video production, most students will have prior experience with photography or videography, though this is not strictly required.

This is a full-year course. Prerequisite: MUS 100 or equivalent. Credits: 4, at the completion of two semesters. Department application required.