NTRES 6940

NTRES 6940

Course information provided by the Courses of Study 2018-2019.

The department teaches "trial" courses under this number. Offerings vary by semester and are advertised by the department before the semester starts. Courses offered under the number will be approved by the department curriculum committee, and the same course is not offered more than twice under this number.

When Offered Fall or Spring.

View Enrollment Information

  •   Regular Academic Session. 

  • 1 Credit Sat/Unsat

  • Topic: Current Topics in Non-Model Genomics

  • 17879 NTRES 6940   LEC 001

  • This graduate seminar will take the form of a journal club that meets weekly to discuss the burgeoning literature on non-model genomics, focusing on applications of next-gen sequencing to address ecological and evolutionary questions in non-model organisms (i.e. without the benefit of a high-quality well-annotated reference genome or extensive species-specific annotation information and genomic resources). The topics covered will be determined based on student interest and will include both novel results and methodological questions. Enrollment targeted to graduate students in the Department of Natural Resources and the Department of Ecology and Evolutionary Biology.

Syllabi: none
  •   Seven Week - First. 

  • 1 Credit Sat/Unsat

  • Topic: The Fundamentals of R

  • 18675 NTRES 6940   LEC 002

    • MTF Fernow Hall 210
    • Sep 4 - Sep 10, 2018
    • Sun, C

      Wong, A

  • Program R is a powerful statistical software (and language) for analyzing quantitative and qualitative datasets. It is free and open source, making it accessible, flexible, and powerful for a wide variety of applications. It is used heavily in the sciences and a variety of research fields. This course introduces the breadth of data manipulation and statistical computation possible with R and teaches the fundamentals that allow a basic use of R. Topics will include data manipulation and visualization, logic statements, and basic statistical analyses. The course will reinforce gained skills and comprehension through coding exercises. The course will provide students with a working knowledge of R, the ability to use and master R in the future for their own statistical analyses, and importantly, the skills to solve R coding issues when they inevitably arise. This course is designed for students with no prior knowledge of programming or of R. Students will be required to bring a personal laptop to class. Undergraduates may enroll with instructor permission.