NTRES 3500

NTRES 3500

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

As data sets grow larger and more complex, computational skills are now in high demand across all areas of science. This course introduces a series of practical tools that enable scientists to spend less time wrestling with software and more time getting research done in efficient and powerful ways. Topics covered include  1) formatting, visualizing, and filtering complex datasets, 2) automating repetitive tasks and combining tools for implementing analysis pipelines, 3) basic programming for building and testing custom tools, and 4) best practices for reproducible science workflows. We will primarily work in R and the Unix command line environment, and the course will be structured around hands-on (the keyboard) learning.

When Offered Fall.

Prerequisites/Corequisites Prerequisite: one college-level statistics course (e.g. STSCI 2150, or AEM 2100) and basic familiarity with the R statistical computing environment, or permission of the instructor.

  • Students will be able to describe and classify different data sources, types, formats, and structures.
  • Students will be able to compile, re-format, and annotate complex data sets for efficient and reproducible downstream analysis.
  • Students will be able to automate repetitive tasks and batch process files through the command-line interface and in R.
  • Students will be able to use shell scripting to build analysis pipelines that combine multiple programs in modular workflows.
  • Students will be able to explain basic programming concepts and design, code, and test small programs that complete a specified task.
  • Students will be able to design and implement strategies for reproducible science (including version control and data archiving).

View Enrollment Information

Syllabi: none
  •   Regular Academic Session.  Choose one lecture and one laboratory.

  • 3 Credits Stdnt Opt

  • 17427 NTRES 3500   LEC 001

  • 17428 NTRES 3500   LAB 401