Fall 2012 Web site -- next offering: Fall 2014


Class Periods: Wed. 9:30-11:20 am
Locations: CNR 026 (GIS Lab)
Web Site: http://webpages.uidaho.edu/phiguera/FOR504_2012/

Department of Forest, Rangeland, and Fire Sciences
College of Natural Resources
University of Idaho



Recent Announcements

  • End-of-course logistics: You have two more assignments due for this course: (1) To help improve this course in the future, please fill out the course evaluation form and provide feedback on what ...
    Posted Nov 28, 2012, 12:51 PM by Philip Higuera
  • Materials from Wk 1 linked to from calendar The in-class presentation and a web page with Matlab code from in-class demonstrations are linked to from the calendar.
    Posted Aug 22, 2012, 8:52 PM by Philip Higuera
Showing posts 1 - 2 of 2. View more »


Instructor 

Philip Higuera, Assistant Professor

Office: CNR 204B

Office Hours: Wed., Thur, 1:00-2:20 pm                *Make an appointment here*

E-mail:  phiguera[at]uidaho.edu
   (Include "FOR 504" in subject)

Phone Number:  208-885-6024



Course Rationale

Accessing and analyzing data in a computer programming environment is a key skill needed by graduate students and professionals in natural sciences. Learning a programming language will fundamentally change your ability to think quantitatively, understand datasets, test alternative hypotheses, and utilize existing data relevant to ecological and natural resource sciences.

Goals

The objectives of this course are to teach students (1) the basic skills needed to access, manipulate, and visualize (large) datasets, and (2) how to perform, automate, and customize data analysis using command-line computer programming. Although not a formal statistics course, we will cover both basic and more advanced statistical methods throughout the semester. The course will focus on skills applicable to multiple computer languages, including R, S-plus, and Python, but it will primarily utilize the program Matlab for teaching and examples.

Format

Two-hour class periods will consist of approximately 25% lecture, 75% hands-on computer work. Students are encouraged to use their own datasets throughout most of the course. Exercises and on-line video tutorials will be used to prepare for and cover weekly topics for the first 8-10 weeks. The remaining weeks will be dedicated to semester projects, focusing on a dataset relevant to individual students or groups of students (your choice!).



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