Fall 2014

Class Periods: Mon. & Wed., 3:30-5:00 pm
Locations: TLC 241 (Mon.) and CNR 026 (GIS Lab, Wed.)
Web Site: http://webpages.uidaho.edu/phiguera/FOR504/

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

Recent Announcements

  • Assignment 3: Random seedling data - DUE by 3:00 pm, Wed. 24 Sep. Download the dataset randomSeedlingData.csv, from the "Resources" tab, which contains (made up) data on:1.“Site” – id for different sites across space 2.“Class” – id for different “treatments,” for ...
    Posted Sep 18, 2014, 8:44 AM by Philip Higuera
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Philip Higuera, Assistant Professor

Office: CNR 204B

Office Hours: Tue. 1:30-2:50, Wed. 11:00-12:20                   *Make an appointment here*

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

Phone Number:  208-885-6024

Graduate TA

Adam Young, PhD student

Office: 306 Phinny

Office Hours: Mon. 12:30-1:20, Thur. 9:00-10:20

E-mail:  amyoung[at]uidaho.edu

   (Include "FOR 504" in subject)

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 improve your ability to think quantitatively, understand datasets, test alternative hypotheses, and utilize existing data relevant to ecological and natural resource sciences. In short, if you take this class, you will become a better scientist. 


By the end of this course, you will have acquired and developed the skills needed to be able to:

  1. Implement an efficient work flow including accessing, preparing, manipulating, and visualizing (large) datasets using the command-line computer program R or MATLAB
  2. Develop critical thinking and technical skills needed to write scripts and functions to automate and customize data analysis, including data exploration, basic parametric and non-parametric statistics, Monte Carlo methods, and simple model development
  3. Apply best practices in "open science," including accessing code and contributing code developed as part of your research
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, Matlab, and Python, but the primary programs used in the class will be R and Matlab (student's choice -- final decision will depend on makeup of class).  


Class periods will consist of approximately 75% lecture and 25% question / answer / discussion. Lab periods will consist primarily of guided or self-directed lab exercises that reinforce weekly material highlighted in lectures. 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). You should make significant progress on data analysis for your research over the duration of this course!

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