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:
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!