Spring 2023 - EVSC 445 D100
Environmental Data Analysis (4)
Class Number: 3106
Delivery Method: In Person
Introduces environmental scientists to application of modern data analysis methods. This course covers sampling, experimental design, and the analysis of quantitative data collected in the course of environmental monitoring, assessment and restoration programs. Students will be introduced and gain experience with the statistical programming language R.
This course is intended to introduce environmental scientists to the statistical methods that will be useful for them in their work, and provide practical experience therein. This course covers the basic and most useful methods of sampling and experimental design, and the analysis of data collected in observational studies and designed experiments. Particular emphasis will be placed on practical aspects of sampling and experimentation in environmental applications. The course will also include some special topics including, for example, the statistics of environmental impactassessment and those of assessing site reclamation. Examples will be drawn from the literature, and from the instructors own experience. A lab tutorial accompanies the lectures that will include practical examples of the concepts presented in lectures and will give additional support for learning the R programming language.
This course will consist of a weekly 2-hour lecture and a 2-hour interactive software tutorial where students will apply the concepts learned in lectures.
- Homeworks 30%
- Mini Project 10%
- Midterm 1 (open-book) 20%
- Midterm 2 (open-book) 20%
- Final 20%
This course outline is subject to change and the instructor will share the final course outline by the end of the first week of classes.
MATERIALS + SUPPLIES:
The course will require the widely-used programming language Rf or statistical computing andgraphics. This will be required for both lab tutorials and for homework assignments. Students are expected to download and install R or RStudio onto their computer from this website:
The course is based on several sources, which will be assigned throughout the semester and added to the SFU Canvas link as the course progresses.
Learning Statistics Using R by Randall E. Schumacker (2014).
REQUIRED READING NOTES:
Your personalized Course Material list, including digital and physical textbooks, are available through the SFU Bookstore website by simply entering your Computing ID at: shop.sfu.ca/course-materials/my-personalized-course-materials.
ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS
SFU’s Academic Integrity website http://www.sfu.ca/students/academicintegrity.html is filled with information on what is meant by academic dishonesty, where you can find resources to help with your studies and the consequences of cheating. Check out the site for more information and videos that help explain the issues in plain English.
Each student is responsible for his or her conduct as it affects the university community. Academic dishonesty, in whatever form, is ultimately destructive of the values of the university. Furthermore, it is unfair and discouraging to the majority of students who pursue their studies honestly. Scholarly integrity is required of all members of the university. http://www.sfu.ca/policies/gazette/student/s10-01.html