Simon Fraser University
Engaging the World

Course Outlines

Fall 2026 - EVSC 445 D100

Environmental Data Analysis (4)

Class Number: 5227

Delivery Method: In Person

Overview

  • Course Times + Location:

    Sep 9 – Dec 7, 2026: Mon, 12:30–2:20 p.m.
    Burnaby

  • Prerequisites:

    GEOG 251, or one of STAT 100, 201, 203, 205 or 270 or permission of the instructor.

Description

CALENDAR DESCRIPTION:

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.

COURSE DETAILS:

The content of EVSC 445 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. 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.

Students will be introduced to the principles of statistics and the course aims to:

1. Motivate an intrinsic interest in statistical thinking and understand its importance in scientific research.

2. Learn to formulate statistical hypotheses, understand assumptions and build confidence in data analysis and interpretation.

3. Provide experience and support in learning the statistical programming language R.

COURSE-LEVEL EDUCATIONAL GOALS:

  • Demonstrate the ability to apply fundamental concepts in exploratory data analysis.
  • Design studies or experiments for obtaining data while avoiding common design flaws that incur bias, inefficiency and confounding.
  • Demonstrate an understanding of probability and random variables.
  • Understand the concept of the sampling distribution of a statistic.
  • Understand the foundations for confidence intervals and hypothesis testing.
  • Interpret and analyse data using parametric methods and non-parametric methods.
  • Apply and interpret simple and multiple linear regression models.
  • Exposure to some special topics including, for example, mixed effect models
  • Demonstrate an understanding of likelihood and generalized linear models.

Grading

  • Homework 30%
  • Mini-Project 10%
  • Mid-Term 1 20%
  • Mid-Term 2 20%
  • Mid-Term 3 20%

NOTES:

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. Some labs will occur on campus outside of the classroom, and it is recommended for students to bring warm clothes and shoes for walking to these labs. Students will be notified and these labs will be weather-dependent.

The mini-project is a student-led project, providing an opportunity to analyse data that holds some specific interest to the student, or (if requested) the course instructor can supply a dataset to the student.

REQUIREMENTS:

The course will require the widely-used programming language R for statistical computing and graphics. 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:

https://www.r-project.org/
https://rstudio.com/products/rstudio/

Students are expected to participate in lectures and labs and follow along on a laptop computer on which the software program R and R-Studio can be installed. Computers are available on loan from the SFU Bennett library.

Materials

REQUIRED READING:

There is no textbook for this course, instead it is based on several sources which will be assigned throughout the semester and added to the ENV 445/645 Canvas link as the course progresses

Registrar Notes:

ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS

At SFU, you are expected to act honestly and responsibly in all your academic work. Cheating, plagiarism, or any other form of academic dishonesty harms your own learning, undermines the efforts of your classmates who pursue their studies honestly, and goes against the core values of the university.

To learn more about the academic disciplinary process and relevant academic supports, visit: 


RELIGIOUS ACCOMMODATION

Students with a faith background who may need accommodations during the term are encouraged to assess their needs as soon as possible and review the Multifaith religious accommodations website. The page outlines ways they begin working toward an accommodation and ensure solutions can be reached in a timely fashion.