Classes to Take

This document lists classes that are worth taking (and, potentially, ones worth avoiding). An important caveat is that classes might change and evolve without this document changing and evolving to match them; take all recommendations with a few grains of salt.

FOR (Forestry)

FOR 465 - Natural Resources Policy
- Instructor: Dr. Malmsheimer
- Notes: To take this class as a grad student you should email Dr. Malmsheimer, and he will help you register the course as a 600 level independent study. A great class that focuses on major historical policies that control/manage/impact our United States natural resources, and how those policies have shaped our current landscape. The course is specifically tied to major US departments of resources management (Bureau of Land Management, Fish and Wildlife Services, National Park Service, Department of Agriculture etc), and even discusses what it might be like to work in these agencies.
FOR 522 - Forest Mensuration
- Instructor: Dr. Bevilacqua
- Notes: A mix of field methods with applied statistics and sampling theory to make estimate of forest attributes. Quite a bit of work with weekly field trips/labs. Excellent place to start if you would like to gain familiarity with forest inventory techniques.
FOR 521 - Forest Ecology & Silviculture
- Instructor: Dr. Nowak
- Notes: An intense course on silviculture in northern hardwoods systems. Quite a bit of work with weekly field trips/labs. The class is based primarily on a set of 10ish papers that describe or reflect on the life history, treatments, and outcomes observed in the field that week. Weekly quizzes as well.
FOR 532 - Forest Ecology
- Instructor: Dr. Beier
- Notes: Strongly recommend taking this class, and not just because it’s taught by Colin and TA’ed by one of your very cool lab mates. Basics of forest ecosystem structure, function and dynamics at multiple scales. Class concepts are highly relevant to the work that happens in this lab. This class does have field labs so it can be a bit of a time commitment but the out of class work is minimal.
FOR 557 - Fundamentals of GIS
- Instructor: Dr. Bevilacqua
- Notes: Great class on introduction to spatial data structures and how to use a GIS. Highly recommend if you’re new to spatial data, Colin will likely make you take this.
FOR 570 - Forest Management Decision Making and Planning
- Instructor: Dr. Wagner
- Notes: If you are interested in the economic sides of forest management, or want to learn more about the driving forces in private forest management this is a great class. Will likely go down easier with some familiarity of general economics.
FOR 572 - Sustainable Harvesting Practices
- Instructor: Dr. Germain
- Notes: Fundamentals of forest operations. Good if you are trying to get into the nitty gritty of logging and forestry practices. Field labs and some out of class work.
FOR 659 - Advanced GIS
- Instructor: Dr. Badruddin
- Notes: If you’ve taken FOR 557 (or any other GIS class) then this won’t be very hard. Might not even be necessary. A bit tedious, but adds some efficiency if you’re going to be working in a GIS.
FOR 796 - Applied Machine Learning for Environmental Science
- Instructor: Mike Mahoney
- Notes: Best class ever. Probably never going to be taught again. All resources can be found here

APM (Applied Mathematics or Statistics)

APM 625 - Sampling Methods
- Instructor: Dr. Stehman
- Notes: Introduction to a whole variety of different statistical sampling methods. Recommend especially if you think you may end up implementing your own field protocol. Dr. Steman’s classes are all excellent and he’s a very patient and helpful instructor.
APM 630 - Regression Analysis
- Instructor: Dr. Zhang
- Notes: An intro course on regression. A tiny bit of theory just to get you going, but mostly applied. Great if you haven’t done much regression or modeling, Colin will likely make you take this class. The course is taught all in SAS (unfortunately), but you are allowed to work in R.
APM 635 - Multivariate Statistical Methods
- Instructor: Dr. Zhang
- Notes: This is another of Dr. Zhang’s many stats classes, it’s the same as the others with just another flavor of stats to focus on. If you need multivariate stats, this is your class.
APM 645 - Nonparametric Statistics and Categorical Data Analysis
- Instructor: Dr. Zhang
- Notes: A very applied focus on, well, nonparametric statistics and categorical data analysis. Definitely not relevant to every student in the lab, but a solid choice if you need to do this type of work. Dr. Zhang writes massive books of lecture notes, and then lectures are more or less just reading the notes page by page; this is both a plus and minus for when it comes to actually taking the class. You can buy the book without taking the class from the ESF printers.
APM 671 - Map Accuracy Assessment
- Instructor: Dr. Stehman
- Notes: A really awesome class, rooted in sampling theory, about how to do rigorous assessments of map information. It’s 1 credit, and is only offerred in the Spring. Highly recommend.
APM 730 - Advanced Regression Modeling Methods
- Instructor: Dr. Zhang
- Notes: A direct follow up from APM 630, just taking things a bit further into more specific approaches.

ERE (Environmental Resource Engineering)

ERE 530 - Numerical and Computing Methods
- Instructor: Steve Shaw
- Notes: Introductory basics of R. You won’t learn anything from this class that you couldn’t learn from R for Data Science, but this is a well structured class if you prefere a little more structure or guidance in learning programming languages. This class is geared towards engineering students, but there are other classes at ESF that also teach R if you are looking for a different perspective.
ERE 565 - Principles of Remote Sensing
- Instructor: Dr. Mountrakis
- Notes: This class covers a wide range of topics related to the basics of remote sensing. It’s a good place to get a foothold for some of the concepts that we deal with around here. The class is quite work heavy, although most of it is quite basic.
ERE 621 - Spatial Analysis
- Instructor: Dr. Mountrakis
- Notes: “Not a matlab course” but kind of a matlab course. Challenging, and a fair bit of work, but is pretty great at getting into the weeds of how many spatial statistics we calculate and analyze are actually cooked up. The other half of this course is focused on interpretation of these statistics, which is also really helpful.

EFB (Environmental Biology)

EFB 650 - Landscape Ecology
- Instructor: Dr. Frair
- Notes: This is not the hardest or most technical course in the world, but provides a solid introduction to the practicalities of landscape ecology research. Other classes go deeper on the theory (including Colin’s Forest Ecology course), but this class does a good job of introducing the standard analytical toolkit for landscape ecology.
EFB 797 - Adaptive Peaks Grad Seminar
- Instructor: Dr. Razavi
- Notes: EFB 797 is a one-semester, one-credit seminar to enhance and compliment the course work and research undertaken by graduate students. This course will introduce graduate students to a range of research programs conducted by visiting speakers and help develop the skills for platform presentations and also critically discussing and evaluating research publications. Students will be required to give a seminar on their research topic (or previously completed research/other), review research papers and lead discussions and also coordinate logistics associated with speaker visits. – From the 2019 syllabus

ESF

ESF 797 - Graduate Seminar on Information Resources
- Instructor: Casey Koons
- Notes: This is a solid 1 credit class to take in your early days at ESF, and is a cool place to openly chat about grad student life with other new students. It really should be titled ‘how to be a grad student at ESF’, as it covers things like:
  - Building your committee
  - Developing relationships with Profs
  - Developing your research questions
  - How to navigate ESF paperwork and other degree deadlines/requirements
  - How to use the library databases and resources

SU Courses

CEE600 M0002 - Environmental Data Science
- Instructor: Dr. Carter
- Notes: Introduction to data science methods for environmental analysis in the R and Python programming languages. Topics covered include reproducible scientific computing (bash scripting for data management, git for version control); open geospatial data sources; common structures of environmental data; several statistical and machine learning methods used in space/time analysis (in R and Python); and high throughput computing. – From the Fall 2020 syllabus