Education & Training

Add valuable tools to your quantitative methods and decision analysis toolbox

The Quantitative Fisheries Center at Michigan State University offers online courses aimed at natural resource professionals to improve their skills in quantitative methods and decision analysis. These courses are designed to extend their prior training and provide them with skills to better address the challenges and complexities of resource management. We also offer QFC certification in R programming for students who complete a suite of our courses.

Short courses:

All short courses are three days long (Tuesday through Thursday), taught at locations around the Great Lakes, and are in-person only. All short courses can be purchased here.

Email the first course instructor for questions about the course content. For all other questions, email Charlie at belinsky@msu.edu.

Maximum Likelihood Estimation Using RTMB

The course reviews concepts underpinning maximum likelihood estimation and covers applications using Template Model Builder (TMB) via the RTMB package, a software tool increasingly used in fisheries and ecology for fitting of highly parameterized nonlinear non-normal statistical models, including state-space models.
Dates: December 17 to December 19, 2024.
Location: East Lansing, Michigan (Michigan State University)
Instructors: Jim Bence (bence@msu.edu) and Chris Cahill

Fish Mortality Estimation

Dates: April 1 to 3, 2025
Location: Sault Ste. Marie, Michigan
Instructor: Travis Brenden (brenden@msu.edu)

Fish Recruitment and Reference Points

Estimating stock-recruitment relationships and deriving commonly used fisheries management reference points like FMSY and MSY using age-structured simulation approaches.
Dates: June 17-19, 2025 (date has been changed from original email)
Location: Duluth, Minnesota
Instructors: Chris Cahill (cahill11@msu.edu) and Travis Brenden

Fish Age and Growth Modeling

Dates: September 2 to 4, 2025
Location: London, Ontario
Instructor: Travis Brenden (brenden@msu.edu)

Model Uncertainty and Diagnostics

How to characterize uncertainty in maximum likelihood models and diagnose model mis-specification.
Dates: December 9 to 11, 2025
Location: East Lansing, Michigan (Michigan State University)
Instructor: Chris Cahill (cahill11@msu.edu) and Travis Brenden

Asynchronous courses:

Asynchronous courses are always open for registration and can be started anytime.

Introduction to Simulation for Decision Analysis

This course introduces the types of computer simulation models that are used to inform decisions about management of fish and wildlife populations. The course goal is to provide you with the knowledge you need to begin building your own models of systems that matter to you, and to use those models to inform the process of making a good decision.

Programming Fundamentals Using R

This course introduces students to the principles of programming using the R and RStudio software packages. The course will teach students basic programming and data structures, as well as good programming practices--skills that are transferable to other languages like C++ and Python.

Advanced R: Graphing with GGPlot

This course introduces students to the graphing packing in R called GGPlot2. GGPlot2 is a powerful data visualization tool used to make publication-quality plots. We will cover how to create common graphs such as bar graphs, scatter plots, and histograms with the goal of creating reusable plots that are easy to modify.

Resampling Approaches to Data Analysis

Resampling methods construct sampling distributions for statistics of interest by resampling observed data. In this course, students are taught common resampling approaches including bootstrapping and randomization/permutation testing, with heavy emphasis on different bootstrap data generating methods and different bootstrap confidence intervals.

Past courses:

Applied Bayesian Modeling for Natural Resource Management

This course introduces students to Bayesian modeling applications in Natural Resources. Students will learn how to elicit, fit, check and compare models under the Bayesian Paradigm in the context of common problems in natural resources using the Stan programming language. There is currently no scheduled offering of this course.

Introduction to Structured Decision Making and Adaptive Management

We explore the role of uncertainty in decision - making about renewable natural resources. Students are introduced to Structured Decision Making (SDM) and Adaptive Management (a special case of SDM), and to quantitative methods associated with them. You will learn about the importance of models and of effective stakeholder engagement. There is currently no scheduled offering of this course.