ADD VALUABLE TOOLS TO YOUR QUANTITATIVE METHODS AND DECISION ANALYSIS TOOL BOX!

The Quantitative Fisheries Center at Michigan State University offers online classes aimed at natural resource professionals to improve their skills in quantitative methods and decision analysis. These classes 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.

Current classes:

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. This class is asynchronous and is now open to enrollment.

Introduction to maximum likelihood using TMB

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. This tool is particularly powerful for fitting models with random effects (such as state-space models).  Next offering: December 2023.

Introduction to maximum likelihood using ADMB

The course reviews concepts underpinning maximum likelihood estimation and covers applications using AD Model Builder (ADMB), a software tool that has historically been used widely in fishery stock assessment models. This course is intended for students who know they need to use ADMB because of legacy code or agency use.  This class is asynchronous and is now open to enrollment.

Applied Bayesian Modeling for Natural Resource Management

This class 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. Next offering: Fall, 2023.

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.

Programming Fundamentals Using R

This course introduces students to the principles of programming using the R and RStudio software packages. The class 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.  This class is asynchronous and open to enrollment anytime.

Advanced R: Graphing with GGPlot

This class 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.  This class is asynchronous and open to enrollment anytime.

Resampling Approaches to Data Analysis

Resampling methods construct sampling distributions for statistics of interest by resampling observed data.  In this class, 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.  This class is asynchronous and open to enrollment anytime.

Inference Paradigms

This course provides a conceptual overview of different statistical inference approaches used in study of natural resource (and other) systems.  Topics include basic probability, the frequentist and Bayesian approaches, and parametric and non-parametric (including resampling) methods.   This class is asynchronous and open to enrollment.

Certification:

The QFC provides certificates recognizing areas of specialization obtained through taking our classes

R Programming Certificate

For students who complete:
1) Programming Fundamental Using R
2) Advanced R: Graphing with GGPlot
3) One of Resampling Approaches to Data Analysis or
     Introduction to maximum likelihood using TMB

Statistical Inference Certificate

For students who complete:
1) Introduction to maximum likelihood using TMB
2) Introduction to Bayesian modeling in Biology
3) Resampling Approaches to Data Analysis

For more information:

Contact Charles Belinsky at belinsky@msu.edu or 517-355-0126

   

 

 

5

 

 

Fish-Bar

 

 

Nature-bar