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:

Software Tools for Maximum Likelihood Estimation

The lead instructor will be Jim Bence, the QFC co-director. The full course will review maximum likelihood concepts and cover both AD Model Builder (ADMB) and Template Model Builder (TMB), two modern software tools fitting of nonlinear and non-normal statistical models by maximum likelihood. TMB is particularly useful for models with random effects, including state space models. These software packages are widely used in fishery stock assessments. The ADMB and TMB portions of the course can be taken separately. This course will be offered over an eight week period with two 2 hour scheduled video meetings per week (days/times TBD), and students will be expected to cover some material outside of course meetings. Next offering: October 2021.

Introduction to Bayesian modeling in Biology

The lead instructor will be Juan Pedro Steibel, a Michigan State University faculty member with extensive experience in developing Bayesian methods and applications and teaching applied Bayesian statistics.  This class introduces students to Bayesian modeling applications in biology. Students will learn how to elicit, fit, check and compare models under the Bayesian Paradigm, in the context of common problems in ecology, biology and genetics.  The Bayesian Paradigm allows for a conceptual integration of prior information and new data.  In addition, it allows for the outputting of probability about states of nature, updated based on observed data, which are often highly useful in decision making. Next offering starts September 28, 2021.

Introduction to Structured Decision Making and Adaptive Management

In this non-credit class we will consider the role of uncertainty in decision -­ making about renewable natural resources. Students will be 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 to inform good decisions. Click here for an introductory video to the course.  Next offering: Fall, 2021.

Programming Fundamentals Using R

This is a non-credit, online, and asynchronous course.  The purpose of this class is to introduce students to the principles of programming using the R and RStudio software packages. R and RStudio are powerful and versatile data analysis packages that are freely available. While the class focus is on programming in R and RStudio, the programming skills taught are designed so that students can transfer their skills to other programming platforms like ADMB or C.  This class is open to enrollment and students can start anytime.

Advanced R: Graphing with GGPlot

This is a non-credit, online, and asynchronous course.  The purpose of this class is to introduce students to the graphing packing in R called GGPlot2.  GGPlot2 is a powerful data visualization tools used to make publication-quality plots and it has one of the largest, and most active, R communities.  The goal of GGPlot2 is to modularize plot data, dividing a plot into its components parts. GGPlots also has a large and active community of R users.  This class uses RStudio and is designed for students who have at least a basic background in programming -- the equivalent of one semester of R, or any similar programming language (e.g., JavaScript, C...).  This class is open to enrollment and students can start anytime.

Resampling Approaches to Data Analysis

Resampling methods are approaches to conducting inference (e.g., standard error estimation, confidence interval construction, hypothesis testing) that rely on the power and speed of computers to construct sampling distributions for statistics of interest and that can be used in wider situations than traditional normal-based approaches.  In this class, you will be exposed to common resampling approaches including jackknifing, bootstrapping, and randomization/permutation testing. Particular attention is paid to bootstrapping with coverage including multiple approaches for constructing bootstrap confidence intervals and bootstrap data generating methods.   This class is open to enrollment and students can start anytime

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
     Software Tools for Maximum Likelihood Estimation

Statistical Inference Certificate

For students who complete:
1) Software Tools for Maximum Likelihood Estimation
2) Introduction to Bayesian modeling in Biology
3) Resampling Approaches to Data Analysis

For more information:

Contact Charles Belinsky at belinsky@msu.edu or 989-272-2623

   

 

 

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