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.
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. Next offering: August 2020.
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, 2020.
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.
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
R Programming Certification:
The QFC offers an R Programming Certificate for students who complete the following classes:
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
For more information:
Contact Charles Belinsky at email@example.com or 989-272-2623