This course addresses basic approaches to experimental design, statistical analysis, and presentation of quantitative information. The broad topic areas of this course are the use of frequency distributions in statistics, understanding the properties of the normal model and other frequently encountered distributions, quantitative comparisons of the means, regression and correlation analysis, and basic statistical decision making. This course will provide graduate level understanding of statistical approaches and how to apply them in scientific endeavor.
This course is designed to delve into both basic and advanced quantitative approaches central to fishery assessment and fishery science. It aims to provide a comprehensive overview of the approach and process of fisheries management, while also introducing quantitative methods of fisheries assessment. An integral part of the course is to expose students to the current primary literature related to fisheries management. Additionally, the course is structured to promote an interest and understanding of scientific research in the field of fisheries management, with a strong emphasis on developing critical thinking skills.
This course delves into the essential principles and techniques of programming in R, a statistical programming language crucial for data analysis, manipulation, visualization, and reproducible research. Recognizing R’s significant utility in various sectors, including academia, industry, and government scientific groups, the course is tailored to meet the growing demand for proficient data analysis practitioners. It begins by introducing the basics of R programming, emphasizing typical programming concepts like code modularization, function writing and usage, and code reusability. Participants will gain practical knowledge of R, R Studio, and relevant packages, along with software engineering concepts such as project build and code testing. The course also covers the challenges of organizing a typical data analysis project, which often involves managing numerous data files and binary scripts. To address these challenges, students will learn to use a comprehensive suite of analytical tools. Additionally, the course focuses on operations on vectors, wrangling, analyzing, and visualizing data using base R and specialized packages like tidyverse and ggplot2. Lastly, the course promotes a workflow conducive to reproducible research, teaching how to create markdown documents that integrate text and code for effective data presentation. This comprehensive approach ensures that participants are well-equipped to handle real-world data analysis challenges.
Course will include lectures, activities and workshops designed to improve scientific writing, grantsmanship, oral/poster presentation skills, and other aspects associated with professional development and scientific communication.
This course addresses basic approaches to initializing, executing and analyzing individual-based and agent-based models. This course is intended to provide students with an understanding of common modeling approaches and how to apply them correctly in the ecological and fishery sciences. The course uses the text: Agent-Based and Individual-Based Modeling: A Practical Introduction. 2011. Princeton University Press. 352 pages.