The objective of this cource is to provide students with probabilistic and statistical methods to analyze environmental data. This class emphasizes both theoretical and applied aspects of data analysis methods. Weekly lab exercises are from environmental applications. The course will cover topics from survey design, regression models, analysis of variance, non-parametric methods, general and generalized linear models, drawing on a range of practical examples. It will also provide an introduction to statistical computing in R.
This course will provide students with a hands-on overview of the major data analysis techniques, contemporary problems in and common approaches to environmental sciences. It will provide students with hands-on experience using contemporary software to conduct environmental data analysis, challenge students to use best practices in data management, emphasize the importance of reproducibility in research, and foster interpersonal communication and collaboration.
Upon successful completion of this course, students will be able to:
1. Identify problems in environmental science requiring data collection, analysis, and dissemination
2. Successfully collect and analyze, and disseminate results
3. Communicate the theory, basis, methods, results, and context of applied environmental data analysis
4. Will be proficient in R and R Studio
- Преподаватель: Basil Yakimov