This course provides a comprehensive overview of standard and innovative methods in environmental data analysis and modeling, with an emphasis on frequentist and Bayesian statistical approaches. The curriculum is designed to equip students with practical skills using open-source programs in R. Key topics include: probability theory, Bayes' theorem, common statistical distributions, stochastic simulation, likelihood-based parameter estimation, model comparison, and model diagnostics. We will also introduce linear models, generalized linear models, and mixed-effects models, exploring both frequentist and Bayesian inference for these complex structures.

The course emphasizes active learning through frequent hands-on exercises, which give students the opportunity to practice new analytical and modeling techniques using real or simulated datasets. Another important component of this course is the individual project, where students will apply learned methods to their own data or simulated data relevant to their scientific interests. This project culminates in the writing of a complete report. The analytical process will cover initial exploratory data analysis, selection and implementation of statistical and modeling approaches, and effective presentation and discussion of results.