Advanced Statistical Methods for Environmental Research
( 30 Modules )
Module #1 Introduction to Advanced Statistical Methods Overview of the course, importance of statistical methods in environmental research, and review of basic statistical concepts
Module #2 Regression Analysis for Environmental Data Review of simple and multiple linear regression, assumptions, and applications to environmental data
Module #3 Generalized Linear Models (GLMs) for Environmental Data Introduction to GLMs, logistic regression, Poisson regression, and their applications to environmental data
Module #4 Time Series Analysis for Environmental Data Introduction to time series analysis, autoregressive integrated moving average (ARIMA) models, and seasonal decomposition
Module #5 Spatial Analysis for Environmental Data Introduction to spatial analysis, spatial autocorrelation, and spatial regression
Module #6 Machine Learning for Environmental Data Introduction to machine learning, supervised and unsupervised learning, and applications to environmental data
Module #7 Random Forest for Environmental Data Introduction to random forest, advantages, and applications to environmental data
Module #8 Bootstrap Resampling for Environmental Data Introduction to bootstrap resampling, confidence intervals, and hypothesis testing
Module #9 Survival Analysis for Environmental Data Introduction to survival analysis, Kaplan-Meier estimates, and Cox proportional hazards model
Module #10 Bayesian Statistics for Environmental Data Introduction to Bayesian statistics, Bayesian inference, and Markov chain Monte Carlo (MCMC) simulation
Module #11 Spatial Regression Models for Environmental Data Introduction to spatial regression models, spatial autoregressive models, and spatial error models
Module #12 Panel Data Analysis for Environmental Data Introduction to panel data analysis, fixed effects, and random effects models
Module #13 Meta-Analysis for Environmental Research Introduction to meta-analysis, fixed effects, and random effects models
Module #14 Dealing with Missing Data in Environmental Research Introduction to missing data, types of missing data, and methods for dealing with missing data
Module #15 Big Data Analytics for Environmental Research Introduction to big data, Hadoop, and Spark, and applications to environmental research
Module #16 Data Visualization for Environmental Research Introduction to data visualization, principles, and best practices for environmental research
Module #17 Case Studies in Advanced Statistical Methods for Environmental Research Real-world examples and case studies of advanced statistical methods in environmental research
Module #18 Advanced Topics in Statistical Computing for Environmental Research Advanced topics in statistical computing, including parallel computing, and high-performance computing
Module #19 Statistical Software for Environmental Research Overview of popular statistical software, including R, Python, and MATLAB
Module #20 Reproducibility and Replicability in Environmental Research Importance of reproducibility and replicability, and methods for achieving them in environmental research
Module #21 Collaborative Research and Interdisciplinary Approaches Importance of collaborative research, and interdisciplinary approaches in environmental research
Module #22 Ethics in Environmental Research Importance of ethics, and responsible research practices in environmental research
Module #23 Communicating Statistical Results in Environmental Research Effective communication of statistical results, and presentation techniques
Module #24 Statistical Consulting for Environmental Research Role of statistical consulting, and best practices for effective collaboration
Module #25 Grant Writing and Funding Opportunities Tips and strategies for writing successful grants, and funding opportunities for environmental research
Module #26 Publishing Research in Environmental Journals Tips and strategies for publishing research, and navigating the peer-review process
Module #27 Career Development for Environmental Researchers Career paths, professional development, and networking opportunities for environmental researchers
Module #28 Staying Current with Advanced Statistical Methods Staying current with new developments, and continuing education opportunities in advanced statistical methods
Module #29 Course Wrap-Up and Final Project Course wrap-up, final project, and course evaluation
Module #30 Course Wrap-Up & Conclusion Planning next steps in Advanced Statistical Methods for Environmental Research career
