Module #1 Introduction to Conservation Planning Overview of conservation planning, importance of predictive modeling, and course objectives
Module #2 Introduction to Predictive Modeling Basics of predictive modeling, types of models, and their applications in conservation
Module #3 Data Requirements for Predictive Modeling Data types, data sources, and data quality considerations for predictive modeling in conservation
Module #4 Introduction to R and RStudio Overview of R and RStudio, installing and setting up R, and basic R syntax
Module #5 Data Preparation in R Importing, cleaning, and preprocessing data in R for predictive modeling
Module #6 Simple and Multiple Linear Regression Theory and application of simple and multiple linear regression in R
Module #7 Logistic Regression and Generalized Linear Models Theory and application of logistic regression and generalized linear models in R
Module #8 Decision Trees and Random Forests Theory and application of decision trees and random forests in R
Module #9 Support Vector Machines and K-Nearest Neighbors Theory and application of support vector machines and k-nearest neighbors in R
Module #10 Model Evaluation and Selection Metrics for evaluating and selecting regression and classification models in R
Module #11 Introduction to Spatial Modeling Basics of spatial modeling, spatial autocorrelation, and spatial regression in R
Module #12 Generalized Additive Models for Location, Scale, and Shape (GAMLSS) Theory and application of GAMLSS in R
Module #13 Machine Learning for Conservation Overview of machine learning algorithms and their applications in conservation
Module #14 Gradient Boosting Machines and XGBoost Theory and application of gradient boosting machines and XGBoost in R
Module #15 Deep Learning for Conservation Introduction to deep learning, convolutional neural networks, and recurrent neural networks in R
Module #16 Handling Missing Values and Imputation Methods for handling missing values and imputation in R
Module #17 Model Interpretation and Visualization Techniques for interpreting and visualizing predictive models in R
Module #18 Model Ensembling and Stacking Theory and application of model ensembling and stacking in R
Module #19 Sensitivity Analysis and Uncertainty Quantification Methods for sensitivity analysis and uncertainty quantification in predictive modeling
Module #20 Case Studies in Conservation Planning Real-world examples of predictive modeling applications in conservation planning
Module #21 Implementation of Predictive Models in Conservation Planning Practical considerations for implementing predictive models in conservation planning
Module #22 Communication of Predictive Modeling Results Effective communication of predictive modeling results to stakeholders and decision-makers
Module #23 Uncertainty Communication in Conservation Planning Strategies for communicating uncertainty in conservation planning
Module #24 Collaboration and Stakeholder Engagement Importance of collaboration and stakeholder engagement in conservation planning
Module #25 Future Directions in Predictive Modeling for Conservation Planning Emerging trends and future directions in predictive modeling for conservation planning
Module #26 Final Project Overview Guidelines and expectations for the final project
Module #27 Final Project:Week 1-2 Work on final project:data preparation and model selection
Module #28 Final Project:Week 3-4 Work on final project:model implementation and evaluation
Module #29 Final Project:Week 5-6 Work on final project:model interpretation and visualization
Module #30 Course Wrap-Up & Conclusion Planning next steps in Predictive Modeling for Conservation Planning career
