Predictive Modeling for Conservation Planning
( 30 Modules )

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

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Predictive Modeling for Conservation Planning ( 30 Modules )

Predictive Modeling for Conservation Planning
( 30 Modules )