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Guided learning journeys that build knowledge step by step.
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This advanced learning path equips students in environmental modeling with the knowledge and skills to couple diverse models, focusing on coupling frameworks, data transfer, and integration software. It covers essential programming concepts, model coupling theory, and practical implementation, culminating in the ability to design and implement coupled environmental models.
This learning path equips students in computer science and environmental science with the skills to apply machine learning techniques to environmental modeling. It covers essential programming, statistics, ML algorithms, and evaluation methods, culminating in practical applications to environmental data.
This learning path equips students with the skills to apply advanced statistical models—GLMs, GAMs, hierarchical models, and Bayesian approaches—to environmental data. Starting from core statistical and programming foundations, it progresses through model structures, diagnostics, and practical implementation, culminating in a capstone project.
This learning path equips environmental science students with the knowledge and skills to apply agent-based modeling (ABM) to environmental challenges. Starting with programming and modeling basics, it covers core ABM concepts, key environmental applications, and practical implementation using NetLogo. By the end, learners will be able to design, implement, and analyze simple agent-based models to explore environmental phenomena.
This learning path guides ecology students from foundational concepts in ecology and programming to the construction and analysis of population, community, and ecosystem models. It emphasizes dynamic modeling approaches and practical application using R.
This learning path introduces the principles of climate models, covering the physics and modeling basics, model components, scenarios, and ensemble methods. It progresses from fundamental climate science and numerical methods to specific model types and applications, suitable for university students in climate science.
This learning path guides hydrology students from foundational concepts in hydrology and programming to the practical application of hydrological models. It covers rainfall-runoff processes, groundwater interactions, watershed modeling, and hands-on use of HEC-HMS and SWAT.
This learning path guides atmospheric science students from the fundamentals of atmospheric composition and meteorology through Gaussian plume dispersion modeling to advanced photochemical models. It covers essential mathematical tools, model evaluation techniques, and practical applications for air quality management.
This path introduces high school students to water quality modeling, covering fundamental concepts, model types, and applications to nutrient loading, dissolved oxygen, and eutrophication. Learners will understand how models are used in water management and gain practical skills for interpreting model outputs.
This learning path introduces high school students to using GIS for spatial environmental modeling. Starting from basic GIS concepts and programming, it progresses through spatial data handling, map algebra, interpolation, and raster modeling, culminating in a practical capstone project.