Earners of this stacked credential have completed all four modules of the CGC-UMCES AI & Data Science for Environmental Scientists workshop series. Across approximately 13 hours of hands-on instruction, participants developed a broad and integrated foundation in modern computational methods applied to real environmental science problems. Working with Chesapeake Bay water quality data, NASA satellite imagery, U.S. Census socioeconomic data, oyster shell photographs, and IFCB phytoplankton images, participants used Python, R, and large language model (LLM) coding assistants to build geospatial analysis pipelines, interactive data dashboards, machine learning models, and image processing workflows. This credential recognizes demonstrated competency in AI-assisted coding, data science, and environmental data analysis across four interconnected domains.