AI & Data Science for Environmental Scientists — Complete Series

AI & Data Science for Environmental Scientists — Complete Series

AI & Data Science for Environmental Scientists — Complete Series

Default Expiration: Does not expire

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.

About this Credential

Learning Objectives:

  1. Use large language models (LLMs) as coding assistants to generate, debug, and iteratively refine Python and R code across a complete environmental data science workflow, applying prompt engineering strategies including context-setting, role framing, and iterative refinement.
  2. Build and execute a geospatial data science pipeline that integrates socioeconomic (U.S. Census ACS) and remote sensing (NASA HLS NDVI) datasets to compute a Social Vulnerability Index and Green Space Inequity Index at the census tract level, and communicate findings through choropleth maps, scatter plots, and KMeans cluster analysis.
  3. Design and deploy interactive R Shiny data dashboards connected to real environmental monitoring data, applying Shiny’s reactive programming model to link map interactions, time series plots, and summary tables, and use LLM assistance to extend or adapt dashboards for new datasets.
  4. Execute a complete supervised machine learning workflow — data cleaning, feature engineering, model training, performance evaluation, and model interpretation — using both ensemble (Random Forest) and neural network (MLP) methods to predict an environmental variable (bottom dissolved oxygen) from observational monitoring data, and apply SHAP to interpret feature contributions.
  5. Apply image processing techniques spanning classical computer vision (pixel manipulation, threshold-based segmentation, contour detection, ellipse fitting) and modern deep learning (YOLO object detection and instance segmentation, CNN classification) to environmental science images, and evaluate how data quantity, class balance, and augmentation affect model performance.
How to add this type of credential to LinkedIn:
  • Copy the specific credential link for your earned record from your My Credentials page.
  • In LinkedIn, choose Add profile section > Licenses & Certifications.
  • Use this badge title as the certification name and “University of Maryland Center for Environmental Science (UMCES)” as the issuing organization.
  • Paste your unique credential link into “Credential URL”.