Training Data Scientists to Solve Environmental Challenges

Technical expertise in data analytics is increasingly important in the data-driven environmental field. The Environmental Analytics and Modeling (EAM) concentration offers a specialized, data-focused pathway that prepares students for high-demand careers in environmental data science, geospatial analysis, energy systems modeling and climate risk assessment. 

The program emphasizes quantitative and computational skills, including statistical modeling, machine learning and causal analysis. Using geographic information systems, spatial modeling and satellite data, students gain expertise in geospatial and remote sensing analysis to address land, water and climate challenges. Coursework in energy and climate systems modeling equips students to analyze energy markets, decarbonization strategies and climate risk. 

Concentration Courses

CORE COURSES – REQUIRED

The following courses are required:

  • ENVIRON 700L - Environmental Data Exploration (1.5 credits, fall year one)
  • ENVIRON 710L - Applied Statistical Modeling for Environmental Management (3 credits, spring year one)
  • ENVIRON 712L - Environmental Data Exploration II: Applied Analysis and Visualization (1.5 credits, fall year one)

Elective Course Suggestions

ÌÇÐÄ´«Ã½s may consider the following courses to round out their degree:

  • ECS 568S—Integrated Assessment Modeling (3 credits, spring) 

  • ENVIRON 514—Machine Learning in Environmental Science 

  • ENVIRON 558L—Satellite Remote Sensing for Environmental Analysis (3 credits, spring) 

  • ENVIRON 559—Fundamentals of Geospatial Analysis (4 credits, fall and spring) 

  • ENVIRON 623—Biodiversity on a Changing Planet (3 credits, spring) 

  • ENVIRON 658/A—Applied Qualitative Research Methods (3 credits, fall) 

  • ENVIRON 665—Bayesian Inference in Environment Models 

  • ENVIRON 716L—Modeling for Energy Systems (3 credits, fall) 

  • ENVIRON 717—Markets for Electric Power (3 credits, spring) 

  • ENVIRON 761—Geospatial Analysis for Land and Water Management (3 credits, spring)  

  • ENVIRON 765—Geospatial Analysis for Coastal and Marine Management (3 credits, spring) 

  • ENVIRON 771L—Geospatial Field Skills Part I-III  (1 credit for each section, spring) 

  • ENVIRON 797—Time Series Analysis for Energy and Environment Applications (3 credits, spring) 

  • ENVIRON 832—Environmental Decision Analysis (3 credits, spring) 

  • ENVIRON 850—Quantitative Causal Inference in Environmental Policy (3 credits, ad hoc) 

  • ENVIRON 859/A—Geospatial Data Analytics (3 credits, fall) 

  • ENVIRON 876A—Data Time Series Analysis in Marine Sciences,  (ÌÇÐÄ´«Ã½ Marine Lab (4 credits, spring)) 

EAM Primary concentration pathway

The course-based pathway will be available in a limited capacity to students matriculating in fall 2025 and will be fully available and the default pathway for students matriculating in fall 2026 and beyond. Proposed courses and requirements are subject to change. Please contact your primary concentration faculty chair with any questions.

ÌÇÐÄ´«Ã½s whose primary concentration is EAM will choose one of two pathways. Whether through advanced coursework in the course-based pathway or the Master’s Project (MP) pathway, students will gain tangible deliverables, professional growth and preparation for their next steps after graduation. Most students will follow the course-based pathway. 

The EAM course-based pathway offers options from approved courses and requires a minimum of six additional graded credit hours. ÌÇÐÄ´«Ã½s whose primary concentration is EAM may fulfill the 6-credit requirement by:

  • Completing an additional two, three-credit courses from the menu of EAM electives beyond the courses already used to meet EAM and environment concentration core requirements
  • Completing one, year-long, six-credit project-based experience with sufficient EAM exposure (Bass Connections, Design Climate, etc.).
  • Completing a one-semester project-based experience with sufficient EAM exposure AND completing one additional EAM elective

 

Expectations

Coming in: In addition to the school-wide prerequisites in calculus and statistics, which are required for all concentrations, to be successful in the EAM concentration, students should have an interest in data analysis, environmental problem-solving and computational tools. Although prior experience with coding is helpful, it is not required. Our courses are designed to support skill-building. 

During the program: ÌÇÐÄ´«Ã½s will gain hands-on experience working with real-world environmental datasets, applying analytical techniques and developing computational skills. Through coursework and applied projects, students will learn to process, model and communicate data-driven insights for effective environmental decision-making.

 

Jonathan Gilman

What I’ve loved most about EAM is how it bridges the gap between environmental science and real-world decision-making. It’s given me a skill set I’ll actually use — helping me answer tough environmental questions with code, maps, and models, and apply those insights to real problems."

–jonathan gilman, MEM'26

 

 

Transferable Skills

In this concentration, students will develop skills related to:

  • Data processing and cleaning — preparing raw environmental data for analysis
  • Data visualization — communicating insights through visual storytelling
  • Workflow development and automation — streamlining data analysis processes
  • Statistical and machine learning methods — applying predictive and inferential analytics
  • Geospatial analysis and remote sensing — using geographic information systems (GIS), spatial modeling and satellite data
  • Environmental and energy systems modeling — simulating and optimizing environmental processes and energy systems
  • Decision science and risk assessment — supporting data-driven environmental decision-making
  • Programming and computational tools — proficiency in Python, R, GitHub, ArcGIS, and open-source analytical platforms
  • Communication and technical reporting — translating complex data into actionable insights for diverse audiences

 

Knowledge Gained

ÌÇÐÄ´«Ã½s will gain: 

  • Experience working with real environmental datasets from public and private sectors
  • The ability to process, visualize and communicate complex data to drive decision-making
  • A strong foundation to adapt to evolving analytical tools and methodologies in a rapidly changing field
     

Enrich Your Experience

ÌÇÐÄ´«Ã½s in this concentration will find a range of opportunities to expand their academic experience and get connected to projects and people that align with their interests. We recommend exploring these programs to get started: